Sequential Attention-based Sampling for Histopathological Analysis
Authors: Tarun G
Naman Malpani
Gugan Thoppe
Devarajan Sridharan
Venue: NeurIPS 2025 poster ()
Keywords: Deep Learning
Deep Reinforcement Learning
Multiple Instance Learning
Histopathological Analysis
Whole Slide Images
Primary area: machine_learning_for_sciences
TL;DR: An attention-augment deep reinforcement learning method for automated histopathology

Abstract: Deep neural networks are increasingly applied in automated histopathology. Yet, whole-slide images (WSIs) are often acquired at gigapixel sizes, rendering them computationally infeasible to analyze entirely at high resolution. Diagnostic labels are largely available only at the slide-level, because expert annotation of images at a finer (patch) level is both laborious and expensive. Moreover, regions with diagnostic information typically occupy only a small fraction of the WSI, making it inefficient to examine the entire slide at full resolution.
Here, we propose SASHA -- Sequential Attention-based Sampling for Histopathological Analysis -- a deep reinforcement learning approach for efficient analysis of histopathological images. 
First, SASHA learns informative features with a lightweight hierarchical, attention-based multiple instance learning (MIL) model. 
Second, SASHA samples intelligently and zooms selectively into a small fraction (10-20\%) of high-resolution patches to achieve reliable diagnoses.
We show that SASHA matches state-of-the-art methods that analyze the WSI fully at high resolution, albeit at a fraction of their computational and memory costs. In addition, it significantly outperforms competing, sparse sampling methods. 
We propose SASHA as an intelligent sampling model for medical imaging challenges that involve automated diagnosis with exceptionally large images containing sparsely informative features. Model implementation is available at: https://github.com/coglabiisc/SASHA.

Sequential Attention-based Sampling for Histopathological Analysis
Tarun
Naman Malpani
Indian Institute of Science Bangalore, India tarungBiisc.ac ac.
Indian Institute of Science, Bangalore, India namanmalpani01 pwiiaaallaiiiiii...amai ac.
Gugan Thoppe
Devarajan Sridharan Indian Institute of Science, Bangalore, India sridhar ac. in
Indian Institute of Science, Bangalore, India gthoppeGiisc. ac.
Abstract
Deep neural networks are increasingly applied automated histopathology. Yet, whole-slide images (WSIs) often acquired at gigapixel sizes, rendering them computationally infeasible to analyze entirely at high resolution. Diagnostic labels are largely available only at the slide-level, because expert annotation of images at a finer (patch) level is both laborious and expensive, Moreover, regions with diagnostic information typically occupy only small fraction of the WSI, mak- ing it inefficient to examine the entire slide at full resolution. Here, we propose SASHA Sequential Attention based Sampling for Histopathological Analysis deep reinforcement learning approach for efficient analysis of histopathological images. First, SASHA learns informative features with lightweight hierarchical, attention-based multiple instance learning (MIL) model. Second, SASHA samples intelligently and zooms selectively into small fraction (10-20%) of high-resolution patches to achieve reliable diagnoses. We show that SASHA matches state-of-the- art methods that analyze the WSI fully at high resolution, albeit at a fraction their computational and memory costs. In addition, it significantly outperforms competing, sparse sampling methods. We propose SASHA as an intelligent sam- pling model for medical imaging challenges that involve automated diagnosis with exceptionally large images containing sparsely informative features
Introduction
Deep learning models have emerged major frontier automated medical imaging diagnosis. For example, such models accurately identify tumorous tissue in whole-slide histopathological images Yet, hardware and memory bottlenecks render it impractical to analyze WSIs acquired at gigapixel sizes. Moreover, analyzing WSI-s at high resolution is inefficient because regions with diagnostic information (e.g., tumor cells) make up only a small fraction of the entire image, and are not clearly identifiable low resolutions. As result, methods that predict slide-level labels from low-resolution WSIs suffer from performance bottlenecks (29 [6 By contrast, methods that incorporate fine-graing grained "patch-level" information perform better 28 37). Yet. these latter methods require significant resources and expert annotations WSIs at the patch level.
(WSI) [4, 10 [2
The Multiple Instance Learning (MIL) framework [L] addresses these limitations elegantly. In MIL, the WSI is divided into non-overlapping patches, with each patch being encoded into high- dimensional feature vector. The feature vectors are aggregated using simple methods, like max- or mean-pooling, and this "bag" of features is slide-level labels. Furthermore, diagnostic regions can be identified with weighted aggregation methods, like Attention-Base deep Multiple Instance Learning (ABMIL) [5), which assigns patch-level attention scores, based on their relevance.
Model implementation is available at:
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Low resolution image
Updsted state, St+1
Se BAX
action
SASHA Agent
FE
TSU
action
S E
B
Episode Complate
Feature
V(ap)
zoomed
Aggregator(FA)
HAFED
Classifier
(Pred. Label)
4; patch selected by RL Agent similar patches
similar patches updated
FE- Pr-rrraadd Feature Extractor
NNw Feature Aggregator output
An attention attention-augmennt augmented deep RL model. Starting with low-resolution WSI features Sn E IR at each time step I, the deep RI. agent selects a patch de (red outline) to zoom in high-resolution, based on policy (a (S+). High-resolution features V V(a)) E TR extracted and aggregated using a Heirarchical Attention-based Feature Distiller (HAFED, Section (32)). The states S, of patches with features similar a, are updated with Targeted State Updater (TSU, Section (.3) At the end of each episode, the classifier (Section 3.2) predicts the presence or type of cancer.
Figure 1: SASHA:
Nonetheless, major limitation of MIL-based approaches is the need to process most WSI patches high resolution. Because tumors typically occupy only a small portion of the slide, feature extraction for the majority of non-tumor patches becomes computationally inefficient, increasing inference time and memory usage. To address this, Zhao et al. [40] proposed a deep reinforcement learning (RL)-based approach- -oogisch-RLogss... that first examines WSIs at low resolution and then selectively zooms into diagnostically relevant righ-uesooltion patches to predict slide-level labels.
Although reportedly much faster (~4x) than conventional MIL approaches at inference time 40), RLogist suffers from major drawbacks: i) Poor accuracy: Because RLogist samples only a fraction of the whole slide, it lags current sota models that sample the whole WSI at high resolution by ~10-15%; ii) Weakly diagnostic features: Image features are extracted with a generic ResNet-50 [3 model pretrained ImageNet (6), thereby rendering these features poorly diagnostic of pathology; iii) Training inefficiency: The RL agent training suffers from bottlenecks, such as convergence issues arising from the RL policy network being trained concurrently with the classification network.
Here, we address these limitations by combining RL with multi- attention MIL in a framework we call SASHA Sequential Attention-base Sampling for Histopathological Analysis (Fig. TT. Our study makes the following key contributions:
We design an attention -uggteetd deep RL agent that scans WSI patches at low resolution, and zooms into selected patches to extract meaningful, high-resolution features.
To improve diagnostic accuracy, (HAFED), which uses multiple attention heads to generate label-informed MIL features.
propose Hierarchical Attention-basn FEature Distiller
For stable convergence of the RL policy, train slide-level label classifier as part of the HAFED model, and freeze the classifier during RL training.
For faster training and inference, we exploit similarities among patch features to perform a concerted state update of correlated instances with targeted state updater (TSU).
We show that SASHA achieves sate-of-the-ant performance two cancer benchmarks 26 comparable to models that analyze the entire WSI at high resolution, while requiring only ~ 6% of their memory for WSI representation and with upto 8x faster inference times.
Related Work
Deep learning approaches for automated histopathology have a rich and detailed history (reviewed in 34 (7]). Here, review specifically those methods that inspire key aspects of our solution and, therefore, form benchmarks for performance comparisons.
Multiple Instance Learning (MIL). MIL is weakly supervised learning paradigm widely applied WSI analysis. Unlike fully supervised learning, where each instance is associated with a MIL involves dividing the slide into "bag" of instances (patches); the model is then trained with
label,

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bag-level labels. MIL is, therefore, suitable for classification problems where slide-level, but not patch-level, labels are available. Early MIL approaches relied on instance selection to identify the most relevant patches within a bag (19). Recent models, such as attention-basee MII. (ABMIL)) [15. assign importance weights to patches, allowing the model to focus on diagnostically relevant regions.
Advances in attention- based MIL. A key limitation of these approaches is that each patch is treated independently of the others, even though many may share strong feature correlations. Recent models exploit correlations among instance representations using a self-attention mechanism (e.g., TransMIL (32). Other approaches cluster instances based on their attention scores [2]. Yet, these attention mechanisms may capture only some types of diagnostic features. leading to overfitting and lack of generalization. To overcome this, Aitention-Challengee
Multiple Instance Learning (ACMIL) [39] employs multiple attention blocks, each focusing on distinctive diagnostic features. Inspired by these approaches, our model incorporates multiple attention heads for diagnostically relevant feature extraction and correlation-
state update rule for efficient feature updates. Moreover, earlier approaches largely process every patch at high resolution, which is computationally inefficient. ZoomMIL [36] addresses this by recursively sampling fixed number of patches per resolution based on top-k attention scores. However, its sampling strategy depends on predefined attention criteria, which motivates the use of adaptive and contexl-aware patch selection methods.
Deep Reinforcement Learning (DRL) in medical imaging. Conventional RL learns a policy for actions that maximizes the expected reward in novel, unstructured environments. DRL augments RL with the representational power of deep neural networks. The power of DRL was originally demonstrated in challenging strategy and video games (e.g., Deep-Q. A AlphaGo), [25 33 particularly useful in domains with limited annotations, such as automated medical diagnosis, and has been successfully applied to various medical imaging tasks, including localization of anatomical 23 35). 3D image segmentation of MRI scans (20) and tumor classification in WSIs [40] 41] In such tasks, a DRL agent learns optimal search strategy by exploring different portions of the environment (here, image) and receiving sparse feedback the form of classification accuracy, without the need for analyzing the entire image at high resolution.
DRI. is
landmarks in medical scans []. lesion detection
DRL for histopathology. Diagnosis of WSIs by human histopathologists involves viewing the slide at low ("scanning") resolution and zooming into suspected tumor regions - at high resolution for reliable diagnosis. In this context, a recent approach, RLogist 440, models the scanning process of histopathologists as a Markov Decision Process (MDP) using an RL agent. The agent learns an efficient observation strategy, selectively zooming into only a few high-resolution WSI patches before making classification decision, thereby reducing computational overhead and inference time. Similarly, PAMIL [4] addresses the shortcomings of instance-based clustering methods using an RL agent to form pseudo-bags (aggregates) of related instances from high-resolution WSIs. It models pseudo-bag formation as an MDP, forming new pseudo-bags based on past knowledge from the previously formed bags, informed by the label error arising from bag-level predictions.
Feature extraction for histopathology. Deep neural networks such as ResNet [33 and Vision Transformers (ViT) (7). pre-trained on ImageNet (6). are commonly used for feature extraction in computer vision tasks. Their exposure to millions of natural images enables them to identify generic, relevant features, for specialized downstream tasks. Yet, pretraining on natural images is not ideal for medical image diagnosis. 2 For instance, histopathology images lack a canonical orientation, exhibit low color variation due standardized staining protocols, and their diagnostic information varies with magnification. To address this, Kang et al. [17) introduced ResNet-50 and VIT-S models with self-supervised pretraining on large WSI datasets (e.g.. TCGA, TULIP).
3 Model description
Our framework combines multi-attention multiple instance learning (MIL) with reinforcement learning (RL), to identify diagnostic patches in the WSI. We also employ targeted update rule that exploits correlations among instances to efficiently update the state. Each step in the model - and how it addresses shorteomings with existing methods - is described subsequently.
3.1 Problem Statement and Overview
For our task, the model seeks to predict binary target labels Y E (0.1) for a given Whole Slide Image (WSI) X. The latter is 3-channel RGB image that is available in the dataset at multiple different levels of magnification, whereas the former either indicates the presence (versus absence) of cancer or indicates one of two subtypes. We notate the size (in pixels) of the image at magnification
3

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(8)
HAFED
(b)
TSU
U
patch selected at random concatenste
similar patches AW Feat. Agg. Output
N R2 R
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U,
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V 0,0
92
V gdaaa
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High
Feature Aggregator (FA)
Classifier
scores
Agg.
Zoomed-
Figure 2: Components of SASHA. (a) HAFED. During training, features from each patch in each WSI are extracted and aggregated with a hierarchical, attention- based feature distiller (HAFED). High-resolution input U E IR N xxxx undergoes two levels of attentional filtering, first operating across the k high resolution patches in each low resolution patch
R xk and then operating across the N low resolution patches a2 E EN to produce the final classifier prediction Y. During inference, only the sampled patch's 's high-resolution features are analyzed. (b) TSU. Following this the state of all patches whose features correlated with the sampled patch are concurrently updated, with a targeted state updater (TSU) (see sections 3.2 and 3.3 for details).
level m {1, G The goal of our approach is to the image at the lowest resolution G which we call the "scanning-level" as [40], while zooming into and analyzing only select portions of the image non-overlapping patches, such that a given WSI X represented as X = {o1,... [21,...,Iy, EyY }. where N is the total number of patches low resolution G3; note that N can vary across different WSI images. Now, when zoomed in, these patches are magnified to a high-resolution; for this, we choose the highest resolution G in all datasets tested. Therefore, each patch 2; the low-resolution comprises of l patches at the high-resolution; we denote these as (*.1..., T.k)-
higher resolution G For this, each WSI at G is divided into multiple
Given these definitions, the three main components of the algorithm
follows (Fig.
Hierarchical Feature Distiller: from the & high-resolution patches for each low-resolution patch and creates a d dimensional feature representation for The output of this network is used to update WSI "state" and to train downstream WSI label classifier; see Section 3.2 for details.
This hierarchical attention- based network extracts diagnostic features
This network maintains state of the entire WSI, comprising d di- mensional feature representation for each low-resolution patch. As each patch is zoomed in to high-resofution, the state vector is updated, but only for instances whose features correlated with those of the sampled patch; see Section 3.3 for details.
Targeted State Updater:
Deep RL Agent: This agent comprises policy network and value network. Based the current state, the former network produces a probability distribution over a set of actions from which the agent selects the next patch to zoom in, whereas the latter network is used to compute generalized advantage estimate for each state-action combination. Both networks are optimized using the Proximal Policy Optimization (PPO) algorithm;
Section 3.4 for details.
Preprocessing and Feature Extraction. Prior to training and testing, we preprocess the WSI and extract features using a pre-trained model, as follows: To isolate histopathological tissue from the glass slide background on the WSI we apply established segmentation methods 22 at low resolution. The extracted tissue is then divided into non-overlapping patches of size (256 x 256 x 3), again at both resolutions; these operations were performed using the code provided as part of the CLAM 22 GitHub repository. For the first level of feature extraction, we utilize pre-trained ViT-based encoder 77 as described in the Related Work (Section 2). Each input patch is encoded into a feature embedding vector of dimension d. We denote the input feature representation of WSI at low-resolution as Z E IR xxd, and at high-resolution as U E I Nxkxd.
3.2 Hierarchical Attention-based Feature Distiller (HAFED)
A key first step with diagnosis of large WSIs is to learn informative, yet parsimonious, feature representations for the high-resolution patches. Such representations are critical for the RI. agent to update WSI state, as it makes decisions about which patch to visit next, as well as for the

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final classification. Recent advances in ivention-baseed MIL models compute such informative representations for nigh-resolution whole-slide images (WSIs) [T]. However, in an RL setting, the state representation for the WSI at each time step comprises a mix of information both at low and high-resolutions, depending on the specific patches zoomed in (see next). is, therefore, desirable for both the low-resolution and high-resolution feature representations to have matched dimensionality; such a design choice considerably simplifies downstream computations (e.g., classification). To achieve this, we propose Hierarchical Attention- FEature Distiller (HAFED) (Fig. 2
HAFED is two-stage attention based model designed to operate directly on high-resolution feature patches while producing representations compatible with low-resolution patches. In the first stage (Fig. 24. Feature Aggregator), the model takes as input high- high-resolution features U E RNxxxx and learns to estimate attention scores for each of the k sub-patches reflecting the latter's 's relative importance in each of the N low resolution patches; a stochastic instance masking strategy is applied to avoid over-fitting [39] A weighted aggregation of the sub-patches based on these attention scores produces a compressed feature representation V E R xd, thereby reducing the dimensionality of the high-resolution features and aligning them with the dimensionality of the low-resolution features Z E IR xx In the second stage (Fig. 2g. Classifier), a second attention mechanism operates across the N patch-level features in V to identify diagnostically important patches at the slide level. A weighted aggregation of the N features, guided by the second-stage attention scores, yields a final slide-level embedding h E Rd that summarizes the entire WSI. This d-dimensional embedding is then used for downstream WSI classification. We employed multiple (M) attention branches, and the model is trained with the sum of three losses [39]: i) similarity loss to learn distinct attention weight patterns in each branch, ii) the label loss associated with each branch, and iii) overall label loss.
During training, both stages of the model are trained end-to-end by visiting all of the N patches at high resolution. Thus, attention weights, and feature representations, in both the first and second stage of the model are learned in a "label-aware" manner using the ACMIL loss (classifier and dissimilarity losses). During inference, a single high-resolution patch a; is visited at each timestep / and its compressed feature representation V(a,) E R is used to update the slide state. While previous approaches [40] employ generie, label-agnostic feature extractors HAFED identifies important regions at both the sub sub-patch (first-stage) and patch (second-stager levels, which enables efficient learning of diagnostically-reles releevant and parsimonious feature representations from high-resolution WSIs.
3.3 Targeted State Updater (TSU)
In our deep RL model, each WSI is represented by "state" St E R at each timestep ł in an episode. The RL agent makes sequential decisions of which patch to visit based on S,. We describe, here, how this state is computed and updated over successive time steps: Scanning begins with the low-resolution feature representation Z, which serves as the initial state Sg. While Z provides a coarse global view of the WSI, it often lacks the fine- tra-ggained diagnostic details necessary for accurate classification. Obtaining high-resolution features for all patches is computationally expensive; thus, at each step t. selectively zoom into a single patch at high resolution to gather additional information. The selected patch, denoted as a,, yiclds high-resolution sub-patches, which are processed through the first attention model of HAFED. A weighted aggregation of the sub-patches produces a refined high-resolution feature vector V(ae) E Rd that captures detailed local information.
The challenge then is how to effectively integrate this newly acquired righuresolution information into the overall state representation S, without corrupting previously acquired information. One possible approach (e.g., Zhao et al. 40)) involves concatenating V V(a+) with every patch in St, training an MLP (multi-layer perceptron) with this augmented state as input to produce an updated global state representation S that gradually converges toward the feature representation obtained from all high-resolution patches. We argue that such a global state update is far from idcal. Because only one high resolution patch is observed in each time step, updating instances globally risks corrupting the state, especially when instances unrelated to the sampled patch are concurrently updated. Moreover, information from the sampled patch is likely to be relevant, primarily, for updating instances that share features similar to it 32 22
To exploit these correlations, we propose targeted, similarity-based state update (TSU) model (Fig. 2b). For the selected patch d, we compute the cosine similarity between S, and all other patch representations in S.. We then define a set C' containing the indices of patches whose cosine similarity with S, (a:) exceeds a threshold (r, a hyperparameter), excluding a, itself. Only the patches in C' are updated: V (a,) is concatenated with the representations of patches in C and passed through
5

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Algorithm 1 Agent-Ennironment Loop at Time Step L
Input:
Current state S; E R. Set T of sampled patch indices, upto time
7: V(ac) < (u BH) (extract features V(ae) E B from high-resolution zoomed in features, Un, IR of the sampled patch, with the HAFED model]
Parameters: Policy network : HAFED fea- ture aggregator model [- 0H) and classifier fc 8c); TSU state update model fs(:; 8s) Output: Updated state S1+1 and action a++1
8: S441 (at) < V(a+)) (update the sampled patch with its high-resolution feature repre- sentation]
at time t ++1 1
1: at (- | St) {Sample patch index at from the policy distribution}
2 7 {iden- tify set of patch indices whose cosine similar- ity with Sa(ae) exceeds a threshold) for each patch index in C' do
C' C {i:
cos (i),
2: It IU } [ttore selected patch index)
3: if training then
10:
4: dt + (Sc 0c) (predict WSI label with
11:
S (t) < (i) Sy(a,). V(a,)): 0s)
HAFED classifier)
(targeted update of similar patches with the
5: Update my with PPO algorithm; interme- diate reward Ft- CCF( 9t)
TSU model)
12:
end for
6: end if
13:
return S.+1, S141,a++1
an MLP trained to minimize the MSE loss against the corresponding entries in V(C), Additionally, (ax) is directly replaced with V(ae) because its exact high-resolution representation is available. In the next time step, the patch a, is masked out, and future actions are sampled from the remaining unmasked patches; this strategy is applied during both the training and evaluation stages. With ablation studies (Table we show that this selective update strategy outperforms naive approaches.
3.4 Sequential A Attention-based Sampling for Histopathological Analysis
We train a reinforcement learning (RL) agent to select which patches to zoom into, leveraging feature distillation with the HAFED model and state update with the TSU model. We formulate the patch selection process as a sequential decision-making problem, where an agent interacts with a slide-specific environment over multiple steps (Algorithm[] []. During each training episode, at each timestep I, the agent observes the WSI state S,, which initialized with the low-resolution feature So = Z. Based on S,, the policy network лe produces probability distribution over set of actions from a discrete action space {1, 2....,A0 NN. The agent samples an action de, corresponding to the next low-resolution patch to zoom according to the current policy Te, (- SS)) with stochastic sampling rule. The zoomed-in patch is passed through HAFED to obtain its high-resolution representation V(a).. This representation is then used by the TSU to selectively refine the current state S,, by modifying only those patches that are similar to ae in the feature space. This produces an updated state S The classifier trained together with HAFED then uses the updated state S to produce slide-level prediction fj.- This prediction is compared against the ground truth label y to compute time-step-wise reward, based a negative cross-entropy (CE) loss. This feedback is used to update the policy network using Proximal Policy Optimization (PPO) (31] algorithm. We employed Generalized Advantage Estimation (GAE) in computing the advantage function [30]. whose output was trained to predict the reward until the end of the episode. Both the policy and value network were modeled on standard architectures (T5] (details in Appendix A.2.3). The same steps were followed during evaluation, except that the action with the highest probability based on the policy network's output was chosen deterministically.
Experiments
4.1 Dataset and implementation details
We evaluate the effectiveness of SASHA with two publicly available benchmark datasets CAME- LYON16 breast cancer 3 and TCGA-NSCLC lung cancer dataset [26.. While the CAMELYONI6 labels correspond to normal versus pathological WSIs, TCGA-NSCLC labels correspond to dis- tinguishing two different types of cancers, adenocarcinoma versus squamous cell carcinoma. We followed standardized train-test splits for both datasets. In addition, we also evaluate SASHA on multi-class classification tasks using the BRACS and Camelyon+ datasets. Further details on the datasets, hyperparameter selection, implementation, and training are in Appendices A.l and A.2.

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CAMELYON16
TCGA NSCLC
Method
Accuracy
AUC
F1
Accuracy
AUC
Max Pooling
0.933 +:0007
0,971=00001
0.911=0.008
0.881=0.010
0.952=000088
0.882=0.010
Sumple 100%
ABMIL
0,947=00008
0.958=100116
0.928:0.011
0.883=0.045
0,947=0.021
0.884=0.045
CLAM SB
0,909=000012
0.976:10.008
0.931+0.015
0.905:4.0119
0,961+0.009
0.907=00018
TransMiL.
0.936=0.006
0.953++0.008
0.912++0008
0.898=000115
0.964=00004
0.899=0.016
DTFD
0.944=0.014
0,977=00008
0,944=0.014
0.911=0.012
0.962=0.010
0.911=0.022
ACMIL
0.941=000015
0.970=10.011
0.924:1.0117
0.906::0.025
0.959=000006
0.907=0.026
HAFED (ours)
0.963+0.008
0.980+00003
0.951+0.011
0.923+0.011
0.966
0.925-0.010
Zoom-MIL (k=300)
0.780 +00017
0.700 +:00229
0.738 +00015
0.908 +:0.004
0.967 +:0004
0.908 +00004
10%
RLogist-0.1
0.824
0.829
0.828
0.892
Zoom-MIL (k=80)
0.753=00.02
0,743=+0.007
0.743+0.012
0,907=10002
0.061=0.012
0,907=0.002
SASHA-0.1 (ours)
0.901
0.014
0.031
0.897+0.023
0.956+0.023
0.898-0.024
20%
RLogist-0.2
0.862
0.879
0.839
0,903
Zoom-MIl. (k)10))
0.759+0.001
0.794++0.00
0.755++0.001
0.912++00005
0.970+0.001
0.912-0.005
SASHA-0.2 (ours)
0,953
0.979=10008
0,937=200224
0.912:#00100
0,963=00014
0.914=0.011
Table 1: Performance comparison on the CAMELYON16 and TCGA-NSCLC datasets. The top
7 rows reflect models that process all patches at high resolution ("Sample 100%"). Underlined values indicate the best performance among these methods. Rows 9-11 and 12-14 reflect models that selectively sample either "10%" or "20%" " of the patches, respectively. Bold values indicate the best performance among these methods at the respective sampling fraction.
4.2 Baselines and Metrics
We evaluate and compare the performance of our model against two types of baselines. First, we evaluate a range of different sota methods that analyze all patches in the WSI at high resolution, including MaxPooling, ABMIL [15. CLAM [22). TransMIL (32), DTFD (38), and ACMIL (39]. We also include comparisons with our hierarchical attention-based (HAFED) model (Section 3.2) with the full-resolution WSI. Yet, these methods serve only as an upper bound on achievable performance.
For a fair comparison, we also evaluate models that sample only a fraction of the WSI. Specifically, we compare approach with RLogist [4)] when trained to sample exactly 10% and 20% of patches at high resolution, under identical experimental settings. We also compare with a non-RL-based Zoom-MIL (36] model that samples exactly k 80, 160, or 300 patches at low resolution (5x - 20x). The choice of k values aligns with the patch statistics of the dataset: WSIs contain ~800 low-resolution patches on average; thus, k 80 and
160 correspond 10% and 20% of the available patches, on average. providing a fair comparison with the RLogist-O. -ogiist-O... SitiiittO./SSSAAAA. 1/SASHA-0.1 and RLogist-O.2/SASSHE
settings, respectively. k- 300 follows the default configuration used in the original Zoom-MIL paper [36]. This setup allows us to systematically analyze the effect of varying observation budgets on model performance. In addition, we also compare against ACMIL and DTFD under the same observation budgets, with random sampling of high-resolution patches (Table 2); details in Appendix A.3. Models were trained with the official codebase provided by the respective studies, except for RLogist. The Roogist codebase is insufficient for replication; therefore, we report the performance metrics as is from the original study 40
Our performance metrics include Accuracy, AUC, and Fl scores for normal versus pathological tissue classification (CAMELYON16) or for classifying between the different tumor types (TCGA-NSCLC), In addition, we expect that selective sampling models, such as ours, would exhibit significantly faster inference times compared models that examine all patches high resolution; therefore, we also evaluate and compare inference times (Fig. 3P) Finally, we also compute metric of "WSI compressibility". We define this as a ratio of the original WSI image size, at full resolution, to the size the of WSI representation used by each model's 's classifier to make a final decision. This metric has important real- world implications in terms of efficient archival and storage of WSI images. We compare models of comparable complexity and performance (CLAM, TransMII., ACMII,, DTFD, SASHA) in terms both WSI compressibility and overall model size.
4.3 Classification Performance, Inference Time and Compressibility
Classification Performance. SASHA consistently outperforms the sota competing deep RL model RLogist- - by a large margin, for both the 10% and 20% observation budgets [Table[] SASHA-0.1 and SASHA-0.2. vs RLogist-0.1 and RLogist-0.2, respectively). Improved classification performance was reflected in all 3 metrics quantified: Accuracy, AUC, and F1-score Zoom-MIL, performed comparably with SASHA-0.2 for the TCGA benchmark, but underperformed heavily for CAMELYON-16 16. Moreover, HAFED model performed on par with the best sota models (c.g. ACMIL, DTFD) that
7

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(a) Putches
300
(e)
1.0
Time
Inter time
Ioec 4)
1.0
100
8.20% 40-
ADMIL
DFF3
HAPED
8-100
[d)
10%
20%
(9)
int
WSi
everlaphs
Ton
(b) Similar Patches Updated
Time
10
AOMEL
Draa
HAFED
5.20%
1.0
(h)
0.06
1.0
(DE(0)
0.04
Avg. Atin.
(P)
0.5
0.02
0.01
ACMIL
DTFD
HAFED
9-10%
8.20%
And
RL. Rnd
0.0
Figure 3: (a-b) Patch selection and update strategy by SASHA's RL agent. Box color corresponds time fraction episode. (c-d, h) Inference time, compressibility and expected calibration error (ECE) for SASHA and HAFED compared other models. S-10% and S-20% refer SASHA-0.1 and -0.2 models, respectively. (e) Tumor fraction in patches sampled by SASHA's RL agent (S) versus non-sampled patches (NS). (f-g) Top-k attention score overlap fraction and average attention score for patches sampled by SASHA's RL agent versus random policy (Rnd). In (e-g) left and right pairs of bars reflect 10% and 20% observation budgets, respectively. *** indicates p < 0.001.
analyze the full, high-resolution WSI (Table underlined). Interestingly, even with sampling 5x fewer patches, SASHA-0.2 nears ceiling performance achievable with HAFED, when all 100% of patches are sampled at high resolution (Table [ compare SASHA-0.2 with HAFED).
Inference time. The average inference time per WSI for the CAMELYON16 dataset, including both pre- prepprccsssing and feature extraction time, was ~117 seconds, using HAFED (Fig. 3b. Other sota attention-basee based methods like ACMIL and DTFD also exhibited similar average inference times per WSI. The most expensive operation was feature extraction for each of the high resolution patches. By contrast, model samples only few patches at high-resolution, thereby leading to substantial decrease in overall inference time, 14 seconds and ~26 seconds per WSI for SASHA-0. and SASHA-0.2 8x and 4x speedup, respectively compared to the second fastest model (Fig 3
WSI compressibility. Similarly, comparison of the WSI compressibility revealed a significant (> 16x) improvement, compared to models that examine the entire slide at high resolution (Fig. (J)) The main reason for this saving is the fact that our HAFED model encodes features for both low- and high-resolution patches in uniform N d dimensional space; by contrast, other models encode high- resolution features in a N Is x d dimensional space, where i reflects the ratio of the zoomed-in to the scanning resolution size. The full set of results is shown in Appendix A.4. These results highlight the efficiency our method in balancing performance with computational and representational costs.
4.4 Ablation Study and Limited Sampling
We performed a systematic ablation study removing each component of the model in turn or replacing it with naive variant and evaluating it with the CAMELYON16 dataset. These included: a) replacing the WSI-pretrained ViT with a ResNet50, used commonly for feature extraction [4
b) replacing VIT with the CONCH medical smage-encoder(2 pairs, c) replacing multi-branch attention with single branch in the Classifer (15). d) replacing the
pretrained 1.17M image caption
targeted state update policy with global state update 44), e) removing the TSU and updating the
state only of the sampled (local, regh-resolution) patch, and f) and selecting an action based on random policy (see "Variant's in Table ranging from 7-45%, relative to the baseline SASHA model; similar drops occurred in AUROC and Fl scores Table 2 The strongest impact occurred when changing the RL policy (Table 2 f). The next strongest impact occurred when changing the feature extractor to ResNer-50 (a), but this impact was mitigated when using the CONCH encoder (b). Intermediate levels of impact occurred when changing the classifier to branch attention (c) altering the state update method (d-e).
every case, we observed sharp drops in accuracy.
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Variant
Feature
Classifier
TSU
RL Policy
Accuracy
AUC
F1
SASHA Default
0,964
0,980
0,953
(a) ResNet-50
0,860
0.817
0.780
(b) CONCH
/
0,930
0.950
0.905
(c) 1 Att. Branch
/
V
V
0.899
0.964
0.851
(d) Global Update
0.837
0.824
0.779
(e) Local Update
/
0.860
0,901
0,800
(I) Random Policy
/
0,516
0,550
0.553
gg) ACMIL-0.2 (Rnd.)
0.838
0.857
0.773
(h) DTFD-0.2 (Rnd.)
0.862
0.878
0.862
Table 2: Ablation study on CAMELYON 16 with the SASHA-0.2 setup using 20% high-res patches. / denotes the default, * the altered component.
Additional ablation experiments are described in Appendix A.3. These results indicate that every component of our model was critical for achieving high performance, comparable to sota models.
Finally, we examined the effect of imposing a limited observation budget on other sota models, and compared the performance of SASHA under the same budget. Specifically, ACMIL and DTFD were trained and tested with a random selection of high-resolution patches under a 20% observation budget for each WSI. In this case also we observed substantial drops in performance relative to the default SASHA model (Table g-h). In other words, randomly sampling a limited fraction of nigh-resolution patches, even with the best models, failed to outperform SASHA.
4.5 Patch selection strategy: Explainability
When deploying deep RL models in medical settings, it is essential that they learn not only an effective. but also an explainable action policy. For this, we explore whether SASHA's RL agent exhibits a meaningful patch selection strategy prior to classifying WSI images (Fig. 3h and Fig. (b)) We propose that such strategy must satisfy at least two important requirements: In WSIs containing tumor tissue, the RL agent must preferentially sample patches with a large fraction of tumor tissue for high-resolution scans, and ii) an effective RL policy should preferentially sample patches afforded a high attention score by sota models that examine the entire WSI at high resolution. We evaluated each of these criteria in the data. Indeed, the tumor fraction in patches sampled by the RI. agent at high resolution (10% with SASHA-0.1 or 20% with SASHA-0.2) significantly exceeds that in corresponding number of randomly chosen patches, among those not sampled by the agent (Fig. 3 p<0.001). Moreover, the RL policy guides the agent to preferentially samples patches afforded high attention score by sota models: both the average attention score (Fig. 33. p<0.001) and the fraction of top-k k patches sampled under the RL policy were statistically significantly greater than the fraction of patches sampled by a random policy (Fig. Bi, p<0.001). In other words, our RL agent's 's sampling actions were guided by efficient, intuitive and explainable policy.
4.6
Effect of observation budget: Calibration
We have shown that SASHA achieves near-sota performance at a fraction of the inference time, even with sampling 10% or 20% of the patches at high resolution. Is there any advantage sampling more patches, other than achieving marginal improvement accuracy, especially when the inference time increases almost linearly with the proportion of rish-resolution patches sampled. Here, we show empirically that sampling a higher fraction of patches yields better calibrated predictions. With well calibrated models, the model's s confidence its prediction (e.g., predictive uncertainty) is a useful indicator for when referral to a specialist is warranted 8 We quantified the expected calibration error (ECE) I1 as a measure of the degree of calibration of the models. The ECE decreased systematically at the proportion sampled patches increased from 10% (SASHA-0.T 1) to 20% (SASHA-0.2) to 100% (HAFED). This pattern occurred possibly because providing the model more timesteps to sample the WSI in each RL episode enabled the model to recover from sub-optimal sampling actions in the early timesteps, and to sample the relevant portions of the WSI in the later timesteps. Surprisingly, ECE with SASHA-0.2 was lower than that of even sota attention- based models like ACMIL and DTFD (Fig. Bh). In other words, while a shorter observation budget encouraged efficient learning, a longer budget produced better calibrated predictions.

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5 Conclusions and Limitations
We show that an attention -aaeeiot-ed deep RL agent outperforms or performs on par with sota models on WSI classification, albeit at fraction of their memory and computational costs. Key limitations of the model include a significant impact of the observation budget on model calibration, and the need train the model with all high resolution patches, which significantly increases training time; other limitations are discussed in Appendix A.6. Nonetheless, we demonstrate explainable, reliable, and calibrated predictions, which could enhance clinicians' trust in model predictions.
Acknowledgements
This research was supported by a Department of Science and Technology SwarnaJayanti Fellowship, Google Research Grant, a Pratiksha Trust Intramural Grant and a Gore Subraya Bhat Chair Associate Professorship Digital Health (all to DS), Ministry of Education Scholarships (to TG and NM), an Institute of Eminence (IoE) Grant (to IISc), Walmart Centre for Tech Excellence at IISc, the Centre for the Promotion of Advanced Research Grant (CEFIPRA: Project 7102-1), Kotak HSS Fin'Tech Grant, DST-SERB Core Research Grant (CRG/2021/008330) and a Pratiksha Trust Young Investigator Award (all to GT). We are very grateful to Karm Patel, Mainak Biswas, Aditi Saxena, and Tej Pratap Yadav for help with model development and simulations, and Raj Jain for help with the original draft of the manuscript. The authors declare no competing interests.
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Sequential Attention-based Sampling for Histopathological Analysis
A Appendix
A.1 Dataset details
The CAMELYONI6 dataset comprises 399 whole-slide images (WSIs) of lymph node sections. After pre-processing and segmentation, we generated non-overlapping patches of size 256 x 256 (x3 RGB channels). On average, this resulted in, on average, ~800 patches per WSI at scanning resolution of 5x, and 12,,00 patches at high resolution of 20x. We used the official test set for evaluation
31. The training set was further divided to create a validation set (10%) while maintaining a similar proportion of normal and tumor slides in each subset.
The TCGA-NSCLC dataset comprises 1,054 whole-slide images (WSIs) obtained from the TCGA- NSCLC repository under the TCGA-LUSC and TCGA-LUAD projects. Ten low- low-quality slides without magnification information were discarded, and three slides with errors were ignored. We followed the same test split as in DSMIL [18. The test set comprised total of 213 slides: 104 from TCGA-LUAD and 109 from TCGA-LUSC. Following preproeessing. we obtained, on average, ~212 patches per WSI at 5x magnification and ~3,393 patches at 20 magnification. The training set was further divided to create validation (20%) set while maintaining a similar proportion of labels in each subset.
Train
Val
Test
Dataset
Task
Class
Class
Class
Class 2
Class
Class 2
CAMELYONI6
Classify lumor
140
102
18
80
49
TCGA-NSCLC Classify sub-type
339
319
86
81
104
109
Table 3: Summary of datasets used in this study. CAMELYON16 involves binary classification of normal versus tumor slides, while TCGA-NSCLC involves distinguishing between adenocarcinoma (LUAD) and squamous cell careinoma (LUSC). The table shows the number of WSIs for each class in the training, validation, and test sets. Here, "Class I" refers to Normal (for CAMELYON 16) and LUAD (for TCGA-NSCLC), while "Class 2" refers to Tumor (for CAMELYONI6) and LUSC (for TCGA-NSCLC).
The CamelyonPlus dataset 21, comprises 1,347 whole-slide images (WSIs) derived from the curated CAMELYÓN16 and CAMELYON17 collections, after removal of low-quality slides and re-labeling involves four-class classification. We created training, validation, and test splits to ensure class balance across all four labels. A summary of the splits and labels can be found in Table
Train
Val
Test
Dataset
Neg.
Mi-m.
Ma-m.
ITC
Neg.
Mi-m.
Ma-m.
ITC
Neg.
Mi-m.
Ma-m.
ITC
Camelyon+
608
122
174
36
130
26
38
9
131
26
38
9
Table 4: Summary of Camelyon+ dataset. The WSIs have the following 4-class slide-level labels: Negative (Neg.), Micro-metastasis (Mi-m.), Macro-metastasis (Ma-m.), and Isolated Tumor Cells (ITC). The table shows the number of WSIs for each class in the training, validation, and test sets.
The BRACS (BReAst Carcinoma Subtyping) dataset comprises 547 labeled WSIs obtained from the public repository [ This dataset involves 7-class classification task, characterizing each tumor as one of three benign (BT), two atypical (AT), or two malignant (MT) sub-types. We ignored the "normal" subtype in the benign (BT) class, and each set of subtypes within BT, AT, and MT was grouped into its respective class. Additionally, a small number of slides (7/503) that lacked all
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relevant levels of magnification were excluded. After preprocessing, we use the official dataset split across train, validation, and test split with details outlined Table
Benign (BT)
Atypical (AT)
Malignat (MT)
Train
171
52
135
Val
16
14
21
Test
25
23
16
Table BRACS dataset distribution for 3-class classification (Benign, Atypical, Malignant) across training, validation, and test splits.
A.2
Implementation and Hyper-parameter Selection
A.2.1 Patch feature distillation with HAFED
The first step of SASHA involves feature extraction and aggregation of high-resolution patch fea- tures, which specifies the RL agent's s state S(t). We use a Vision Transformer (ViT) pretrained on histopathological images to extract features from each zoomed in, high-resolution patch [17]. This VIT was used as is without any additional fine tuning, and its output a 384 dimensional embedding per patch was provided as input hierarchical attention-basee feature aggregator and distiller (HAFED).
Layer name
Input Dim
Output Dim
Input Batch
Output Batch
Layer-1 Layer-2
384
128
U. (Un, U2, ... U.
A; {aa 0:25 Osk
384
128
U. (Ua, U2,.. Usk
B. {b11, bi2..... bik
Layer-3
128
sigmoid(A) (A) tanh(BB
a1 E R
Vi ai
U1,
V/ E Rdd
Layer-4
384
128
(Vi, Ve...., VNl
C (e,, CN}
Layer-5
384
128
== (Vi V22 VN
D d2,, ..... dN
Layer-6
128
M (5)
sigmoid((") tanh(D)
ag R Nx
1
02 (::).
AER"; h V, HER
a
M
Layer-7
384
h
logits
Table 6: Architecture of the HAFED model. Layers 1-3 correspond to the feature aggregator (FA) and layers 4-7 correspond to the classifier. (~~800 for camelyon-16) is the number of patches in low-resolution WSI, & (16) is the number of sub-patches in high-resolution per one patch in low-resolution, d (384) feature embedding dimension of one patch. U, E R 16x3884 A E IR 16x 128 B, E R R11 C R Nx128 D R Nx h E 384
As described Section 3.2] the first stage of HAFED acts as a feature aggregator (FA); the architecture of this network is shown in Table 6 We employed single attention branch, which takes high resolution features U E N xkxd and assigns attention scores each of the sub patches. The inner product between the attention scores and U produces V E Nxd which is in same dimensionality as the low-rcsolution features (Z E IR Nxd). The second stage of HAFED acts as classifier, which is trained end-to-end with the feature aggregator. Here, use five attention branches for the CAMELYONI6 dataset and one for the TCGA-NSCLC dataset; the number branches was determined as hyperparameter based on the respective validation set (Table 7). This stage takes V as input, assigns attention scores across the N patches, and computes the inner product between the scores and V to produce of this network is shown in Fig. 2a. We applied masking to the top four and top eight patches with a probability of 0.4 in the first and second stages, respectively (Table
di4-simensi embedding representing the entire WSI. The architecture
HAFED trained with an annealed learning rate schedule starting at 100- with the AdamW optimizer and weight decay of x10-1 The HAFED module is trained end-to-end using the
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following loss components: (I) a cross-entropp loss between the final predicted label and the ground truth label, (2) cross-entropy loss applied independently to the predictions from each attention branch in second stage and ground truth label, and (3) dissimilarity loss that encourages diversity among the attention scores of the different branches in the second stage. This dissimilarity loss encourages each branch to focus on distinct tumor features, thereby improving the model's ability to capture complementary diagnostic features (3
During inference, at each time step, the first stage of HAFED (FA), receives as input k zoomed-in sub-patch images corresponding to the selected action. It assigns attention scores to estimate the relative importance of each sub- sub-patch and aggregates their features by computing weighted sum based on these scores; this is then used to update the state (see next subsection, Appendix (A.2.2). In the final time step. the second stage of HAFED (classifier), takes the state representation Sn and predicts the label ij- In addition to the final classification, the second stage also used to assign rewards during SASHA' RL agent training (see subsection Appendix A.2.3
Model
Hypere-urumettrs
Selection Criterion
SASHA 0.1/0.2
CAMELYON16
TCGA-NSCLC
Range
Values
Range
Values
Attn-2 in HAFED (M)
Validation
1-5
5
1-5
HAHRD
Aunn -1 Masking Probahility
Validation
0.4 0.6
014
0.4 01.6
0.4
Attn -2 Masking Probability
Validation
0.6
0.4
0.4-0.6 0.6
0.4
TSU
Cosine Threshold in TSU (T)
Validation
0.88 0.98
0.95
0.8 0,92
0.9/0.9
Discount Factor
Validation
0,8-1.0
1.0
0.8-1.0
1.0
Clipping Parameter
Default
0.1
0.1
Actor & Critic
Entropy Regularization Coefficient
Default
0.001
0.001
Gradient Clipping Threshold
Default
0.5
0.5
Table 7: Hyperparameters for the CAMELYON16 and TCGA-NSCLC datasets. In the Values column, entries separated by a slash (/) represent values for SASHA-0.1 and SASHA-0.2 models respectively; singleton entry reflects the same value across both SASHA variants.
A.2.2 Updating WSI state with TSU
The second step of SASHA involves updating the RL agent's state with the high-resolution patch representation extracted with HAFED. During training, the states of similar patches concurrently updated with a targeted state update (TSU) rule. Specifically, this included all patches whose low- resolution representation's 's similarity to the currently selected patch, exceeded a threshold value (r), based on cosine similarity; was tuned as a hyperparameter (Table
The architecture of this TSU network is shown in Table For each patch or that crosses the similarity threshold, the TSU model takes as input a concatenated vector comprising (i) its low-resolution features Za, E Rd. (ii) the row-uusolution feature representation of the selected patch o,, Za, Rd and (iii) the high-resolution feature representation of the selected patch V., E Rd. Note that while (i-ii) are obtained directly from pretrained ViT, (iii) is obtained from the FA output of the HAFED model. Concatenating these 3 features results in a 3dddimensional input (3 x 384 = 1152), based on which the model predicts d-dimensional high-resolution feature of the patch a,, Va.- The state of the selected patch alone is updated by directly copying its high-resolution HAFED (FA) output Vo,, into its (corresponding) entry in the WSI state S(t). In this manner, the TSU integrates information from the low-resolution representation of patches similar to the currently visited patch, with the high-resolution representation of visited patches update the WSI state.
During training the TSU network is trained by sampling patches randomly, and updating their states based on the predicted high-resolution feature of each patch (V) and its actual high-resolution feature (V) obtained from HAFED's ' feature aggregator. Once a patch state is updated- either directly, by visiting it, or indirectly. by the similarity- -based rule- its index is masked out, and it is no longer visited during training. The TSU model is trained for 500 epochs with Adam optimizer and learning rate of
The best model was chosen based on the minimum of the validation loss.
During inference, the low-resolution patch to visit at each time step a, is selected by RL agent. The state patches similar to the currently visited patch are updated with the same sequence of steps as described for the training. A key difference is that, in this case, only the directly visited patch indices
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are masked out, and the RL agent is free to select, for its action in the next time step (t + 1), any of the patches that have not been visited directly in the previous time steps .O.....t).
Layer
Input Dim
Output Dim
# Parameters
FC-1
1152
768
885,504
FC-2
768
384
295,296
Layer Norm
384
384
Table 8: Architecture of the TSU model. The input is 3d 1152 dimensional vector (d 384; see text for details). The final output is d-dimensional. FC: fully connected layer with bias.
A.2.3 Actor and Critic
The final component of SASHA is reinforcement learning (RL) agent responsible for sampling. sequentially, the most relevant patches for high-resolution analysis. This selection is formulated as sequential decision-making problem, where the agent learns to identify diagnostically informative regions within each slide. We adopt the Actor-Critic framework and train the agent using the Proximal Policy Optimization (PPO) algorithm, a widely used on on-policy method known for its stability and effectiveness in complex RL tasks (31).
The purpose of the actor network is to learn a policy a conditional probability distribution over actions given the current state St. In our setup, the environment is described as, the state is initialized with low-resolution features (Z E RNxd), the action is selected from a discrete action space N} (indices of low-resolution patches), and the reward function is specified as the negative of cross-entropy (CE) loss between the predicted WSI label, at time step 4, and the ground truth. Given the definitions of state, action and reward, the actor and critic networks are designed as follows.
A E {1,
The Actor network follows the same architecture as Layers 1-3 in Table 6 A softmax is applied to the output attention scores to obtain a categorical distribution (i.e., the policy) over the N patches, from which action a, (i.e.,& patch index) is sampled during training. To reduce redundancy, once a patch is selected, its index is masked in all subsequent time steps - by setting its corresponding logit to negative infinity effectively removing from consideration for future visits. the rationale behind this policy is that zooming into the same patch multiple times will not yield additional information, as the patch content remains unchanged. After selecting dr, the Target State Updater (TSU) module is invoked to update the current state S, to the next state S.+1, thereby incorporating the high-resolution information from the selected patch into the state representation.
The Critic network also mirrors the backbone of actor network i. architecture of Layers 1-3 in Table 6 with additional final layer that takes the aggregated state representation (a SS)), and predicts the reward-to-go or expected return given the current state St. The reward-to-ge is used in calculating the generalized advantage estimate (GAE, (0).. During training the actual reward-to-pe from the given state known, and the critic network is trained by minimizing MSE loss between the predicted and actual value.
Both networks are trained for 15 epochs using the AdamW optimizer with an annealing learning rate starting from 1 x 10-5 and weight decay of 1 x 10-3, Network hyperparameters are reported in Table 7 During training of the Actor and Critic networks, all other components of SASHA are frozen (i.c.. HAFED, TSU), and only the actor and critic networks are updated. We sclect the best policy across epochs as the one that minimizes the validation loss while simultancously maximizing the sum of the validation AUROC and Fl scores (min, [Loss + 2 (AUROC + F1)]). This formulation ensures that the selected policy balances both accuracy (AUROC and F1) and calibration (validation loss).
During inference, given the current state S,, the Actor network outputs policy the form of a categorical probability distribution over the N patches. The action is then selected deterministically as the patch index with the highest probability. In a later section (Appendix A.3.3) we explore the effect of alternative, stochastic selection policies during inference.
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A.3 Additional Control Experiments
A.3.1 Multiclass Classification Performance of SASHA
We extended the SASHA framework to evaluate its performance on multiclass classification tasks using the Camelyon+ and BRACS datasets (see Appendix A. for a description of the datasets).
For both datasets, the SASHA framework was trained using experimental settings identical to those of the binary classification tasks. The same patch-level sampling strategy and aggregation mechanism were employed to ensure consistency in evaluation. To mitigate class imbalance, we employed a resampling mechanism that oversamples minority classes (e.g., ITC for the Camelyon+ dataset) during training.
The performance comparison (Table 2 shows that the methods (HAFED. SASHA) perform com- parably with other sota methods, even in this multiclass setting. As before, sampling only 20% of the patches (SASHA-0.2) achieves performance that is comparable to methods that sample all of the WSI patches at high resolution (e.g., DTFD, ACMIL).
CAMELYON+ AUC
BRACs
Method
Accuracy
Accuracy
AUC
100%
DTFD
0.848 0.022
0.021210.023
0.80410.00
0.570.1..035
0.782.10.032
0.570
ACMIL
0.832:10.013
0.898110.005
0.786110.023
0.53410.046
0.749110.014
0.596:10.059
HAFED (ours)
0.866=0.014
0.93420.01
0.84720.015
0.54410.03
0.74710.01
0.575+0.036
SASIIA-0.1 (ours)
0.869100008
0.905110.003
0.84810.003
0.59610.001
0.729100001
0.532.10.010
SASHA-0.2 (ours)
0.871=0.005
0.935:10.006
0.85240.006
0.598=0.016
0.762:10.010
0.533+0.079
Table 9: Comparison of multiclass classification performance of different methods on the Camelyon+
and BRACS datasets. Reported metries include weighted-averagee Accuracy, AUC, and F1-score.
A.3.2 Alternative Models under an Observation Budget
We compared the performance of SASHA with competing sota models like ACMIL [39 and DTFD 38 under the same observation budget of either 10% or 20%. That is, we predicted slide labels by having these sota models examine only limited fraction of patches in each WSI at high resolution, matching the fraction of the WSI sampled by SASHA.
Each model was trained with a combination of low-resolution and high-resolution patch features and tested, similarly, with a combination of high and low resolution patch features in the same proportion as of training. Specifically, for given slide, if N is the total number of low-resolution patches and each low-resolution patch corresponds comprised of total of NL + NH patches comprising Ny (N [f NN) low-resolution and Nu = [f N] k high-resolution patches, where f E {0.1. 0.2). Because low and high- resolutiot patches were of the same pixel dimensionality (256, 256, 3), the pretrained VIT model (Section 3 was used to extract features from each such patch; this yielded a feature vector W E RNNN+NN, +N,) xa which was provided as input to each model for label prediction, both during training and evaluation.
1: high-resolution patches, then for that slide, the input
We tested these models on the CAMELYON16 dataset. First, we created five random splits of the train and validation sets of the dataset. For each dataset split, we performed random patch sampling with three different random seeds. Then, for each model, we computed the average performance across these 15 (5 splits 3 seeds) model combinations. In each case, the sum of validation accuracy and F1 scores was used to determine the model for evaluation.
As shown in Table 10, such strategy of sparsely sampling patches substantially reduced the performance of ACMIL. and DTFFD models. In each case, SASHA sampling either 10% (0.1 fraction) 20% (0.2 fraction) of the patches at high resolution outperformed the sota models with the corresponding, identical observation budget. In other words, competing sota models failed to live up SASHA's performance under a comparable observation budget for high-resolution patches.
A.3.3 Alternative Action Policies during Inference
In the current SASHA implementation, the inference strategy follows a deterministic, greedy policy wherein, for given state, the action with the highest probability is always selected. This means the
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Model
Accuracy
AUC
F1
ACMIL 0.1
0..41
0.828110.344
0.773
DTFD 0.1
0.829#0.013
0.835+00039
0.829#0.013
SASHA 0.1
0.901
0.918-100144
0.856+10031
ACMIL 0.2
0.838
0.773+10.033 0.837+10.000
DTFD 0.2
0.862+1.027
0.878+0.030
0.862:40.027
SASHA 0.2
0.013
0.979-10.008
0.937=00024
Table 10: Performance of ACMIL (rows 1, 4), DTFD (rows 2, 5) and SASHA (rows 3, 6) models under a limited observation budget of 10% (-0.1) or 20% (-0.2) for high-resolution patches per WSI (see text for details). Values represent averages and standard deviations on the CAMELYONIO dataset across 15 model combinations (5 random data splits x 3 random seeds for sampling per split).
agent always pieks the most likely action as per the learned policy distribution during inference (but not during training, see Appendix A.2.3).
We evaluate the effectiveness of this policy over other stochastic policies, similar to the ones used dur- ing learning. Specifically, we introduce more exploration by considering stochastic policy variation, where the agent samples actions from the entire probability distribution over actions given a state.
We explore two different variants top-k sampling and top-p sampling. top-k sampling, where only the top & actions with the highest probabilities are retained as candidates for stochastic selection, and the remaining actions are are masked out (assigned zero probability). The agent then samples from this limited distribution, following renormalization In top-p sampling, actions are sorted in descending order of their probability, and we identify the smallest set of actions such that their cumulative probability exceeds a predefined threshold p. Sampling is then done from this subset, ensuring a balance between diversity and confidence in action selection.
Comparison of different sampling strategies is shown in Table II Interestingly, the default deter- ministic policy of SASHA greedy strategy of selecting the action with the highest probability outperformed all other stochastic policies. This behavior was observed empirically, where the agent consistently selects action with highest probability given current state S,, resulting in more informative state transitions from S. to St+1 and improved classification performance, as shown in Table IT. In addition, the stochastic policy that had the greatest degree of freedom for sampling (Stochastic full distribution) outperformed, by and large, the other policies (top-k and top-p); this was understandable because the latter policies impose more constraints on the action space for sampling.
Inference Policies
Accuracy
AUC
F1
Stochastic- full distribution
0.884
0.846
0.831
Stochastic top-k (A: 3)
0.876
0.826
0.814
Stochastic top-k (k: 5)
0.868
0.823
0.805
Stochastic top-p (p 0.9)
0.876
0.859
0,818
Stochastic top-p (p- 0.8)
0.868
0.851
0.795
Deterministic (SASHA default)
0.953
0.979
0.937
Table 11: Comparison of different stochastic policies (top rows), with SASHA's default deter- ministic policy, during inference. Values denote the performance of the SASHA-0.2 model on the CAMELYON16 dataset. Bold and underlined: best and second best performing policies, respectively.
A.3.4 Alternative Reward Schedules during Training
The default SASHA model employs a step-wise reward during training, where the agent receives a reward at every time step of the episode associated with the label prediction loss at that time step (see Appendix A.2.3). (..2.dix(A.2.3). In contrast, a previous competing model (RLogist (40) employed a terminal reward strategy, providing feedback only at the end of each episode.
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We tested this alternative reward schedule employing only terminal reward at the end of each episode, both during training and inference. As shown Table [2] the RL agent trained with reward at each time step significantly outperformed an agent trained using terminal rewards. We propose that this occurs because our reward formulation which is designed mínimize the classification loss at each time step encourages the model to sample more tumor-relevant patches during inference. Confirming this prediction SASHA's default reward schedule sampled, on average, more patches with high attention (relevance) score, compared to the terminal reward schedule (Table (2)
Top-k attention score patches n RL actions
Avg attention score of RL actions
Reward Schedule
Accuracy
AUC
F1
Last time step (Terminal) Each time step (SASHA default)
0.892
0,941
0.862
15.5
0.0020 0.0032
0.953
0.979
0.937
24.25
Table 12:
Comparison of a terminal reward schedule (row 1) with SASHA's default schedule of reward each time step (row 2), during training (see text for details). Column tells the average patches per slide that are selected by RL agent are in top-k according to the attention score given by HAFED model. Column 6 tells the average attention score of the selected patches by RL agent. (see Fig. -g for details). Values denote the performance of the SASHA-0.2 model on the CAMELYON16 dataset. Bold: the best performing reward schedule.
A.3.5
Analysis of RL Agent Behavior
Tumor Pred. Prob.
Entropy
Hit Ratio
Correctly classifled TS
Mis-classifled TS <
TF 0.1
0.1 <TF 0.6
0.6 STF
1.00
0.75
0.50
0.25
0.00
0.0
0.1
0.0
0.1 0.0 SASHA
0.1 0.0
0.1
1.00
0.75
0.50
0.25
0.00
0.0
0.2 0.0
0.2 0.0
0.2 0.0
(b) SASHA 0.2
Figure 4: Evaluation of model performance on tumor slides from the test set, categorized into four groups. The first three columns correspond to correctly classified tumor slides (TS) by both SASHA and HAFED across increasing tumor fraction (TF) ranges, while the fourth column represents TS misclassified by SASHA but correctly predicted by HAFED. The x-axis denotes the visited patch fraction, binned with a width of 0.005. For each slide, we compute the average entropy, average tumor class prediction probability, and maximum cumulative tumor patch hit ratio (Eq. (1) within each bin. These metrics are then averaged across all slides, with solid lines indicating the mean and shaded regions representing the standard deviation.
To better understand the behavior of the RL agent, we perform two additional analyses.
First, we asked: how quickly does the RL agent visit tumor patches, and how does the model's confidence evolve over time steps in an episode? We first performed this analysis for slides that were correctly classified by SASHA, separately for 3 classes of WSIs with varying tumor fractions (TF, Fig. 4 columns 1-3). For this, we defined cumulative tumor patch Hit Ratio using
Number tumor patches sampled up to time
Hit Ratio(t)= = min (Observation Budget, Total Tumor Patches in WSI)
(L)
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In other words, the cumulative hit ratio begins at 0.0 at the start of each episode and would linearly reach 1.0 at the end of each episode for the most efficient sampling strategy, i.e. if every patch sampled by the agent were tumor patch.
For both variants of the model (SASHA-0.1 and SASHA-0.22, the slides with very sparse tumor regions (TF<0.1), the classifier's 's output favoring the tumor class reaches a high value (~~0.8-1.0) even before it vists 5% of all patches at high-resolution (Fig. $ column 1, green trace). In addition, its predictive entropy drops progressively as more patches are sampled (Fig. 4 column 1, green trace). Surprisingly. even though the peak of the classifier output is reached earlier for SASHA-0. as compared to the SASHA-0.. model, a longer observation budget produces higher accuracies, as well as better calibrated predictions, as shown the main text Table I and Fig. B
As the budget increases from 10% to 20%, the tumor hit ratio improves correspondingly. With a 10% budget, SASHA tends misclassify slides with small tumor regions (see F1 score in Table []. highlighting the importance of increasing the sampling budget. At 20%, SASHA correctly classifies nearly all slides that were correctly predicted by HAFED, demonstrating improved robustness across varying tumor fractions.
As the fraction of tumor patches increases (0. 10. and TF>0.6), the model output favoring the tumor class reaches peak upon visiting progressively miniscule fractions of high-resolution patches (<0.025% or 0.01 %6 Fig. 4 columns 2-3, green traces). The predictive entropy also goes to near zero at these fractions indicating confident model predictions with very few samples.
For Fig. 4 column 2-where the tumor fraction (TF) lies in the range 0.1 S TF < 0.6-the tumor hit ratio increases slightly as the budget increases. However, the improvement minimal because most tumor patches are already captured within the initial 10% of patches selected by the RL. agent. This suggests that for WSIs with moderate tumor presence, visiting just 10% the patches is often sufficient.
Second, we asked: Is the agent able localize tumor regions even in slides that are ultimately misclassified? Answering this question is important, because robust tumor localization ability could potentially assist histopathologists by prioritizing regions for first-pass analysis in clinical or laboratory settings. For this analysis, we considered slide that were misclassified by SASHA but correctly classified by HAFED.
We illustrate this with an example slide Fig. 5 For this slide, the RL agent successfully identifies tumor patches, with the first tumor patch being visited a fraction of 0.0085 and the last 0.03. For the entire cohort of such slides, tumor patch visits ranged from 0.001 - 0. 0.099 fraction for SASHA-0.1 and 0 0.034 fraction for SASHA-0.2 (Fig. 4 column 4, orange trace). In contrast, when patches are randomly sampled for the same slide, the first tumor patch is typically sampled after visiting 0.141 = 0.121 fraction of the patches. This demonstrates that the RL agent able to localize tumor regions, typically with an order of magnitude fewer steps than random sampling.
0
selected by RL Agent
Similar Patches Updated
Figure 5: Visualization of the observation path traced by the RL agent for a WSI. The first image shows the tumor-annotated region in the WSI. The second image illustrates the patches selected by the RL agent from the initial to the terminal time steps. The third image presents the similar patches updated by the TSU model.
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Method
Patch
FE(HR)
FE(LR)
Infer
TSU
RL
Total
DTFD
1.46
116.75
N/A
0.0230
N/A
N/A
116.77
ACMIL
1.46
116.75
N/A
0.0140
N/A
N/A
116.76
HAFED
1.46
116.75
N/A
0.0010
N/A
N/A
116.75
SASHA-0.1
1.46
8.60
4.74
0.0007
2.06
0.105
15.50
SASHA-0.2
1.46
18.23
4.95
0.0007
4.09
0.219
27.49
Table 13: Comparison of inference time for different methods, including the time involved in each computational step. "Patch" denotes the time for dividing the slide into patches, FE denotes Feature Extraction time, with HR representing High Resolution and LR representing Low Resolution, "Infer" refers to the inference time for single model pass. TSU corresponds to the time required for state update, and RL indicates the time needed for agent policy and value updates. The "Total" time (last column) corresponds to the values shown in Br. which does not include the fixed "Patch" time overhead. For SASHA, the value in "Infer." corresponds specifically to the time for one forward pass through the HAFED model.
A.4 Computation of WSI Compressibility, Inference time, and Calibration Error
Whole Slide Images (WSIs) are typically acquired at gigapixel resolution. For laboratories or hospitals maintaining long-term archives, this poses significant storage challenge. However, we propose that, for accurate tumor diagnosis, not necessary to store WSIs as raw images. By storing them as informative feature representations, considerable reduction in storage requirements can be achieved.
To quantify the data reduction achieved during WSI processing, we define the WSI compressibility factor as the ratio between the original image size at full magnification and the size of the final feature representation used for classification. We define the raw image size W X H 3, representing the width, height, and RGB color channels, respectively.
Models such ACMIL, DTFD, and HAFED operate high-resolution feature tensors U Nxxxd where N is the number of low-resolution patches, & (16) is the number of sub-patches in high-resolution per low-resolution patch, and (384) is the feature dimension. In contrast, SASHA operates on an initial low-resolution feature set E R.N and iteratively refines it by zooming into diagnostically relevant patches. The final state representation S7 R xd can be archived compact summary of the WSI, requiring approximately 16 times less storage compared to methods like ACMIL, DTFD, or HAFED, Fig. Bd.
The compressibility factor defined as:
Image Size
WXHx3
for ACMIL, DTFD, HAFED for SASHA
N Nxxxd
Feature Space
WXX Nxd
Empirically, the compressibility factor is ~ 5 x 10s for ACMIL, DTFD, and HAFED, and 10 for SASHA-0.1 and SASHA-0.2. This indicates that SASHA methods yield significantly more compact feature representations for classification tasks.
Inference time computation: As shown in Figure 3r, SASHA, with its selective sampling strategy, achieves the lowest inference time compared models such ACMIL, DTFD, and HAFED, which sample all the patches at high resolution. We report the average inference time per slide for the CAMELYON 16 test dataset. The inference time reported Fig. 3 includes all steps: pre- pre-processing feature extraction, and classification. Table [13 shows the inference time involved in each step. The column "Patch" refers to the time required to divide the WSI low resolution into non-overlapping 256x256 patches, while also removing background content such as regions with only the glass slide or insufficient biological tissue. This step a constant overhead across all methods and is not included in the Total time in Table [3] Feature extraction of high-resolution patches reflects major inference time bottleneck. Because SASHA visits only small fraction (10-20%) patches at high resolution, this yields considerable savings over other approaches that visit all (100%) of patches at high resolution, like DTFD, ACMIL,, HAFED. addition to feature extraction, the TSU is secondary bottleneck in SASHA because at each timestep. the cosine similarity with all other
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non-masked patches must be computed, albeit at low resolution. Improving the speed of this step is scope for future work.
Expected Calibration
Error (ECE): Expected Calibration Error (ECE) is metric used to quantify how well neural network's predicted probabilities align with the true probabilities (or observed frequencies) of its predictions (II). It measures how reliable a model's confidence levels are in its predictions. A perfectly calibrated model would have an ECE of O, indicating that the model's confidence scores accurately reflect the true likelihood of its predictions being correct.
To compute ECE, we first divide the predicted confidence scores (i.e., the maximum softmax probabilities) into M = 10 equally spaced bins. For each bin, we calculate two quantities: i) Accuracy: the proportion of correctly classified samples in the bin; and ii) Confidence: the average of the predicted probabilities in the bin. These metrics are then used to compute the calibration error as the absolute difference between its accuracy and confidence for each bin, summed across bins (11].
HAFED achieves the lowest Expected Calibration Error (ECE) of approximately 0.015, indicating highly calibrated predictions. In contrast, ACMIL and SASHA-0. exhibit the highest ECE values, around 0.06. SASHA-0.2 achieves an intermediate ECE of around 0.03, which is still lower than that of ACMIL and DTFD. This suggests that SASHA-0.2 not only matches HAFED in terms of classification accuracy but also performs better than other sota methods in terms of calibrated predictions.
A.5 Hardware
For feature extraction and model inference, we utilized an NVIDIA GeForce GTX 1080 Ti GPU with 12 GB of memory. Feature extraction was carried out with a batch size of 512. Training for HAFED and TSU was also performed on the same GPU. To accelerate the training of the RL agent described we employed an NVIDIA Tesla V-100 GPU with 32 GB of memory.
in Section 3.4
A.6 Limitations and Extensions
Training time cost A key limitation of our method is that, despite being RL-based, it performs feature extraction all high-resolution patches during training. To reduce training time, future improvements would involve developing an end-to-end pipeline that trains the RL agent concurrently with the HAFED and the TSU modules. In such model, the policy network decides the specific low-resolution patch to sample in real-time, followed by HAFED feature distillation for the zoomed-in patch, and TSU state update for the entire slide, with all steps being trained end-to-ent with reward at each time step. This would be followed by the additional steps of training the policy and value networks, and on, until the end of each episode, for several episodes. This mimics how RL agents learn, conventionally, in novel environments with sporadic rewards. With this method, there would be no need to sample every patch high resolution during training. Yet, key challenges with stable, training of multiple modules concurrently may need to be addressed.
Fixed sampling budget- In our current setup, employ fixed sampling budget per slide (e.g.. 10% or 20%). However, as shown in Figure 4 we observe that for certain slides, the classifier can become confident in its prediction fairly early in the sampling process, rendering further sampling unnecessary. This suggests that a dynamic budget allocation strategy guided by the classifier's s confidence could further reduce inference time without compromising performance.
Architecture overhead and parameter scaling- We compare the total number model param- eters excludinn the ViT-based feature extractor (see Appendix A.2.T] -across different methods. UTFD, and HAFED, all of which operate on high-resolution inputs, are relatively lightweight with approximately 82.56K. 84.37K, and 300K parameters, respectively. In contrast, the SASHA pipeline consists of three distinct modules: HAFED, the Target State Updater (TSU), and the Reinforcement Learning (RL) agent, with parameter counts of 300K, 1. and 296K, respec- to approximately 1.7M parameters. This represents a 3.5x increase in parameters compared to HAFED and a 21x increase relative to DTFD and ACMIL.
Filtering normal patches WSIs typically contain far more normal patches, as compared to tumor patches. This imbalance becomes particularly problematic when the tumor occupies only small fraction of the slide. In such cases, the RI. agent is more likely to encounter and select normal patches- especially in the early steps leading to inefficient exploration and increased risk of
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miss. The sparse distribution of tumor regions makes it difficult for the agent to consistently focus on diagnostically relevant areas within a limited sampling budget. A more efficient strategy would involve the early filtering of normal, redundant, concentrate its computational efforts on a smaller, more informative subset of patches. This could improve both performance and inference efficiency.
uninformative patches, allowing the agent to
Our method is particularly suited for deployment in clinical settings, from two key standpoints Explainability and Calibration. First, clinicians are likely to trust decisions made by AI agents only if these decisions can be readily and rationally explained. Our RL agent consistently selects high-attention tumor-rich patches, as compared to normal patches. Moreover, this selection occurs far more frequently than that of random policy. These selection strategies make the decisions of the RL agent intuitive and easy to explain to clinicians (Section 4.5)
Incorporation into clinical worktlows-
Second, clinicians are also likely to benefit from reliable "confidence" score from the Al model, to know when they can, and cannot, trust its predictions. We showed that the expected calibration error (ECE) for SASHA=0.2 2 were consistently lower than those of competing sota attention- baaod models like ACMIL and DTFD (Section 4.6). With well-calibrated models (lower ECE), the model's confidence in its predictions (e.g.. predictive uncertainty) is reliable. In this case, it becomes a useful indicator for when referral to a clinician or expert is warranted. In other words, by quantifying predictive uncertainty of the SASHA-0.2 classifier with established approaches (e.g., predictive entropy), our RL model could provide clinicians with valuable additional information about when its prediction can (or cannot) be trusted. Finally, inference time accuracy may depend on the extent of domain shift between the test and training clinical samples; developing approaches that combine domain adaptation with efficient sampling may be relevant to address this challenge.
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Reviews:
[{"review_id": "uCrZkP4ILv", "reviewer": "Reviewer_foU5", "summary": "This paper introduces Sequential Attention-based Sampling for Histopathological Analysis (SASHA), a deep reinforcement learning (RL) framework that selectively analyzes diagnostically relevant high-resolution regions in gigapixel whole-slide images (WSIs). SASHA achieves performance comparable to state-of-the-art full-resolution models while utilizing only 10–20% of the high-resolution patches.", "questions": "See the Weaknesses.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**:\n\n(1) The proposed approach closely mimics practical diagnostic workflows—initial low-resolution scanning followed by selective high-resolution inspection.\n\n (2) The paper is well-written, clearly structured, and easy to follow.\n\n (3) Experimental results on two benchmark datasets demonstrate that the proposed method outperforms existing approaches while using only 10–20% of the high-resolution patches.\n\n (4) Qualitative visualizations further support the effectiveness of the proposed sampling strategy.\n\n**Weaknesses**:\n\n(1) Training Cost: The method requires access to all high-resolution patches during training, which contradicts its low-resource inference objective and significantly increases training time and memory usage.\n\n (2) Inference Time Reporting: While Section 4.3 provides a general analysis of inference efficiency, the paper would benefit from more detailed, side-by-side comparisons of actual inference times across methods.\n\n (3) Clinical Deployment Considerations: Although the approach is promising, the paper lacks discussion on practical deployment challenges in clinical settings, such as interpretability, robustness to failure, and integration into existing diagnostic workflows.\n\n (4) TSU Similarity Threshold (τ): The threshold parameter τ plays a key role in the targeted state update mechanism, yet no sensitivity analysis or justification is provided for its choice.", "was_revised": true, "final_justification": "The experiments included in the rebuttal have satisfactorily addressed some of the issues I raised, thus I will uphold my existing rating.", "rebuttal": "We thank the reviewer for these valuable suggestions, and for the positive review of our methods, writing, evaluation and visualizations. We address each question, below.\n&nbsp;\n\n## 1) Training Cost\n\n> method requires access to all high-resolution patches during training\n\n**Substantial savings during inference**\n* Our approach is similar in training cost to existing state-of-the-art models like ABMIL [1], ACMIL [2], DTFD [3] etc, which require access to all high-resolution patches, and perform feature extraction on them, during training. Indeed we acknowledge this limitation in Section 5.  \n* Yet, where we differ -- and a major advantage of our approach -- is our low-resource inference objective: access to all high-resolution patches is not required at inference time. This makes our inference faster by 4x-8x -- depending on the observation budget -- a substantial savings, without compromising performance.\n\n**Stable training of the RL agent**\n* We train and freeze the feature extraction (HAFED) and state-update (TSU) mechanism before training the RL agent. \n* An advantage of such an approach is that it stabilizes the training of the RL agent, because these other modules do not have to train concurrently with the policy and value networks.\n \n**Value in low-resource settings**\n* In its current form, the algorithm still finds significant value in low-resource settings. \n* By training the full model in high-resource settings, and fine-tuning HAFED and TSU with a limited number of OOD (out-of-distribution) WSI images, we can run the more efficient RL inference engine on unlabeled WSIs in low-resource settings.\n\n**End-to-end pipeline for RL training**\n* To reduce training time, future improvements would involve developing an end-to-end pipeline that trains the RL agent concurrently with the HAFED and the TSU modules. \n* In such a model, the policy network decides the specific low-resolution patch to sample in real time, followed by HAFED feature distillation for the zoomed-in patch, and TSU state update for the entire slide, with all steps being trained end to end with reward at each time step. \n* This would be followed by the additional steps of training the policy and value networks, and so on, until the end of each episode, for several episodes. \n* With this method, there would be no need to sample every patch at high-resolution during training, and would align with how RL agents learn, conventionally, in novel environments with sporadic rewards [4]. \n\n**Caveats** \n* Yet, in this case, there could be challenges associated with stable training of the RL agent (the policy and value networks), along with the HAFED and TSU networks. \n* We seek to overcome these challenges through alternating or sequential minimization of the loss functions associated with each of these components [5], thereby training all components in a coordinated manner; this is scope for future work.\n\nWe will discuss this scope for improvement in detail in the revision, in Appendix A.6 (Limitations).\n\n*** \n&nbsp;\n\n## 2) Inference Time Reporting: \n\n> detailed, side-by-side comparisons of actual inference times across methods\n\n**Detailed inference time comparisons**\n* We have provided below a detailed table of inference times (in seconds) for the different methods, including the break up for the different components of SASHA. \n* Our method achieves a ~4-8x speedup in total inference time. \n```\nMethod    |Patch | FE(HR) | FE(LR) | Infer. | TSU  |  RL   |  Total\nDTFD      | 1.46 | 116.75 |   N/A  | 0.0230 |  N/A |  N/A  | 116.77\nACMIL     | 1.46 | 116.75 |   N/A  | 0.0140 |  N/A |  N/A  | 116.76 \nHAFED     | 1.46 | 116.75 |   N/A  | 0.0010 |  N/A |  N/A  | 116.75\nSASHA-0.1 | 1.46 |   8.60 |  4.74  | 0.0007 | 2.06 | 0.105 |  15.50\nSASHA-0.2 | 1.46 |  18.23 |  4.95  | 0.0007 | 4.09 | 0.219 |  27.49\n```\n\n*Table Caption* \n* FE: Feature Extraction | HR: High Resolution | LR: Low Resolution | Infer.: Inference time for one model pass | TSU: Time for state update | RL: Time for policy and value update\n* “Total” time (last column) corresponds to the values shown in Fig. 3c. of the paper. \n* “Patch” refers to time required for dividing the WSI at low resolution into non-overlapping 256x256 patches, while also removing background content (e.g. portions with the glass slide alone, or with insufficient biological tissue). \n* This is a constant overhead for all methods, and is not included in the Total time.\n* For SASHA, “Infer.” corresponds to time for one forward pass through the HAFED model.\n\n**Conclusions**\n* The main cost saving at inference time is with feature extraction for the high-resolution patches. \n* Because SASHA visits only a small fraction (10-20%) of patches at high resolution, this yields considerable savings over other approaches that visit all (100% of) patches at high resolution. \n* In addition to the feature extraction, the TSU is a secondary bottleneck in SASHA because at each time step the cosine similarity with all other non-masked patches must be computed, albeit at low-resolution. \n* We are working toward making this component more computationally efficient, for example, by precomputing the pairwise similarities and storing them in a lookup table. \n\nWe will discuss this approach in the revision, and seek to incorporate it into future extensions of our method.\n\n*** \n&nbsp;\n\n## 3) Clinical Deployment Considerations:\n\n> lacks discussion on practical deployment challenges in clinical settings, \n\nWith regard to clinical workflows [6] and utility for real-world deployment we discuss two cornerstones features -- Explainability and Calibration -- in Sections 4.5-4.6. \n\n**Explainability -- easy to explain RL agent’s strategies for clinicians**\n* In Section 4.5, we show that: *“tumor fraction in patches sampled by the RL agent at high resolution (10% with SASHA-0.1 or 20% with SASHA-0.2) significantly exceeds that in a corresponding number of randomly chosen patches, among those not sampled by the agent (Fig. 3e, p<0.001). Moreover, the RL policy guides the agent to preferentially samples patches afforded a high attention score by sota models: both the average attention score (Fig. 3g, p<0.001) and the fraction of top-k patches sampled under the RL policy were statistically significantly greater than the fraction of patches sampled by a random policy (Fig. 3f, p<0.001).”*\n* In other words, our RL agent consistently selects high-attention, tumor-rich patches far more often than a random policy, making its decisions intuitive and easy to explain to clinicians.\n\n**Calibration -- useful indicator for referral to a clinician specialist**\n* In Section 4.6 we show that: *“sampling a higher fraction of patches yields better calibrated predictions… We quantified the expected calibration error (ECE) [10] as a measure of the degree of calibration of the models. The ECE decreased systematically as the proportion of sampled patches increased from 10% (SASHA-0.1) to 20% (SASHA-0.2) to 100% (HAFED). This pattern occurred possibly because providing the model more timesteps to sample the WSI in each RL episode enabled the model to recover from sub-optimal sampling actions in the early\ntimesteps, and to sample the relevant portions of the WSI in the later timesteps.”* \n* Moreover, SASHA-0.2 ECEs (=0.0292) were lower than those of competing sota attention-based models like ACMIL (0.0617) and DTFD (0.0452), as shown in Fig. 3h. \n* A lower ECE reflects a better calibrated model. With well calibrated models, the model’s confidence in its prediction (e.g., predictive uncertainty) is accurate. In this case, it becomes a useful indicator for when referral to a clinician specialist is warranted. \n* In other words, by quantifying predictive uncertainty of the SASHA-0.2 classifier with established approaches (e.g. predictive entropy), our RL model could provide clinicians with a valuable preview of when its prediction can (or cannot) be trusted. \n\nWe will include this discussion on incorporating our method into clinical workflows in more detail in the revised paper.\n\n*** \n&nbsp;\n\n## 4) TSU Similarity Threshold: \n\n> The threshold parameter τ … no sensitivity analysis or justification is provided for its choice.\n\nWe regret the lack of clarity. \n\n**τ was tuned as a hyperparameter**\n* As shown in Table 6, and related text in Appendix A.2.2, the TSU similarity threshold (τ) was tuned as a hyperparameter with a validation set (details in Table 3 of Appendix A.1) to maximize the sum of AUC and F1 scores. \n* We paste below the relevant rows of the table:\n* Table 6 in the Appendix also provides a full list of model hyperparameters, selection criteria and the different ranges explored and values selected for the two cancer benchmarks.\n\n```\nHyperparam   | Sel. crit. |      CAMELYON16      |       TCGA           \n                          |   Range   |  Values  |   Range  |  Values \nThreshold (τ)| Validation | 0.88-0.98 | 0.9/0.95 | 0.8-0.92 | 0.9/0.9\n```\n\n*Caption:* Hyperparameters for the CAMELYON16 and TCGA-NSCLC datasets. In the Values column, entries separated by a slash (/) represent values for SASHA-0.1 and SASHA-0.2 models respectively.\n\n***\n&nbsp;\n\n*References*\n\n[1]  Ilse, et al, ICML, 2018\n\n[2] Zhang, et al, ECCV, 2024\n\n[3] Zhang, et al, IEEE/CVF, 2022\n\n[4] Mnih et al, Nature, 2015\n\n[5] Choromanska et al, ICML, 2019\n\n[6] Pantanowitz et al, Lancet Dig Health, 2020"}, {"review_id": "7yA8YfNe4v", "reviewer": "Reviewer_tugf", "summary": "The authors introduces SASHA, a novel deep RL framework designed for the efficient diagnosis of gigapixel-sized whole-slide images (WSIs) in histopathology. SASHA uses an RL agent that intelligently samples a small fraction (10-20%) of informative patches from a low-resolution view of the WSI to \"zoom in\" on for high-resolution analysis. It consists of several key modules: \n\nHierarchical Attention-based Feature Distiller (HAFED): A 2-stage attention model that learns to extract diagnostically relevant features from high-resolution patches for aggregation into both a set-based representation (for updating state via TSU) as well as slide-level representation (for classification).\n\nTargeted State Updater (TSU): An efficient method to update the representation of the WSI. After a patch is selected and analyzed at high resolution, the TSU updates the state of not only the selected patch but also other, un-sampled patches that are highly correlated with it, supervised by their ground truth features during training time.\n\nVia 2 WSI-classification benchmarks, the paper demonstrates that SASHA achieves classification performance comparable to state-of-the-art methods that analyze the entire WSI at high resolution, but does so at a fraction of the computational cost, with up to 8x faster inference times and requiring significantly less memory.", "questions": "I recommend expanding the scope of evaluation to at least 2 - 3 other benchmarks, including more challenging classification tasks (e.g. BRACs or EBRAINS), as well as prognostic tasks (e.g. TCGA or SurGen). Comparison with non-RL based sampling methods such as ZoomMIL should also be added.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\nThe method is well thought out and motivated, maximizing the learning signal from multiple resolution while allowing computationally efficient inference after training. \n\nWeakness\nExperiments is lacking with only 2 datasets that are focused on binary diagnosis (e.g. no prognosis tasks or more challenging large-scale classification tasks) and are already near saturation in performance, making it difficult to assess potential performance impact across diverse tasks in computational pathology. The norm in the field for MIL-based methods is usually 5+ benchmarks, in addition to potential few-shot / ablation experiments. \n\nThere's also no comparison with non-RL-based sampling methods such as ZoomMIL.", "was_revised": true, "final_justification": "My concerns have been addressed. Raised scores.", "rebuttal": "We thank the reviewer for the important suggestions, and for evaluating our method as well-motivated and computationally efficient at inference time. We address the suggestions below.  \n&nbsp;\n\n## 1) Expanding the scope, including more challenging classification\n\n> recommend expanding the scope of evaluation to at least 2 - 3 other benchmarks, including more challenging classification tasks\n\nPer the reviewer’s suggestion, we have now evaluated two other benchmarks, Camelyon-Plus and the BRACS dataset, each of which is a multi-class classification problem.\n\n**CamelyonPlus 4-class classification**\n* First, we employed the curated CamelyonPlus dataset [1]. \n* This dataset comprises 1,347 WSIs from Camlelyon-16 and Camlelyon-17 datasets after removing low-quality slides and upgrading the task to a four-class classification: negative, micro-metastasis, macro-metastasis, and Isolated Tumor Cells (ITC). \n* The following are the train/validation/test splits (numbers indicate WSI counts). We trained the model with the pooled datasets and report the performance separately for the Camelyon-16 and Camelyon-17 test WSIs.\n```\n         |  CAMELYON16+(4-class) |  CAMELYON17+(4-class) | \n         | Neg | Mic | Mac | ITC | Neg | Mic | Mac | ITC |\nTrain    | 172 | 45  | 51  | 6   | 436 | 77  | 123 | 30  |\nVal      | 30  | 12  | 9   | 1   | 100 | 14  | 29  | 8   |\t\t\nTest     | 35  | 14  | 9   | 1   | 96  | 12  | 29  | 8   |\n```\n\n* We observed performance metrics that are on par with sota methods like DTFD and ACMIL (table). We will add these results to those in Table 1.\n```\nMethod      |     CAMELYON16+(4-class) |    CAMELYON17+(4-class)   | \n            |  Acc   |  AUC   |   F1   |   Acc   |   AUC  |   F1   | \nDTFD        |  0.918 |  0.947 |  0.904 |  0.896  |  0.903 |  0.891 | \nACMIL       |  0.962 |  0.925 |  0.965 |  0.826  |  0.932 |  0.827 | \nHAFED       |  0.963 |  0.911 |  0.949 |  0.883  |  0.931 |  0.868 | \nSASHA-0.1   |  0.856 |  0.880 |  0.801 |  0.869  |  0.914 |  0.841 | \nSASHA-0.2   |  0.939 |  0.937 |  0.894 |  0.889  |  0.921 |  0.864 |\n```\n\n**BRACS multi-class classification**\n* For the BRACS (BReAst Carcinoma Subtyping) dataset, we employed the public dataset available at [2]. \n* This dataset comprises 547 labeled WSIs, and involves a 7-class classification that characterizes the tumor as one of 3 benign (BT), 2 atypical (AT), or 2 malignant (MT) sub-types. \n* We grouped the sub-types within each class into its respective “uber”-class and excluded the “normal” sub-type in the BT class (Group_BT/Type_N), as well as 7/503 slides that did not include all relevant levels of magnification.\n* The final distribution of train/validation/test WSIs is as follows:\n```\n         |         BRACS (3-class)          |\n         |  Benign  |  Atypical | Malignant | \nTrain    |    171   |    52    |    135     | \nVal      |     16   |    14    |     21     | \nTest     |     25   |    23    |     16     | \n```\n\n* We performed a 3-class classification among BT, AT and MT classes and compared the performance of SASHA-0.2 with the other sota models. \n```\nMethod      |         BRACS (3-class)        |   \n            | Accuracy  |   AUC    |   F1    | \nDTFD        |   0.609   |   0.796  |  0.609  | \nACMIL       |   0.547   |   0.750  |  0.522  | \nHAFED       |   0.593   |   0.756  |  0.559  | \nSASHA-0.2   |   0.609   |   0.755  |  0.589  | \n```\n* SASHA, even with sampling 20% of the patches, performs comparably with DTFD, and outperforms ACMIL, each of which samples 100% of patches at high resolution.\n* The lower F1 score compared to sota (0.695, Brancati & Frucci, Information, 2024), could be because we used the intermediate 5x→20x zoom levels for SASHA, whereas, conventional models have employed the highest zoom levels (40x). \n* In future work, we will perform more extensive hyperparameter searches, extend this classification to all the different subtypes (2 per class) as well as normal, and evaluate our model on this 7-class classification problem.\n\n***\n&nbsp;\n\n## 2) Comparison with non-RL based methods\n\n> Comparison with non-RL based sampling methods such as ZoomMIL should also be added.\n\n**Differences between SASHA and ZoomMIL**\n* There are comparatively few non-RL-based sampling methods for histopathology. We thank the reviewer for the pointer to ZoomMIL [3]\n* ZoomMIL uses a rational strategy of zooming into patches recursively, at different levels of magnification, so that all patches need not be sampled at high resolution, thereby providing significant speedups.\n* In ZoomMIL RoI selection for zooming is performed with a gated attention and a differentiable top-K module, and not with an RL agent, like SASHA.\n* One similarity is that our RL agent also chose to sample and zoom into patches afforded a high attention score by conventional methods as shown in Fig. 3f (Top-k overlap) and Fig. 3g (Avg. Attention Score).\n* Yet, the RL policy does not exclusively sample the Top-k attention patches and, hence, the two policies are not identical.  \n* Also, while ZoomMIL samples a fixed budget of K patches at each resolution, SASHA samples a fixed proportion of samples at high resolution (0.1 or 0.2).\n* Their relative performances must be compared empirically.\n\n**Performance comparison of SASHA with ZoomMIL**\n* We ran ZoomMIL with the Camelyon-Plus benchmark using the 4-class classification, in two stages: \n\n*Histopathology-tuned ViT feature extractor*\n* First, we re-trained the ZoomMIL model using the histopathology-tuned ViT feature extractor [4] for the **10x→20x** zoom levels (top-K=300), for the Camelyon-16 dataset. \n* This encoder change improved upon the results reported in the original study, based on the ResNet50 encoder, by ~6-7%.\n```\nMethod              |    CAMELYON16 (2-class)   |\n                    |  Accuracy |  Weighted-F1  |\nZoomMIL (ResNet50)  |   0.842   |     0.833     |\nZoomMIL (ViT-histo) |   0.906   |     0.905     |\n```\n\n*Comparison with SASHA & HAFED*\n* Second, we compared ZoomMIL with SASHA-0.1, SASHA-0.2 and HAFED for evaluation of two different datasets -- Camelyon-16 and Camelyon-17. Models were trained together using the entire CamelyonPlus database. \n* We report the results for the **5x→20x** zoom levels for SASHA and ZoomMIL, but using the histopathology-tuned ViT feature extractor for all methods.\n* Moreover, SASHA-0.1 and SASHA-0.2 sample on average 84+/-62 (mean +/- std, median=68) and 168+/-125 (median=136) patches respectively, based on the observation budget. Therefore, we run ZoomMIL for 2 different values of top-K -- K=80 and 160 -- for a fair comparison with SASHA-0.1 and -0.2, respectively.\n* We also run ZoomMIL with K=300 for the Camelyon-16 dataset,  as per the original study\n```\nMethod             |  CAMELYON16+(4-class)  |  CAMELYON17+(4-class) | \n                   |  Acc  |  AUC  |   F1   |  Acc |  AUC  |   F1   | \nZoomMIL(ViT, K=80) | 0.706 | 0.841 |  0.710 | 0.841| 0.885 |  0.812 | \nZoomMIL(ViT, K=160)| 0.672 | 0.852 |  0.683 | 0.827| 0.860 |  0.792 |\nZoomMIL(ViT, K=300)| 0.741 | 0.852 |  0.688 | 0.820| 0.859 |  0.790 |\nHAFED              | 0.963 | 0.911 |  0.949 | 0.883| 0.931 |  0.868 | \nSASHA-0.1          | 0.856 | 0.880 |  0.801 | 0.869| 0.914 |  0.841 | \nSASHA-0.2          | 0.939 | 0.937 |  0.894 | 0.889| 0.921 |  0.864 |\n```\n* SASHA-0.1 and -0.2 outperform ZoomMIL -- across all top-K values -- by ~5-20% for both Camelyon-16 and Camelyon-17. \n* This difference may be due to the choice of 5x→20x zoom levels. Attention scores -- which ZoomMIL uses for magnification -- were perhaps less reliably computed at the 5x zoom level than at the 10x level, used in their paper.\n* Nevertheless, zooming between the *same 2 levels* (5x→20x) SASHA outperformed ZoomMIL. \n* We will perform more extensive hyperparameter searches to validate these findings before reporting them in the paper.\n\n***\n&nbsp;\n\n## 3) Ablation experiments\n\n> norm in the field … is usually 5+ benchmarks, in addition to potential few-shot / ablation experiments. \n\n**Detailed ablation experiments are reported in the paper**\n* In the paper, Section 4.4 and Appendix A.3, we already conducted full-fledged ablation experiments by removing or replacing each component of the SASHA model -- including the feature extractor, classifier (HAFED), state-updater (TSU) and RL agent policy. \n* We describe these in the paper as follows: *”We performed a systematic ablation study removing each component of the model in turn – or replacing it with a naive variant – and evaluating it with the CAMELYON16 dataset. These included: a) replacing the WSI-pretrained ViT with a ResNet50, used commonly for feature extraction [37, 36], b) replacing multi-branch attention with single branch in the Classifer [14], c) replacing the targeted state update policy with a global state update [36], d) removing the TSU and updating the state only of the sampled (local, high-resolution) patch, and e) and selecting an action based on a random policy (see “Variant”s in Table 2). In every case, we observed sharp drops in accuracy, ranging from 7-45%, relative to the baseline SASHA model... (Table 2). The strongest impact occurred upon changing the RL policy (Table 2, e), with the next most impactful effects occurring upon changing the feature extractor (a) or the state update method (c-d). The least impact occurred upon changing the classifier to single branch attention (b), but even here, the F1 score dropped by 14% points. Additional ablation experiments, and including exploring the effect of terminal versus intermediate reward during RL agent training, adopting a stochastic action selection policy are described in Appendix A.3.\"* \n* These results indicate that every component of our model was critical for achieving high performance, comparable to sota models.\n\n***\n&nbsp;\n\n*References*\n\n[1] Ling, et al, Science Data Bank, 2025\n\n[2] Brancati, et al, Database, 2022\n\n[3] Thandiackal, et al, ECCV, 2022\n\n[4] Kang, et al, CVPR, 2023"}, {"review_id": "mqo3L8AP78", "reviewer": "Reviewer_jTur", "summary": "The paper presents SASHA, a reinforcement learning-based framework for the efficient classification of gigapixel whole-slide histopathology images (WSI). Unlike traditional methods, that process the entire high-resolution slide, SASHA learns to adaptively sample a small subset (only 10/20%) of high-resolution patches while still achieving state-of-the-art diagnostic classification accuracy.\n\nThe framework consists of three main components:\n1)Hierarchical Attention-based Feature Distiller (HAFED): a two-stage multi-head attention MIL module that extracts label-aware feature representations from WSI high-resolution patches and aggregates them for slide-level classification.\n2)Targeted State Updater (TSU): a network that maintains a global state of the WSI with a d-dimensional feature vector for each low-resolution patch. When a patch is zoomed in to high resolution, the state is updated selectively only for patches with features correlated to the sampled one.\n3)Deep RL agent: a policy network that, given the current WSI state (low-res features and any updated high-res embeddings), sequentially selects the next patch to zoom, aiming to maximize classification accuracy.\n\nOn CAMELYON16 and TCGA-NSCLC benchmarks, SASHA matches or exceeds full-slide MIL baselines (e.g., ACMIL, DTFD) using only 20 % of patches, reduces inference time by 4–8×, and cuts memory use by 16×. Extensive ablation studies confirm the importance of each component of the framework, and explainability analyses show that the agent focuses on tumor-rich, highly informative regions of the WSI.", "questions": "1) TSU input clarification: does the MLP of TSU receive [S(i), S(a_t), V(a_t)] (as per Fig. 2b & Appendix) or only [S(i), V(a_t)] (as per Sec. 3.3)?\n2) Definition of “time fraction”: in Fig. 3a and 3b, the patch boxes are colored according to the “time fraction in episode.” However, the precise meaning of this term is not defined in the text. Is it meant to indicate the normalized timestep at which each patch was visited within an RL episode? Clarifying this would aid interpretability.\n3) Multi-class extension: have you attempted (or can you comment on) applying SASHA to tasks with > 2 labels, such as tumor grading or multi-label subtype classification?\n4) Pretraining sensitivity: In your ablations, replacing the WSI-pretrained ViT with an ImageNet-pretrained ResNet50 caused a substantial drop. Could you discuss how robust is performance if the ViT encoder is pretrained on smaller or out-of-domain datasets?", "limitations": "none relevant", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1) Real-world motivation: the challenge of computationally efficient yet accurate WSI analysis is central to digital pathology. The paper tackles the inefficiencies of full-resolution WSI analysis and prior sparse sampling methods.\n\n2)Novelty of the architecture: combining label-aware MIL (multi-head HAFED), targeted state propagation (TSU), and on-policy RL into a coherent framework is innovative and well justified.\n\n3)Strong empirical results: Under the same observation budget, SASHA consistently outperforms RLogist by a notable margin (e.g., +9% AUC at 10% sampling). Moreover, SASHA-0.2 achieves near-SOTA performance using only ~20% of high-res patches, and HAFED (100%) is competitive with top full-slide MIL models like DTFD and ACMIL. In addition to its strong performance, the framework offers significant gains in efficiency, with an inference time reduced by up to 8× and memory usage by over 16×.\n\n4)Each component of the framework is well justified: removing any key component such as multi-head attention of HAFED, targeted update mechanism of TSU, or replacing the learned policy with a random one—leads to substantial drops in performance, clearly justifying each design decision.\n\n\nWeaknesses\n1) Training time cost: although the framework is efficient at inference, training remains costly and less scalable. It requires access to all high-resolution patches and involves computing high-res embeddings V(i) for all patches within each correlated set C, as part of TSU’s training. This contradicts the efficiency objective of avoiding exhaustive high-resolution patch processing (l.195), a trade-off that is acknowledged but not extensively examined.\n\n2)Generalization beyond binary classification: the evaluation is limited to binary classification tasks. While this is common in the literature, it remains unclear how SASHA would perform in multi-class or multi-label settings common in real-world pathology.\n\n3)Minor presentation inconsistency: there’s a discrepancy between Section 3.3 and Figure 2b regarding the TSU input. The figure and appendix suggest that S(i), S(a_t), and V(a_t) are concatenated, whereas the text says only S(i) and V(a_t) are used.", "was_revised": true, "final_justification": "The paper is solid and technical sound. Experiments are good and the novelty of the proposal is significant.\nAuthors provide a good and solid rebuttal indeed.", "rebuttal": "We thank the reviewer for the valuable suggestions and for the positive comments on our motivation, novelty, strength of evaluation and justification for the model components. We address the questions below.  \n&nbsp;\n\n## 1) TSU input clarification\n\n> there’s a discrepancy between Section 3.3 and Figure 2b\n\n**Typo corrected**\n* Thank you for identifying this typo. As shown in the figure and clarified in the Appendix: *“For each patch a_τ that crosses the similarity threshold, the TSU model takes as input a concatenated vector comprising (i) its low-resolution features Z_aτ ∈ R^d, (ii) the low-resolution feature representation of the selected patch at, Z_at ∈ R^d and (iii) the high-resolution feature representation of the selected patch V_at ∈ R^d.”* \n* We will make the description of the TSU in Section 3.3 consistent with Fig. 2b in the main text.\n\n*** \n&nbsp;\n\n## 2) Definition of “time fraction”\n\n> the precise meaning of this term is not defined \n\n**Definition added**\n* We regret the lack of clarity. As the reviewer correctly points out it is the normalized timestep (timestep/length of episode) at which each patch was visited by the RL agent, within an episode. \n* We will clarify this in the revised manuscript.\n\n*** \n&nbsp;\n\n## 3) Multi-class extension\n\n> have you attempted (or can you comment on) applying SASHA to tasks with > 2 labels\n\nPer the reviewer’s suggestion, we have now evaluated two other benchmarks, Camelyon-plus and the BRACs dataset, each of which reflects a multi-class classification problem.\n\n**CamelyonPlus 4-class classification**\n* First, we employed the curated CamelyonPlus dataset [1]. \n* This dataset comprises 1,347 WSIs with labels from curated Camlelyon-16 and Camlelyon-17 datasets after removing low-quality slides and upgrading the task to a four-class classification task with the following labels: negative, micro-metastasis, macro-metastasis, and Isolated Tumor Cells (ITC). \n* The following are the train/validation/test splits (numbers indicate WSI counts). We trained the model with the pooled datasets and report the performance separately for the Camelyon-16 and Camelyon-17 test WSIs.\n```\n         |  CAMELYON16+(4-class) |  CAMELYON17+(4-class) | \n         | Neg | Mic | Mac | ITC | Neg | Mic | Mac | ITC |\nTrain    | 172 | 45  | 51  | 6   | 436 | 77  | 123 | 30  |\nVal      | 30  | 12  | 9   | 1   | 100 | 14  | 29  | 8   |\t\t\nTest     | 35  | 14  | 9   | 1   | 96  | 12  | 29  | 8   |\n```\n\n* We observed performance metrics that are on par with sota methods like DTFD and ACMIL (table below). We will add these results to those in Table 1.\n```\nMethod      |     CAMELYON16+(4-class) |    CAMELYON17+(4-class)   | \n            |  Acc   |  AUC   |   F1   |   Acc   |   AUC  |   F1   | \nDTFD        |  0.918 |  0.947 |  0.904 |  0.896  |  0.903 |  0.891 | \nACMIL       |  0.962 |  0.925 |  0.965 |  0.826  |  0.932 |  0.827 | \nHAFED       |  0.963 |  0.911 |  0.949 |  0.883  |  0.931 |  0.868 | \nSASHA-0.1   |  0.856 |  0.880 |  0.801 |  0.869  |  0.914 |  0.841 | \nSASHA-0.2   |  0.939 |  0.937 |  0.894 |  0.889  |  0.921 |  0.864 |\n```\n\n**BRACS multi-class classification**\n* For the BRACS (BReAst Carcinoma Subtyping) dataset, we employed the public dataset available at [2]. \n* This dataset comprises 547 labeled WSIs, and involves a 7-class classification that characterizes the tumor as one of 3 benign (BT), 2 atypical (AT), or 2 malignant (MT) sub-types. \n* We excluded the “normal” sub-type in the BT class (Group_BT/Type_N) and the two sub-types within each class were grouped into its respective “uber”-class. \n* In addition, a few slides that did not include all relevant levels of magnification (7/503) were discarded. \n* The final distribution of train/validation/test WSIs is as follows:\n```\n         |         BRACS (3-class)          |\n         |  Benign  |  Atypical | Malignant | \nTrain    |    171   |    52    |    135     | \nVal      |     16   |    14    |     21     | \nTest     |     25   |    23    |     16     | \n```\n\n* We performed a 3-class classification among BT, AT and MT classes and compared the performance of SASHA-0.2 with the other sota models. \n```\nMethod      |         BRACS (3-class)        |   \n            | Accuracy  |   AUC    |   F1    | \nDTFD        |   0.609   |   0.796  |  0.609  | \nACMIL       |   0.547   |   0.750  |  0.522  | \nHAFED       |   0.593   |   0.756  |  0.559  | \nSASHA-0.2   |   0.609   |   0.755  |  0.589  | \n```\n* We find that SASHA, even with sampling 20% of the patches, performs comparably with methods like DTFD, and even outperforms ACMIL, each of which sample all of the WSI patches at high resolution.\n* The lower F1 score compared to sota (0.695, Brancati & Frucci, Information, 2024), could be because we used the intermediate 5x→20x zoom levels for SASHA, whereas, conventional models have employed the highest zoom levels (40x).  \n* In future work, we will extend this classification to all the different subtypes (2 per class) as well as normal, perform more extensive hyperparameter searches, and evaluate our model on this 7-class classification problem.\n* These results will also be added to the paper.\n\n*** \n&nbsp;\n\n## 4) Pretraining sensitivity\n\n>  discuss how robust is performance if the ViT encoder is pretrained on smaller or out-of-domain datasets\n\n**ViT-encoder generalizes to OOD data**\n* We seek to clarify that our ViT encoder was not pretrained on the WSI-s specific to each dataset before training. \n* Rather it was pre-trained on a common set of datasets, including >20k WSIs of TCGA dataset and >15k WSIs of the TULIP dataset, as described in [3]; and this pretrained ViT encoder was used, as is without further fine-tuning. \n* In other words, we did not tailor the ViT encoder to be pretrained on the specific datasets on which it was to be evaluated. \n* The fact that our model (SASHA) performed well even with the Camelyon-16 dataset, even though this dataset was not part of the ViT’s pretraining, indicates that these *features were general enough to extend to OOD (out-of-distribution) scenarios*. \n* We will clarify this in the revision. \n\n**Alternative encoder models are effective**\n* Per the reviewer’s suggestion, we have now tested an alternative, histopathology-based, vision-language foundation model -- CONCH (CONtrastive learning from Captions for Histopathology). \n* CONCH is pretrained on 1.17M image caption pairs and provides significant advantages with feature extraction for downstream tasks [4]. \n* Importantly, because the CONCH dataset was created by crawling a Pubmed Open Access (OA) database of histopathology images, and trained in an unsupervised manner without access to WSI labels, it is not in any way tailored to the specific datasets used in our study (e.g. Camelyon-16).\n* Here, we utilized only the image-encoder output from the CONCH model, and ignored the text-encoder output, for encoding WSI features. Performance was evaluated with the Camelyon-16 dataset.\n```\nVariant   | Feat. | Classif. | TSU | RL Pol.| Accuracy | AUC   | F1 \nSASHA-0.2 | ✓     | ✓        | ✓   | ✓      | 0.964    | 0.980 | 0.953 \nResNet-50 | *     | ✓        | ✓   | ✓      | 0.860    | 0.817 | 0.780 \nCONCH     | *     | ✓        | ✓   | ✓      | 0.930    | 0.950 | 0.905 \n```\n* As shown in the ablation experiment table (extending Table 2 in the paper) above, we discover that replacing the ViT with the CONCH encoder produces far less drop in performance, as compared to replacing it with ResNet-50 (table below). \n* Broadly, our results highlight the advantage of employing a feature extractor appropriate for medical image analyses, over generic feature extractors.\n\nWe will report this in the revised “ablation experiments” in Table 2, and discuss the sensitivity to the pretrained encoder in the revision. \n\n*** \n&nbsp;\n\n## 5) Training time cost\n\n> training remains costly and less scalable … a trade-off that is acknowledged but not extensively examined. \n\nWe appreciate this important point. \n\n**Value in low-resource settings**\n* In its current form, the algorithm still finds significant value in low-resource settings. \n* By training the full model in high-resource settings, and fine-tuning HAFED and TSU with a limited number of OOD (out-of-distribution) WSI images, we can run the more efficient RL inference engine on unlabeled WSIs in low-resource settings.\n\n**End-to-end pipeline for RL training**\n* To reduce training time, future improvements would involve developing an end-to-end pipeline that trains the RL agent concurrently with the HAFED and the TSU modules. \n* In such a model, the policy network decides the specific low-resolution patch to sample in real time, followed by HAFED feature distillation for the zoomed-in patch, and TSU state update for the entire slide, with all steps being trained end to end with reward at each time step. \n* This would be followed by the additional steps of training the policy and value networks, and so on, until the end of each episode, for several episodes. \n* With this method, there would be no need to sample every patch at high-resolution during training, and would align with how RL agents learn, conventionally, in novel environments with sporadic rewards [5]. \n\n**Additional points**\n* In the response #1 to reviewer fOU5, titled **Training Cost**, we have provided a detailed discussion about this point. \n* We are unable to repeat the entire response here, due to limits on character counts. We request the reviewer to refer to that response for further details regarding RL agent training stability and the like.\n\nWe will discuss this scope for improvement in detail in the revision, in Appendix A.6 (Limitations).\n\n\n***\n&nbsp;\n\n*References*\n\n[1] Ling et al., Science Data Bank, 2025\n\n[2] Brancati et al., Database, 2022\n\n[3] Kang, et al, CVPR, 2023\n\n[4] Lu et al., Nature Medicine, 2024\n\n[5] Mnih et al, Nature, 2015"}, {"review_id": "VOEvBqDqSg", "reviewer": "Reviewer_LuCH", "summary": "This paper proposes a new RL technique for a whole-slide classification of histopathology images. The idea is to use RL to select higher magnification patches to save on computation while maintaining classification performance. The novelty of this work is around the feature aggregator that allows the author to combine high magnification feature with low magnification, and to use targeted state updater to select only patches with high similarity for updating the state of RL machine. Experiments show some improvement on two histopathology tasks, and ablation studies demonstrate the importance for each component of the method.", "questions": "N/A", "limitations": "yes", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength\n- The proposed method works relatively well for the selected task. Achieving 4x faster inference time and comparable accuracy.\n- The design is well motivated, and ablation study shows the necessity for each component of the technique.\n\nWeakness\n- The tasks selected here are limited to tumor detection-style binary classification. With the complexity of RL, it would be helpful to demonstrate its ability in more complex, multi task classes. One potentially interesting case is breast cancer mitotic activity scoring, which requires the model to detect dividing cells and classify it into three density classes.", "was_revised": true, "final_justification": "I am satisfied with the rebuttal that shows the proposed technique to be on par with SOTA on more complex problems.", "rebuttal": "We thank the reviewer for the insightful suggestions, and for highlighting the motivation of the study, and the computational advantages of our approach. We address their suggestion below.\n\n> helpful to demonstrate its ability in more complex, multi task classes ... breast cancer mitotic activity scoring\n\n* We sought to download the TCGA-BRCA for breast cancer mitotic activity scoring (e.g., Ibrahim et al, Mod. Pathol. 2024). \n* However, the data were not readily accessible because of the need for specialized download managers associated with the GDC data portal, requiring detailed manifest files that we found challenging to configure. We will resolve these download issues and seek to include this dataset in our future evaluations.\n* At this time, following the reviewer’s suggestion, we have evaluated two additional benchmarks, Camelyon-plus and the BRACS dataset, each of which represents a multi-class classification problem.\n\n&nbsp;\n\n## 1) Extension to multi-class classification \n\n**CamelyonPlus 4-class classification**\n* First, we employed the curated CamelyonPlus dataset [1]. \n* This dataset comprises 1,347 WSIs with labels from curated Camlelyon-16 and Camlelyon-17 datasets after removing low-quality slides and upgrading the task to a four-class classification task with the following labels: negative, micro-metastasis, macro-metastasis, and Isolated Tumor Cells (ITC). \n* The following are the train/validation/test splits (numbers indicate WSI counts). We trained the model with the pooled datasets and report the performance separately for the Camelyon-16 and Camelyon-17 test WSIs.\n```\n         |  CAMELYON16+(4-class) |  CAMELYON17+(4-class) | \n         | Neg | Mic | Mac | ITC | Neg | Mic | Mac | ITC |\nTrain    | 172 | 45  | 51  | 6   | 436 | 77  | 123 | 30  |\nVal      | 30  | 12  | 9   | 1   | 100 | 14  | 29  | 8   |\t\t\nTest     | 35  | 14  | 9   | 1   | 96  | 12  | 29  | 8   |\n```\n\n* We observed performance metrics that are on par with sota methods like DTFD and ACMIL (table below). We will add these results to those in Table 1 of the main text.\n```\nMethod      |     CAMELYON16+(4-class) |    CAMELYON17+(4-class)   | \n            |  Acc   |  AUC   |   F1   |   Acc   |   AUC  |   F1   | \nDTFD        |  0.918 |  0.947 |  0.904 |  0.896  |  0.903 |  0.891 | \nACMIL       |  0.962 |  0.925 |  0.965 |  0.826  |  0.932 |  0.827 | \nHAFED       |  0.963 |  0.911 |  0.949 |  0.883  |  0.931 |  0.868 | \nSASHA-0.1   |  0.856 |  0.880 |  0.801 |  0.869  |  0.914 |  0.841 | \nSASHA-0.2   |  0.939 |  0.937 |  0.894 |  0.889  |  0.921 |  0.864 |\n```\n\n**BRACS multi-class classification**\n* For the BRACS (BReAst Carcinoma Subtyping) dataset, we employed the public dataset available at [2]. \n* This dataset comprises 547 labeled WSIs, and involves a 7-class classification that characterizes the tumor as one of 3 benign (BT), 2 atypical (AT), or 2 malignant (MT) sub-types. * We ignored the “normal” sub-type in the BT class (Group_BT/Type_N) and the two sub-types within each class were grouped into its respective “uber”-class. \n* In addition, a few slides that did not include all relevant levels of magnification (7/503) were discarded. \n* The final distribution of train/validation/test WSIs is as follows:\n```\n         |         BRACS (3-class)          |\n         |  Benign  |  Atypical | Malignant | \nTrain    |    171   |    52    |    135     | \nVal      |     16   |    14    |     21     | \nTest     |     25   |    23    |     16     | \n```\n\n* We performed a 3-class classification among BT, AT and MT classes and compared the performance of SASHA-0.2 with the other sota models. \n```\nMethod      |         BRACS (3-class)        |   \n            | Accuracy  |   AUC    |   F1    | \nDTFD        |   0.609   |   0.796  |  0.609  | \nACMIL       |   0.547   |   0.750  |  0.522  | \nHAFED       |   0.593   |   0.756  |  0.559  | \nSASHA-0.2   |   0.609   |   0.755  |  0.589  | \n```\n* We find that SASHA, even with sampling 20% of the patches, performs comparably with methods like DTFD, and even outperforms ACMIL, each of which samples all of the WSI patches at high resolution. \n* The lower F1 score compared to sota (0.695, Brancati & Frucci, Information, 2024), could be because we used the intermediate 5x→20x zoom levels for SASHA, whereas, conventional models have employed the highest zoom levels (40x). \n* In future work, we will perform more extensive hyperparameter searches, extend this classification to all the different subtypes (2 per class) as well as normal, and evaluate our model on this 7-class classification problem.\n* These results will also be reported in the paper.\n\n***\n&nbsp;\n\n## 2) Comparison with competing methods for multi-class classification\n\nIn addition, in response to a suggestion from reviewer ***tugf***, we have also compared our approach with a previous non-RL-based approach, called ZoomMIL [3], in a multi-class setting.\n\n**Differences between SASHA and ZoomMIL**\n* Briefly, ZoomMIL uses a rational strategy of zooming into patches recursively, at different levels of magnification, so that all patches need not be sampled at high resolution, thereby providing significant speedups.\n* A key difference is that in ZoomMIL RoI selection for zooming is performed with a gated attention and a differentiable top-K module, and not with an RL agent, like SASHA. \n* Effectively, this sampling is similar to the RL policy, in that our RL agent also chose to sample and zoom into patches afforded a high attention score by conventional methods as shown in Fig. 3f (Top-k overlap) and Fig. 3g (Avg. Attention Score).\n* Yet, the RL policy does not exclusively sample the Top-k attention patches and, in this sense, the two algorithms’ sampling policies are not identical.  \n* Moreover, while ZoomMIL samples a fixed budget of K patches at each resolution, SASHA samples a fixed proportion of samples at the high resolution (0.1 or 0.2).\n* Therefore, the relative efficacies must be determined empirically through performance comparisons.\n\n**Performance comparison of SASHA with ZoomMIL**\n* We ran ZoomMIL with the Camelyon-Plus benchmark using the binary class classification (4-class). We performed this comparison in two stages: \n\n*Histopathology-tuned ViT feature extractor*\n* First, we re-trained the ZoomMIL model using the histopathology-tuned ViT feature extractor [4] for the 10x→20x zoom levels (top-K=300), for the Camelyon-16 dataset. \n* As shown in the table below, this encoder change improved upon the results reported in the original study, based on the ResNet50 encoder, by ~6-7%.\n```\nMethod              |    CAMELYON16 (2-class)   |\n                    |  Accuracy |  Weighted-F1  |\nZoomMIL (ResNet50)  |   0.842   |     0.833     |\nZoomMIL (ViT-histo) |   0.906   |     0.905     |\n```\n\n*Comparison with SASHA & HAFED*\n* Second, we report a comparison of ZoomMIL with SASHA-0.1, SASHA-0.2 and HAFED for evaluation of two different datasets -- Camelyon-16 and Camelyon-17 -- with the models trained together using the entire CamelyonPlus database. \n* In this case, we report the results for the 5x→20x zoom levels for SASHA (as in our paper), as well as for ZoomMIL (as in their paper), but using the histopathology-tuned ViT feature extractor for all methods.\n* Moreover, SASHA-0.1 and SASHA-0.2 sample on average 84+/-62 (median=68) and 168+/-125 (median=136) patches respectively, based on the observation budget. Therefore, we run ZoomMIL for 2 different values of top-K -- K=80 and 160 -- for a fair comparison with SASHA-0.1 and -0.2, respectively.\n* We also run ZoomMIL with K=300, as per the choice in the original study for the Camelyon-16 dataset.\n```\nMethod             |  CAMELYON16+(4-class)  |  CAMELYON17+(4-class) | \n                   |  Acc  |  AUC  |   F1   |  Acc |  AUC  |   F1   | \nZoomMIL(ViT, k=80) | 0.706 | 0.841 |  0.710 | 0.841| 0.885 |  0.812 | \nZoomMIL(ViT, k=160)| 0.672 | 0.852 |  0.683 | 0.827| 0.860 |  0.792 |\nZoomMIL(ViT, k=300)| 0.741 | 0.852 |  0.688 | 0.820| 0.859 |  0.790 |\nHAFED              | 0.963 | 0.911 |  0.949 | 0.883| 0.931 |  0.868 | \nSASHA-0.1          | 0.856 | 0.880 |  0.801 | 0.869| 0.914 |  0.841 | \nSASHA-0.2          | 0.939 | 0.937 |  0.894 | 0.889| 0.921 |  0.864 |\n```\n* SASHA-0.1 and -0.2 outperform ZoomMIL -- across all top-K values -- by ~5-20% for both Camelyon-16 and Camelyon-17.\n* This difference may be due to the choice of 5x→20x zoom levels. Attention scores -- which ZoomMIL uses for magnification -- were perhaps less reliably computed at the 5x zoom level than at the 10x level, used in their paper.\n* Nevertheless, zooming between the same 2 levels (5x→20x) SASHA outperformed ZoomMIL.\n* We will perform more extensive hyperparameter searches to validate these findings before reporting them in the paper.\n\n\n***\n&nbsp;\n\n*References*\n\n[1] Ling et al., Science Data Bank, 2025.\n\n[2] Brancati, N., et al., Database, 2022.\n\n[3] Thandiackal, K., et al., ECCV, 2022.\n\n[4] Kang, M., et al., IEEE/CVF, 2023."}]

Author rebuttal:

Meta-review: The paper initially received mixed reviews (5543). The major concerns were:

1) high training cost, and lacks inference time comparisons [foU5, jTur]
2) missing ablation study [foU5, tugf]
3) missing discussion about interpretability, robustness, etc. [foU5]
4) experiments only on 2 datasets, when 5+ are expected [tugf]
5) missing comparison to non-RL based sampling methods (ZoomMIL) [tugf] 
6) lacks evaluation on multi-label datasets [jTur, LuCH]

The authors wrote a response to address the concerns.  All reviewers were satisfied with the response, and tugf raised score from 3 to 5, resulting in all positive ratings. Overall, the AC agrees with the reviewers, and notes that they all appreciated the motivation, novelty, and experiments. The authors should revise the paper according to the reviews, response, and discussions.
Decision: Accept (poster)