MAPLE: Multi-scale Attribute-enhanced Prompt Learning for Few-shot Whole Slide Image Classification
Authors: Junjie Zhou
WEI SHAO
Yagao Yue
Wei Mu
Peng Wan
Qi Zhu
Daoqiang Zhang
Venue: NeurIPS 2025 poster ()
Keywords: multiple instance learning
whole slide image classification
prompt learning
vision-language model
few-shot learning
Primary area: applications
TL;DR: We propose Multi-scale Attribute-enhanced Prompt Learning (MAPLE), a hierarchical framework for few-shot WSI classification that jointly integrates multi-scale visual semantics and performs prediction at both the entity and slide levels.

Abstract: Prompt learning has emerged as a promising paradigm for adapting pre-trained vision-language models (VLMs) to few-shot whole slide image (WSI) classification by aligning visual features with textual representations, thereby reducing annotation cost and enhancing model generalization. Nevertheless, existing methods typically rely on slide-level prompts and fail to capture the subtype-specific phenotypic variations of histological entities (e.g., nuclei, glands) that are critical for cancer diagnosis. To address this gap, we propose Multi-scale Attribute-enhanced Prompt Learning (MAPLE), a hierarchical framework for few-shot WSI classification that jointly integrates multi-scale visual semantics and performs prediction at both the entity and slide levels. Specifically, we first leverage large language models (LLMs) to generate entity-level prompts that can help identify multi-scale histological entities and their phenotypic attributes, as well as slide-level prompts to capture global visual descriptions. Then, an entity-guided cross-attention module is proposed to generate entity-level features, followed by aligning with their corresponding subtype-specific attributes for fine-grained entity-level prediction. To enrich entity representations, we further develop a cross-scale entity graph learning module that can update these representations by capturing their semantic correlations within and across scales.  The refined representations are then aggregated into a slide-level representation and aligned with the corresponding prompts for slide-level prediction. Finally, we combine both entity-level and slide-level outputs to produce the final prediction results. Results on three cancer cohorts confirm the effectiveness of our approach in addressing few-shot pathology diagnosis tasks.

MAPLE: Multi-scale Atrribut-enhanced Prompt Learning for Few-shot Whole Slide Image Classification
Junjie Zho Wei Shao Yagao Yue Wei Mu Peng Wan Qi Zhu Daoqiang Zhang 1.2
The College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics "The Key Laboratory Brain-Machine Intelligence Technology. Ministry of Education 'The School of Engneering Medicine, Beihang University
j junjiezhouunuua. www.:oouuuuaaaeeu.u......am cn, shaowei20022055mmaaa www.enuuaaaueuuc...eomaee edu. cn, dgzhangōmuaa, edu. cn
Abstract
Prompt learning has emerged as a promising paradigm for adapting pre- pre-trained vision-language models (VL.Ms) to few-shot whole slide image (WSI) classification by aligning visual features with textual representations, thereby reducing annotation cost and enhancing model generalization. Nevertheless, existing methods typically rely on slide-level prompts and fail to capture the subtype-specifyy phenotypic variations of histological entities (e.g., nuclei, glands) that are critical for cancer diagnosis. To address this gap, propose Multi-scale Attribute-enhanced Prompt Learning (MAPLE), a hierarchical framework for few-shot WSI classification that jointly integrates multi-scale visual semantics and performs prediction both the entity and slide levels. Specifically, we first leverage large language models (LLMs) generate entity-level prompts that can help identify multi-scale histological enti- ties and their phenotypic attributes, as well as slide-level prompts to capture global visual descriptions. Then, entity-guided cross-attention module is proposed to generate entity-level features, followed by aligning with their corresponding subtype-specios attributes for fine-grained entity-level prediction. To enrich entity representations, we further develop a cross-scale entity graph learning module that can update these representations by capturing their semantic correlations within and across scales. The refined representations are then aggregated into a slide-level representation and aligned with the corresponding prompts for slide-level predic- tion. Finally, we combine both entity-level and slide-level outputs to produce the final prediction results. Results on three cancer cohorts confirm the effectiveness of our approach in addressing few-shot pathology diagnosis tasks. Codes will be available https: //gi thub. com WHW-HHOOUU-CHMTTTT
Introduction
Whole slide images (WSIs) have become the clinical gold standard for cancer diagnosis, offering gigapixel-resolution views of tissue architecture and cellular morphology 6 However, their huge size (e.g.. 150,000 x 150,000 pixels) and hierarchical structure render dense annotation individual patches impractical. To overcome this challenge, multiple instance learning (MIL) has emerged as an effective way for weakly supervised WSI analysis [18 32 23
where each WSI is divided into thousands of patches, encoded via pre-trained feature extractor, and aggregated into slide-level representation for classification [17]. Beyond weak supervision, WSI classification faces another critical challenge: the scarcity of the labeled images 26 31. This limitation stems from factors such
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39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Figure 1: Comparison of MAPLE with existing slide-level alignment methods for the classification of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). (a) Existing methods align seiee-level features with corresponding prompts for classification. (b) Our proposed MAPLE introduces additional entity- level features and incorporates subtype-specifie phenotypic attributes for more interpretable and precise alignment. For simplicity, only the single-scale data stream of MAPLE is visualized.
as privacy constraints, the difficulty of acquiring expert-labeled slides, and the low prevalence of certain cancer subtypes [12
37 16 36). Consequently, few-shot learning has become an attractive paradigm for developing robust classifiers on limited labeled data.
Recent progress in vision languae models (VLMs), such CLIP 30), offers a promising path for few-shot learning by aligning image and text representations in a shared embedding space. Building 21 4 adapt textual inputs using small number of labeled examples, enabling effective transfer to new tasks without fine-tuning mhe-tunin the vision backbone. However, applying these methods for WSI classification remains non-trivial. Unlike natural images, WSIs are extremely large and are typically divided into thousands of instances, making it difficult to construct a unified visual representation suitable for prompt alignment (29 10].
upon VLMs, prompt learning techniques 44 43 25
At the same time, designing prompts that accurately reflect the complex tissue morphology and subtype-specine patterns within a WSI is also challenging. Simple prompts like "a WSI of [CLASS]" often fail to capture the localized, fine- ane-grained attributes that are crucial for cancer diagnosis 34 These limitations highlight central question: how can we bridge the gap between fine praingr instance-level visual details and semantically rich prompts for effective few-shot WSI classificatio??
Recently, several methods have attempted to work on it. For instance, TOP 29 introduces instance- level phenotypic prompts to guide patch aggregation into slide-level features, while ViLa-MIL (34) leverages learnable visual prototypes to guide the fusion process of patch features and considers dual- scale visual descriptive text prompt to boost the performance. However, these approaches generally focus solely on slide-level feature alignment after the aggregation of instance-level representations. phenotypic variations of histological entities (e.g.. nuclei,
and fail to capture the subtype-specio cytoplasm, glands) that are critical for cancer diagnosis. For instance, nucleoli are typically small and inconspicuous in lung adenocarcinoma (LUAD) but appear large and prominent in lung squamous cell careinoma (LUSC). Furthermore, different resolution levels in WSIs naturally correspond different scales of histological entities, where low magnification reveals tissue architecture and organization patterns, while high magnification exposes cellular details and nuclear morphology. Ignoring such fine-grained multi-scale entity variations limits the model's ability to capture discriminative patterns and reduces interpretability for cancer diagnosis.
To this end, we propose Multi-scale Attribute-enhance Prompt Learning (MAPLE), a hierarchical framework designed for few-shot WSI classification by the combination of entity-level and slide-level
predictions. Different from the previous slide-level alignment methods [29 34 IO [T]. MAPLE
additionally considers that the diagnostic information among different cancer subtypes is also reflected by the phenotypic attributes of histological entities across different scales, as illustrated in Fig. [ Specifically, we begin by leveraging large language models (LLMs) to construct two types of prompts: entity-level prompts that identify multi-scale histological entities and their distinctive phenotypic characteristics, and slide-level prompts that capture comprehensive global visual patterns. After employing language-guided instance selection strategy to identify discriminative tumor-related patches from WSIs, we subsequently introduce an entity- cross-attention module to extract entity-level features, which are then aligned with their respective subtype-specific attributes to enable

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entity-level predictions. To enrich entity representations, we further develop a cross-saale entity graph learning module that can update these representations by capturing their semantic correlations within and across scales. The refined entity representations are aggregated to construct slide-level representation, which then aligned with the corresponding prompts to enable slide-level prediction. Finally, we combine both entity-level and slide-level outputs to produce the final prediction results. We conduct experiments on three cancer cohorts derived from the cancer genome atlas (TCGA), and the experimental results indicate the advantage of MAPLE on few-shot pathology diagnosis tasks.
2 Related Work
2.1 Multiple Instance Learning for WSI Classification
Due to the gigapixel size of WSIs and the infeasibility of dense patch-level annotation, MIL based methods B 28 33 32 have become the prevailing approaches for WSI analysis. In the MIL framework, each WSI is represented as bag of instances (patches), with only slide-level labels avail- able for supervision. Early MIL approaches aggregate instance features using non- parammetriic max or mean pooling operations. Subsequent approaches introduce attention- titention-based pooling mechanisms that learn to assign importance weights to instances, significantly enhancing discriminative power and classification performance [15
40] Recent works have further advanced the MIL. frame- work with more structured representations. For example, GTP 22 introduces graph-based vision transformer that models WSIs using sparse token selection and
32 39
relations. WiKG [19] proposes a novel dynamic graph representation algorithm that conceptualizes WSIs as a form of the knowledge graph structure. While these methods demonstrate strong performance under full supervision, they typically require large annotated datasets for training, making them less suitable for scenarios with limited data availability, which is common challenge clinical settings. In this work, we propose a multi-scale vision- vision-language language models to enable effective WSI classification in few-shot scenarios, substantially reducing the annotation burden for practical clinical applications.
seribute-enhaneee prompt learning framework that combines MIL with
2.2 Prompt Learning for Few-shot WSI Classification
Prompt learning downstream tasks in data-scarce settings by optimizing only a small set of textual tokens rather than entire model parameters. Recent work has extended this paradigm to the WSI classification
44
43 25
4 has emerged
efficient strategy for adapting VLMs to
by combining prompt learning with MIL for few-shot classification 22 34 10 M.. TOP [29] first introduces a two-level prompt learning strategy that incorporates linguistic priors to guide both instance- and slide-level feature aggregation. ViLa-MIL (34] extends this direction by proposing dual- scale visual prompts, enabling the fusion of features across high- and low-resolution magnifications. FOCUS [00] introduces three-stage compression strategy that leverages both foundation models and language prompts for focused analysis of diagnostically relevant regions. MSCPT 11 employs graph prompt tuning module to capture spatial context within WSIs. However, these methods primarily emphasize slide-level alignment and often overlook the fine-grained, subtype-specific phenotypic attributes of histological entities. Despite TOP first introduces a two-level framework, it solely focuses on slide-level alignment, treating histological instances implicitly through aggregation rather than modeling them explicit diagnostic targets. Consequently, it fails to capture the fine- grained, cubtype-speciffe phenotypic variations of nuclei, cytoplasm, glands, and other histological entities that pathologists rely on for differential diagnosis. Our MAPLE framework addresses this gap by introducing entity-level prompt learning that explicitly models these diagnostic cues across multiple scales, enabling more accurate and interpretable few-shot WSI classification.
3 Preliminaries
3.1 Problem Formulation
Given a dataset X' = (X1, X2...., XN) consisting of N WSIs, each slide X; can be associated with a slide-level label yı E 2,.... C}. where C' is the number of diagnostic categories. Due to the extremely large size and high resolution of WSIs, it is computationally infeasible to process
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Figure 2: Framework of our proposed MAPLE. (a) MAPLE leverages the LLM to identify multi-scale his- tological entities, and then builds a cross-scale entity graph by modeling the semantic relationships wthin and across scales. (b) Both entity-level and slide-level prompts are enriched with learnable context vectors to enable effective alignment with corresponding visual features. (c) MAPLE jointly integrates multi-scale visual semantics and performs prediction at both the entity and slide levels.
entire slides directly. Instead, each WSI is divided into a set of non-overlap K; patches X, (4.2 ... 2 K. where z.jj E Rd denotes the feature vector of the j-th patch for slide X,.
To address the WSI classification task, a common approach is to formulate such weakly supervised learning problem multiple instance learning (MIL) [18 32 23 D. In the binary classification setting, MIL assumes that bag (ie., a slide) is labeled positive at least one of its instances is positive; otherwise, it is labeled negative [39 17
3/ =
Exy E X, is positive.
For multi-class scenarios, this formulation extends to identifying the dominant cancer subtype represented by the most discriminative patches within the slide.
In the few-shot classification setting, the problem becomes even more challenging: the objective is to learn a reliable classifier using only limited number of labeled WSIs per class. The term "shot" refers to the number of labeled examples per class, commonly set to 1, 2, 4, 8, or 16.
3.2 Prompt Learning for Few Shot WSI classification
VLMs such as CLIP 30 typically consist of two parallel encoders: a vision encoder fu(-)) and text encoder fe(.), which are jointly trained using contrastive learning over large-scale image-text pairs. Prompt learning has emerged as a -ffficiet strategy to adapt pre-trained VLMs to downstream tasks, such as few-shot WSI classification, without extensive fine-tuning [29] (3]..
The typical pipeline for prompt sarming-based few-shot WSI classification involves two key steps: instance-level feature aggregation and slide-level alignment with learnable prompts (29
Given WSI X; { ... } partitioned into K; patches, each patch miy is first
embedded using a frozen image encoder fr. The instance embeddings are then aggregated via a pooling operation (e.g., max, mean or attention-based) to obtain compact slide-level representation Prompt learning then adapts the text encoder by introducing a set of M learnable context vectors V = (U1, U2,..., VM }. 4 (uI...., UM, Cx) is constructed by concatenating these context vectors with the embedding of the class name Cs. and passed through the text encoder to generate the class-specific textual feature = fe(lx). The prediction probability for class t is computed based the cosine similarity between the slide-level visual feature and the class-specific textual features:
= (fgr
For each class k, a textual prompt
cxp(sim ",,,,,,, 2{)/))
P(y = k|x,) =
(2)
Σ cxp(sim , 2tT)

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where T is learnable temperature parameter and sim(..-) denotes cosine similarity. The learnable context vectors V are optimized by minimizing the standard eross-entropy loss between the prediction and the ground-truth label:
LCE
Σ log P(u Uz Xx),
where yi is the ground-truth label for slide X,.
4
Method
In this section, we present the details of our proposed method MAPLE, Multi-scale Attribut--nhanned Prompt Learning for few-shot WSI classification. An overview of the framework is illustrated in Given a WSI X,, we partition it into two sets of non-overlapping patches at both high K
Fig. 2
K! resolution i.e., X = { [-j and low resolution i.e. X = 1 where K and K denote the number of patches at different scales. Then, each patch is embedded with frozen vision encoder fo of the vision-lanm language model (PLIP [4] in our implementation), resulting in the feature sets K -² {2 1=1 and Z و We leverage the LLM construct entity-level and slide-level prompts (Section .. and employ a ringagee-guidee instance selection strategy to identify tumor- related regions that are most relevant for the discrimination of different cancer subtypes (Section (.2). Next, the selected features are aggregated to construct entity representations and aligned with subtype- specific attributes to enable tne-grained entity-level classification (Section (.3). Then, we refine entity representations via the cross-scale graph learning module and subsequently aggregate them to obtain slide-level representations that align with corresponding prompts for slide-level prediction (Section 4.4). Finally, MAPLE jointly optimizes entity-level and slide-level alignment, enabling robust and interpretable few-shot classification (Section 4.5)
4.1 LLM-powered Prompt Construction
For the cancer diagmosis from WSIs, pathologists usually combine the observations from key tissue entities (e.g.. nuclei, cytoplasm, glands) and the overall context of the entire slide to make decisions [ Specifically, for the WSIs observed at high resolution, pathologists analyze cellular components such as nuclear pleomorphism and cytoplasmic features for cancer diagnosis. As to the image with low-resolution, they distinguish different cancer subtypes by examining tissue architecture such as gland formation and tumor-stroma interfaces (20 16]. Inspired by the aforementioned diagnostic way from the pathologists, we construct multi-scale prompts both the entity and slide levels to capture discriminative visual attributes associated with specific cancer subtype patterns. Accordingly, the prompt construction process is composed of two parts, i.e., Entity-level Prompt Discovery and Slide-level Prompt Summary, detailed below.
Entity-level Prompt Discovery. Let C = {cr, cc) denote the set of cancer subtypes. We construct entities from two scales & 8d U g where gh and &l represent the entities derived from high-resolution and low-resolution images, respectively. For each entity E E at scale (h, (}. we query the L.MM to generate two types of textual prompts:
(1) Generic visual description pmm summarizes the general appearance of entity at scale (2) Subtype-specific attributes (pe,ceecc: describes the attribute of entity e in subtype at scale s. Slide-level Prompt Summary. In prior works 29 34) slide-level prompts are often defined using class names (e.g., "a WSI of [CLASS]") or with global descriptions directly generated from LLMs. However, such templates fail to reflect the fine-gre fine-grained entity-level information. To address this, we use the LLM to generate the slide-level prompt p glide, for each cancer subtype e E C at scale (h, by the combination of entity names E and thus can integrate fine-grained entity information into the slide-level representation. More details on prompt construction using L.LMs are provided in Appendix A
4.2 Language-guided Instance Selection
Accurate identification of tumor-related regions within gigapixel WSIs is crucial for cancer subtype classification, as these regions contain the most discriminative diagnostic information 27] There- fore, we propose a language-guidee instance selection strategy that can help identify tumor-associated
5

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patches by leveraging the pre-trained vision-language models. Specifically, we first query the LLM with the following instruction: "What are the visually descriptive characteristies of the tumor-related region in a WSI at high/low resolution?" Based on this query, the LLM generates region prompts Preg = -Pree Preg) for high and low resolution, respectively. Then, each prompt is processed by the frozen text encoder Jt to obtain the corresponding text embeddings: Log and ''eg Given the extracted patch features Z and Z for slide X; from different resolutions, compute the cosine similarity scores between the region text embeddings and patch features from different scales as S sim(t/eg: 2T') and S sim(t Zí). Based these similarity scores, select the top-k instances with the highest similarity scores at each scale to form the tumor-related instance sets:
Ui) < k"}, Z: = (+
DS)) < k')
(4)
where h and ke denote the number selected instances at high and low resolutions, respectively.
4.3 Entity-guided Attribute-enhanced Classification
To incorporate semantic guidance from entity-level prompts, we encode both the generic descriptions and subtype-specine attributes into learnable embeddings. For each entity e E E at scale & (h. l), we prepend a shared set of learnable context vectors V {U1..... UM } to the textual descriptions, forming the learnable prompt tokens t2 ggnn = {UI,,.... UM Pe
g } and t² = (., UM Pr.e)- These tokens are then encoded by frozen text encoder ft to obtain the final prompt embeddings:
dgen, fe(te d= = fe
(5)
Furthermore, we introduce an Entity-guided Ettes-nttention module derive the visual representation of each entity by aggregating instance features. Given the instance set 7 { ) from slide X; scale s {h, 1 and the generic prompt embedding dgen.s E IRd for entity e, the entity-specific feature is computed as:
gagen. (W*Z,) vdg
Ze,k = Norm softmax
W W.Z,
- +d dgen.s
(6)
where W., We, W. Rdxds are learnable projection matrices, and Norm(-) denotes layer normal- ization. This attention mechanism enables the model to selectively aggregate instances that exhibit strong semantic alignment with entity e, yielding a compact and discriminative representation of its visual characteristics within the slide.
For entity-level classification, we compute the cosine similarity between the visual representation = of entity e in slide X; and the corresponding subtype-specific prompt embedding di,c for subtype c:
sim 2
Next, we will integrate G with slvee-level predictions to derive the final classification results, as detailed in Section 4.5
4.4 Multi-scale Entity Aggregator for Slide-level Classification
In this section, we propose multi-scale entity aggregator, where we firstly construct cross-scale entity graph that connects semantically related entities within and across different entity scales to enrich entity representation, and then aggregate the refined entity features obtain slide-level representations which are aligned with corresponding prompts for slide-level prediction.
Cross-scale Entity Graph Learning. Let Z, { legga (el,shegs denote the set of entity
features at different scales for slide X,, We define a graph G, = (V,, &,), where each node V. corresponds to an entity feature in E, To capture semantic relationships between entities across different scales, we compute the cosine similarity between features 2n and 2or as sim(2,, zer) =
[ TI For each node v. its neighborhood ((o) defined as the set of top-k most similar nodes:
M(e) TopK, (sim Zv, 2.
(8)

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Then, the Graph Attention Network (GAT) [35] is applied to propagate information across nodes based on learned attention weights. For each node v, the updated entity representation computed as:
b. = =
Σ
Orwye Wy
('EN())
where W E &Rd''d ReLU), and Ow,v' is the attention coefficient:
xd is learnable weight matrix, a()) denotes a non-linear activation function (e.g..
exp (LeaayReeU(a" [Wg g
W g2
(10)
ZnEN(()) exp (LeakyReLU(a [W g
W g
with a 2dd being a learnable attention vector, and || denoting vector concatenation operation.
Slide-level Representation. We adopt gated attention mechanism 23 to derive the slide-level visual representation by aggregating the refined entity features. Let H ( E TR x denote the entity features at scale s, where N, = -E" The slide-level feature for scale 8 can be obtained via the following weighted sum form:
slide. a H lx
(11)
V (anh(H:: A o (H:W))), a softmax ((AV & AW))",
(12)
where Wy v. W Rdxd Rd are learnable parameters, C denotes element multiplication, and a E R N represent the normalized attention scores.
Prompt-based Slide-level Alignment. For each subtype C and scale E {h, (}, we construct the slide-level textual prompt by concatenating the learnable context vectors V with the scale-specific slide-level token P. diide..
plide. = fu..... UM, Pe slide, Pe }, golide,s = fr (1t(lidee)))
(13)
where fr(.) denotes the text encoder. The slide-level classification logits for subtype e at scale can be computed
slide.s c,e
sim(2, Siddz,
sldde d gelide.s).
(14)
4.5 Training Strategy
For each slide X4. we obtain the slide-level logits Eiide (shown in Eq. 14 and entity-level logits Ex (shown in Eq. 7). To integrate both slide-level and fine- grrengraindd entity- logits across different scales, the final classification logits can be computed via weighted combination:
grnall
1
Σ SE(LA)
A glide, +11
(15)
2
EES
where À is the hyperparameter that controls the contributions of the slide- and entity-level predictions. Finally, the objective function for our MAPLE is formulated as follows:
C
Ler( final
y:).
(16)
where Log is the cross-entropy loss defined in Eq, 3 and yi E C is the ground-truth label for slide X,.
Experiments
Datasets. We evaluate MAPLE on three benchmark WSI datasets from The Cancer Genome Atlas (TCGA): TCGA-BRCA, TCGA-RCC, and TCGA-NSCIA More details for the classification task on each cohort are provided Appendix B.T To simulate the few-shot learning scenario in clinical practice, randomly sample K WSIs per class, where (K = 4, 8, 16 in our implementation).

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Table 1: Few-shot WSI classification results TCGA-BRCA, TCGA-RCC, and TCGA-NSCLC datasets under 4-shot, 8-shot, and 16-shot settings. The best results are in bold, and the second-best results are underlined.
Dataset
Methods
TCGA-BRCA
TOGA-RCC
TCGA-NSCI.C
AEC
F1
ACC
AUC
F1
ACC
AEC
F1
ACC
ARMILL
0.655 + 0.997
0.555
0.061
0.077 0.068
0,876 + 0.823
0.650
+ 0.053
0.681 # 0.053
0.625 + 0.857 0.581
0.063
0.586 0.061
TrasSMIL
0.666 0.036
0.558
0621
01881 4 0.024
0.656
0.057
0.602
0.063
0.629
0.563
0,049
0.581
GTMIL
0.69 0,048
0,522
0.108
0.644 1. 0.136
0.883 1 0.017
0.685
1 0.042
0.713
1. 0.048
0.683 0.946
0.600 1. 0.051
0.608
0.040
4-tot
WIKG
0.53 a: 0.029
0.536
à: 0.107
1615 à: 0.163
0,990 0.030
0.655 4 0.098
0.690 à 0.094
0.620 a: 0.050
0,563
0.072
0.579 2: 0.09
TOP
0.652
0.515
0.11
0.166
0.625
0.067
1. 0.089
0.624
0.531
0.588 0.051
ViLa-MILL
0.663 4: 0.092
0.503
0.100
16666
+: 0.159
0,77 4: 0.852
0.635 +: 0.133
0.658
#: 0.117
0.629 +: 0.943
0.580
+: 0.945 0.589 4: 0.037
MSCPT
0.678 0.045
0.550
0.050
0.593
01872 0,067
0.654
1 0.052
0.673
0,062
0.266 0.020
0,582
0.588 0.038
FOCUS
0.203 1 0.051
0.564
0.695
0.33
0.14
0.890
1 0.035
0.663
1 0.059
0.702
1: 0.054
0,713 1 0.093
0.31
0,078
0.66 1. 0.057
MAPLE
0,722 a: 9.063
0.594
a: 0.076
0.664
a: 0.134
0.909 a 0,020
0.705
à 0.055
0.726
a: 0.057
0.740 a: 0.056
0.663 à: 0.052
0.675 à 0.053
ARMIL.
0.74 0.061
0:557
0.11
0917
0.014
0.783
0.078
0.768
0.042
0.724
0.632
0.634+ 0.000
TraaMil..
0.746 + 0.063
0.578
+ 0.835
0.630 0.035
0.15 + 0.019
0.751
# 0.040
0.765 + 0.048
0.715 + 0.056
0.627
0.112
0L631 0.095
GTMI
0.64 0.056
0,586
:: 0.092
0633
0.13
0917 + 0.018
0.765
0.036
0.761 +: 0.019
0.766 0.041
0.626
0,072
0.644 :: 0.054
M-shot
WIKG
0.709
0.047
0.537
0.073
0.579
0.
0,902 + 0.017
0.728
+ 0.069
0.752 + 0.065
0.740 0.071
0.654
0.070
TOP
0.733
a: 0.047
0.546
0.059
1,580 a: 0.071
0.900 à 0.026
0.702 à: 0.067
0.736
d: 0,000
0.752 2: 0.066
0.652
a: 0.038
01663 2: 0.041
Vila-M..
0.770
0653++0.00
+0000
0.033
0.034
0.643
0.649
MSCPT
0.768
# 0.064
0.555
+ 0.067
1.596 #: 0.090
0.926
+ 0.021
0.771
# 0.133
0.792
# 0.033
0.768 +: 0.966
0.685
0.072
0U692 4: 0.067
FOCUS
0,767
0.054
0.579
04162:0..11
0.94 0.016
0.765
0,043
0.783
0.050
0.818 0.054
0.757
0.066
0.739 0.063
MAPLE
0.786
0.070
IL618
0.024
0.018 IL673
0LU57
0.015
0.124
0.806
0.014
0,555
(L762
IL766
ADMIL
0.724 1. 0.048
0.567
0.047
0.657
1. 0.056
0.937 1 0.012
0.757 1. 0.024
0.790
1. 0.028
0.815 1 0.041
0.747
1.044
0.753 1. 0.048
TranMIIl.
0.738 0.090
0.630
0.001
0.079
0813-+
0.057
0.740
0.741
GIMIL
0,243 1. 0.051
0.619 1 0.009
0.681 0.090
0.30 1 0.008
0.803 1 0.046
0.829
1. 0.042
0.827 0.94
0.750
1. 0.040
0.752 0.038
16-dhot
WIKG
0.754
0.036
0,593
0.054
0.37 0.07
0.43 1: 0,018
0.791
1 0.037
0816 0.037
0.832 0.040
0.755
0.039
0.756 0.039
TOP
0.025 0.775
0.636
0.939 0.015
0.769
1 0.043
0.037
0,84 0.066
0.738
0.730 0.125
ViL=-MIL
0.789
0.022
0.651 0.004
0.703 0.04
01833 1 0.002
0.797
1 0,016
0827 1: 0.042
0.824 0.055
0.757
0.046
0.757
1. 0.047
MSCPT
01758 0.043
0.641
0.702
0.046
0.900 0.011
0834
0.027
0.765
TOCUS
0.765
0.052
0.633
0.046
0.693 0.093
0851
1 0.008
0.826
1 0.021
0,811
1. 0.021
0882 J 0.056
1.011100.051
0.781 0.051
MAPLE
0.801 9.03
[U672
9.076
0.735 + 0,0.9
0.969 + 0.014
0.838 + 0.034
0.867 + 0.03
0 0.033
0.806 0.060
0.810 0.055
Implementation Details. We utilize CLAM 23 for WSI pre- pre-processing. followed by ViLa- MIL [34] to crop both high-resolution (10x) and low-resolution (5x) WSIs into patches with the size of 256 226.. We employ PLIP 14 as vision-language backbone, with feature dimension of 512 for both visual and textual modalities. GPT-4 (2) is taken as the frozen large language model (LLM). For hyperparameter settings, the the number of entities at each scale n& is tuned from 4 to 20 with an interval of 4 (Section [.J). while the number of neighbors for constructing the cross-scale entity graph n. = 7 is tuned from 1 to 13 with interval 2 (Section 4.4). Since WSIs with different numbers of divided patches, we select the top 7r percentage tumor-related patches for each WSI as the the top-k patches, and we tune from 0.1 to I with interval 0.2. Finally, the weighting parameter A (Section 4.5) for combining entity-level and slide-level predictions is tuned from 0 to I with interval 0.1. The model is optimized using Adam W with learning rate of 1 x 10-4 and trained for up to 80 epochs with early stopping based on validation performance. All experiments are conducted using PyTorch 2.0.1 and CUDA 11.7 on Python 3.8 with NVIDIA RTX 3090 GPUs.
Evaluation Metrics. The area under the curve (AUC) score, F1 score (F1), and accuracy (ACC) are utilized as the evaluation metrics in our experiment. We conduct fiye-fold cross-validation and the mean and standard deviation are calculated according to the results of all folds.
5.1 Main Results
We compare MAPLE with SOTA MIL based methods including ABMIL 15). TransMIL 122
GTMIL 42), and WiKG-MIL 19) as well as SOTA prompt based methods in few-shot WSI classification methods like TOP 29), ViLa-MIL 34 MSCPT [ and FOCUS 10 PLIP [4] is applied to extract both visual and textual features for all methods We compare MAPLE with these methods across three datasets under three seo- settings (4-shot, 8-shot, and 16-shot), as shown in Tab. T. We provide additional comparison results using different vision-language models (i.e., CLIP [30 and CONCH (24 in Appendix C.T and C.3 and further comparisons of model complexity and efficiency in Appendix B.3
TCGA-BRCA (2 classes). On the TCGA-BRCA dataset, our proposed MAPLE consistently outperforms all baseline methods across all few-shot settings. In the 16-shot scenario, MAPLE achieves an AUC of 80,1%, Fl of 67.2% and ACC of 73.5%, surpassing the second-best performing ViLa-MIL with an improvement of 1.2% in AUC, 2.1 1% in Fl and 3.2% in ACC. This advantage is
8

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Table 2: Effects of different levels and entity scales under the 16-shot setting.
Mechods
AUC
TOGA-BRCA
Fi
ACC
ACC
TCGA-RCC
F1
ACC
ACC
TCGA-NSCLC
F1
ACC
MAPLE-Lom
0.790 1. 0.052
0.540 0:079
0.704
1 0.1101
0.949 1.0.00
0.796
0.077
0.832
0.000
0.806
0.045
0.780
0006
0,782
1 0.0445
MAPLE-Higgh
0.779 1: 0.065
0.51
0:067
0.229
1 0.800
0.956
1.0.16
0.17
0.48
0.842
0.001
0.875
0.050
0.789
0009
0.791
1 1.048
MAPLE-Emiy
0.792 x: 0.052
0.63
0:077
0.104
0.900
0.014
0815
0.02
0.866
0.03
0.879
0.000
0.787
0045
0.786
0.044
MAPLE-SWA.
0.789
4: 0.035
0.644
0:000
0.702
1 0.06
0.51
4 0.011
0.19
0.41
0,50
1 0.037
0.891
0.033
0.777
1: 0.06
0.785
1 1.003
MAPLE
0.801
0.031
0,672
1 0.076
0.735
1 0.039
0,909 0.014
0.838
0.834
0.867
0.031
0,903
0.033
0.806
0.060
0,810 1 0.055
Table 3: Ablation study each component under the 16-shot setting. "woo Selection" refers MAPLE without
language-guiie instance selection step, "wo FGCAA" refers to MAPLE without entity-guided cross-attention module, and "w/o Graph" refers to MAPLE without the module of cross-scale entity graph learning.
Methods
AUC
TCGA-BRCA
F1
ACC
ABC
TCGA-RCC
F1
ACC
AEC
TCGANSCLC
F1
ACC
MAPLE (wrl Sclextins)
0.792
0.09
0.55
0.400
0.710 = 0.945
0.962 =: 0.015
0.823
0.056
0.559
0.55
0.885
#: 0.064
0.785 #: 0.588
0.791
0,057
MAPLE (wrl BOCA)
0.784 = 0.09
0651
0044
0.200
= 0.861
0.559
=: 0.055
0.820
9.31
0.552
0.032
0.579
+: 0.042
0.782
#: 0.448
0.790
2: 0.047
MAPL (wlo Grigh)
0.791
001
BASS
007S
0216
8.101
0997
0011
0.21
90%
0355
98M
0:381
0.99
0:991
4 0.42
MAPER
0,000
RU672
0U176 0.788 0009
0.999 00114
0.88
0,67
JULM
0,03
0J833
0,306 0L000
0,810 10058
maintained with extremely limited training data. In the challenging 4-shot setting, MAPLE achieves an AUC of 72.2%, F1 of 59.4%, and ACC of 66.4%, demonstrating significant improvements over the second-best method FOCUS (AUC: 70.3%, F1: 56.4%, ACC: 63.3%).
TCGA-RCC (3 classes). For the multi-class TCGA- dataset, MAPLE delivers exceptional performance across all metries and settings. In the 16-shot scenario, MAPLE achieves the highest AUC (96.9%). F1 (83.8%). and ACC (86.7%) showing significant improvements over the second- best method FOCUS. As the number of shots decreases to 8 and 4, MAPLE maintains its superior performance with AUC scores of 95.7% and 90.9%, respectively.
TCGA-NSCLC (2 classes). For the TCGA-NSCLC dataset, MAPLE again achieves superior performance across all metrics, with AUCs of 74.0%, 85.5% and 90.3% in the 4, 8, and 16-shot settings respectively, consistently outperforming FOCUS (AUC: 71.3%, 81.8%, and 86.2%).
Overall, MAPLE consistently outperforms all baselines across three cancer datasets under different few-shot settings, validating the effectiveness of MAPLE in few-shot WSI classification.
5.2 Ablation Study
We conduct comprehensive ablation studies to investigate the effectiveness of each component in MAPLE. The more detailed analysis of the hyperparameters (i.e., The TM: v, and À mentioned in implementation details) and the impact of different large language models is provided in Appendix D
Impact of Combining Slide-level and Entity-level Log- its. To evaluate the effectiveness of combining both slide- level and entity-level logits for cancer diagnosis, we com- pare MAPLE with its competitors that only using entity- level (MAPLE--ntity) and slide-level (MAPLE-Slide) logits under the 16-shot setting in Tab. 2 Notably, the entity-level information achieve comparable or even slightly superior performance to the results using slide- level logits, demonstrating the effectiveness of modeling fine-grained histological entities and their subtype-specific attributes for cancer diagnosis. In addition, it is obviously that our MAPLE performs better than MAPLE- E-tnttt and MAPLE-Slide which confirms the complementary nature of entity-level and slide-level information for cancer sub- type classification. We also discuss the effects of parame- ter X that is applied to balance the contribution of slide-level and entity-level logits in Appendix D.4
(a) Nuckoouu
(b) Kenminiztiinn
(c) Cympliss
(4) Stade-level
Figure 3: t-SNE results of entity- (a c) and slide-level (d) embeddings on the TCGA- NSCLC dataset.
To further demonstrate the discriminative power of entity-level and slide-level representations,
visualize the learned representations from the entities of Nucleolus (Fig 3 (a)). Keratinization (Fig
(b)) and Cytoplasm (Fig. 3 (c)), and slide-level embeddings ((Fig. 3 (d)) on the TCGA-NSCLO
dataset using t-SNE. As shown in Fig. 3] all representations exhibit clear separation among different
cancer subtypes, confirming their ability to capture subtype-specine patterns.
Impact of Multi-scale Entities. To evaluate the effectiveness of integrating multi-scal entities for few-shot WSI classification, we compare MAPLE with its two variants that rely solely entities

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Eatly Straes (Levreeliia)
TCGA-85-933
TECA-SS-AAO
apocas lighte
Dippon denec mare collageridd
Eadie: Naciooles (1lg) contecton)
baiio-ecbiod puichex
LUAD
LUSC
Attelaatc
Altrihate
Propently seail Inonnglcao..
Tepielly lage,ponnssci..
Figure 4: Visualization of entity-relovant patches selected by the entity- cross cros-attention module for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) on the TCGA-NSCLC datasct. Top rows show patches and their corresponding entity attributes (e.g,, stroma) low resolution, while bottom rows show patches and their corresponding entity attributes (e.g., nucleoli) high resolution.
extracted from high-resolution (MAPLE- High) or low-resolution (MAPLE-Love images. As shown in Tab. 2 MAPLE yields higher classification results to MAPLE-High and MAPLE-Low, which validates superiority integrating multi-scale entities for cancer subtype classification.
Impact of Each Component. We further ablate three key components of MAPLE, i.e. language- guided instance selection module, aatity-guided cross-attention module and cross-scale entity graph learning module, to assess their contributions. As shown in Tab. 3 each ablation study achieves inferior performance to MAPLE, indicating that each component contributes positively to the results.
5.3 Visualization Results
To validate entity-level interpretability of MAPLE, we visualize instances identified by our entity- guided cross-attention module on the TCGA-NSCLC dataset. Fig. 4 presents patches selected using generic entity descriptions, which exhibit subtype-specific characteristics matching their corresponding attribute prompts. For example, for the image patch at low resolution, stroma-related patches from lung adenocarcinoma (LUAD) appear lighter and less dense, while those from lung squamous cell carcinoma (LUSC) display denser and more collagenized patterns. Similarly, for image patch at high resolution, nucleolus-focusee nucleoli, whereas those from LUSC exhibit large, prominent nucleoli. These visualizations confirm that MAPLE effectively captures the relationships between entity-related patches and their subtype- specific phenotypic attributes described in our LLLM-generane prompts, providing visual evidence for our classification decisions. More visualization results on the TCGA-BRCA and TCGA-RCC datasets are provided Appendix
patches from LUAD contain small, inconspicuous
Conclusion
In this paper, we introduce MAPLE, a hierarchical prompt learning framework for few-shot WSI classification that explicitly models multi-scale histological entities and their phenotypic attributes. By bridging the semantic gap between fine-graie mre-grai visual details and textual descriptions through entity-level and slide-level alignment, MAPLE provides both enhanced classification accuracy and improved interpretability for cancer diagnosis. Extensive experiments across three cancer datasets demonstrate that our approach consistently outperforms tate-of-the-ar methods. The hierarchical nature of MAPLE aligns well with pathologists' diagnostic workflow, offering a promising direction for computer-aided diagnosis in computational pathology.
Acknowledgment
This work is supported by the National Natural Science Foundation of China (Nos. 62136004, 62572239, 62272226, 62571019, 621 66176,, 62402219), National Key R&D Program China (No. 2023 2048033, the Key Research and Development Plan of Jiangsu Province (No. BF2022842), Natural Science Foundation of Jiangsu Province(No. BC20241382) and Jiangsu Funding Program for Excellent Postdoctoral Talent (No. 2024ZB020).
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References
[1] Heba Abdel-Nabi, Mostafa Ali, Arafat Awajan, Mohammad Daoud, Rami Alazrai, Ponnuthurai N Sug- anthan, and Talal Ali. A comprehensive review of the deep learning- based tumor analysis approaches in histopathological images: segmentation, classification and multi-lcaming tasks, Cluster Computing, 26(5); 3145-3185, 2023.
[2] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint wxXiv:2303.08772 2023.
[3] Martim Afonso, Praphulla MS Bhawsar, Monjoy Saha, Jonas S Almeida, and Arlindo L Oliveira. Multiple instance learning for wsi: A comparative analysis of attention beentio--bbsedd approaches. Journal of Pathology Informaties, 15:100403, 2024.
[4] Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, and Kun Zhang. Plot: Prompt learning with optimal transport for vision-language models. arXiv preprint arXiv:2210.01253, 2022.
[5] Shenghua Cheng, Siho Liu, Jingya Yu, Gong Rao, Yuwei Xiao, Wei Han, Wenjie Zhu, Xiaohua Ix, Ning Li. Jing Cai, et al. Robust whole slide image analysis for cervical cancer screening using deep learning. Nature communications, 12(1):5639, 2021.
[6] Angel Cruz Roa, Hannah Gilmore. Ajay Basavanhally, Michael Feldman, Shridar Ganesan, Natalie NC Shih, John Tomaszewski, Fabio A González. and Anant Madabhushi. Accurate and reproducible invasive breast cancer detection in whole-slide images: A deep learning approach for quantifying tumor extent. Scientific reports, 7(1 (1)4:04500, 2017.
[7] Xiaofeng Dai, Ting Li, Zhonghu Bai, Yankun Yang, Xiuxia Liu, Jinling Zhan, and Bozhi Shi. Breast cancer intrinsic subtype classification, clinical use and future trends. American journal of cancer research, 5(10):2929, 2015.
[8] Ruining Deng, Can Cui, Lucas W Remedios. Shunxing Bao, Michael Womick, Sophie Chiron, Jia Li, Joseph T Roland, Ken S Lau, Qi Liu, et al. Cross-scale multi-instance learning for pathological image diagnosis. Medical image analysis, 94:103124, 2024.
[9] Michael Gadermayr and Maximilian Tschuchnig. Multiple instance learning for digital pathology: review of the state-of-the-art, limitations & future potential, Computerized Medical Imaging and Graphics, 11:1122331 2024.
[10] Zhengrui Guo, Conghao Xiong, Jiabo Ma, Qichen Sun, Lishuang Feng, Jinzhuo Wang, and Hao Chen. Focus: Knowledge-enhanced adaptive visual compression for few-shot whole slide image classification. arKiv preprint arXiv:2411.14743. 2024.
[11] Minghao Han, Linhao Qu Dingkang Yang. Xukun Zhang, Xiaoying Wang, and Lihua Zhang. Msept: Fho-shot whole slide image classification with multi-scale and context-focused prompt tuning. arXiv preprint Xiv::2408 11505 2024.
[12] Petr Holub, Heimo Müller, Tomáš Bil, Luca Pireddu, Markus Plass, Fabian Prasser, Irene Schlünder, Kurt Zatloukal, Rudolf Nenutil, and Tomáš Brázdil. Privacy risks of whole-slide image sharing in digital pathology. Nature Communications, 14(1):2577, 2023.
[13] Ie Hou, Dimitris Samaras, Tahsin M Kure, Yi Gao, James E Davis, and Joel H Saltz. Patch-based convolutional neural network whole slide tissue image classification. Proceedings of the IEEE conference on compuder vision and pattern recognition. pages 2424-2433. 2016.
[14] Zhi Huang, Federico Bianchi, Mert Yuksekgonul, Thomas Montine, and James Zou. A visual language foundation model for pathology image analysis using medical twitter. Nature medicine, 29(9):2307 2316, 2023.
[15] Maximilian llsc, Jakub Tomezak, and Max Welling. Attention-basee deep multiple instance learning. In International conference on machine learning, pages 2127-2136. PMLR, 2018.
[16] Neeta Kumar, Ruchika Gupta, and Sanjay Gupta. Whole slide imaging (wsi) pathology: current perspectives and future directions. Journal of digital imaging, 3334):103 2020.
[17] Narmin Ghaffari Laleh, Hannah Sophie Muti, Chiara Maria Lavinia Loeffler, Amelie Echle, Oliver Lester Saldanha, Faisal Mahmood, Ming Y Lu, Christian Trautwein, Rupert Langer, Bastian Dislich, et al. Benchmarking supervied deep learning pipelines for whole slide classification in computational pathology. Medical image analysis, 79: 10244, 2022.

---

[18] Bin Li, Yin Li, and Kevin W Eliceiri. Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recogmition, pages 14318-14328, 2021.
[19] Jiawen Li, Yuxuan Chen, Hongbo Chu, Qiehe Sun, Tian Guan, Anjia Han, and Yonghong He. Dynamic graph representation with knowledge-awan attention for histopathology whole slide image analysis. In Proceedings of the IEEE/CVE Conference on Computer Vision and Pattern Recognition, pages 11323- 11332, 2024.
[20] Jiayun Li, Wenyuan Li, Anthony Sisk. Huihui Ye, W Dean Wallace, William Speier, and Corey W Arnold. A multi-resolution model for histopathology image classification and localization with multiple instance learning. Computers in biology and medicine, 131:104253, 2021.
[21] Xin Li, Dongze Lian, Zhihe Lu, Jiawang Bai, Zhibo Chen, and Xinchao Wang. Graphadapter: Tuning vision-language models with dual knowledge graph. Advances in Neural Information Processing Systems, 36:13448-133 13466, 2023.
[22] Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chenggi Deng, Chenyu Zhang, Chong Ruan, et al. Deepseek-v3 technical report. arXiv preprint arXiv:2412.194377 2024.
[23] Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Richard J Chen, Mattco Barbieri, and Faisal Mahmood. Data-efficient and weakly supervised computational pathology on whole-slide images. Nature biomedical engineering, 5(6):555-570, 2021.
[24] Ming Y Lu, Bowen Chen, Drew FK Williamson, Richard J Chen, Ivy Liang. Tong Ding, Guillaume Jaume, Igor Odintsov. Long Phi Le, Georg Gerber, et al. A visual-language foundation model for computational pathology. Nature Medicine, 30(3):663-88) 874, 2024.
[25] Yuning Lu, Jianzhuang Liu, Yonggang Zhang, Yajing Liu, and Xinmei Tian. Prompt distribution learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5206-
5215, 2022.
[26] Ahmad Obcid, Said Boumaraf, Anabia Sohail, Taimur Hassan, Sajid Javed, Jorge Dias, Mohammed Bennamoun, and Naoufel Werghi. Advancing histopathology with deep learning under data scarcity: A decade in review. arXiv preprint arXiv:2410. 19820, 2024.
[27] So Yeon Park. Hee Eun Lee. Hailun Li, Michail Shipitsin, Rebecca Gelman, and Kornelia Polyak. Heterogencity for stem cell-related markers according to tumor subtype and histologic stage in breast cancer. Clinical Cancer Research, 16(3):876 08766-872228 2010.
[28] Linhao Qu, Xiaoyuan Luo, Shaolei Liu, Manning Wang, and Zhijian Song. Dgmil: Distribution guided multiple instance learning for whole slide image classification. In International conference on medical image computing and cumputer-assistee intervention, pages 24-34. Springer, 2022.
[29] Linhao Qu, Kexue Fu, Manning Wang, Zhijian Song, et al. The rise of ai language pathologists: Exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification. Advances Neural Information Processing Systems, 36:67551 -675644 2023.
[30] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PmI.R, 2021.
[31] Raphael Schäfer, Till Nicke, Henning Höfener, Annkristin Lange, Dorit Merhof, Friedrich Feuerhake, Volkmar Schulz, Johannes Lotz, and Fabian Kiessling. Overcoming data scarcity in biomedical imaging with a foundational multi-task model. Nature Computational Science, 4(7):495 509, 2024.
[32] Zhuchen Shao, Hao Bian. Yang Chen, Yifeng Wang. Jian Zhang, Xiangyang Ji. al. Transmil: Transformer based correlated multiple instance learning for whole slide image classification. Advances in neurul information processing systems, 34:2136-2147 2021.
[33] Yash Sharma, Aman Shrivastava, Lubaina Ehsan, Christopher A Moskaluk, Sana Syed, and Donald Brown. Cluster-to-conquer: A framework for end-to-end multi-instance learning for whole slide image classification. In Medical imaging with deep learning, pages 682-698. PMLR, 2021.
[34] Jiangbo Shi, Chen Li, Tieliang Gong, Yefeng Zheng, and Huazhu Fu. Vila-mil: Dual-scale vision- lasiou-language multiple instance learning for whole slide image classification. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 11248 11258, 2024.
12

---

[35] Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. arXiv preprint arXiv:1710.10090 2017.
[36] Xi Wang, Hao Chen, Caixia Gan, Huangjing Lin, Qi Dou, Efstratios Tsougenis, Qitao Huang, Muyan Cai, and Phnng-Ann Heng. Weakly supervised deep learning for whole slide lung cancer image analysis. IEEE transactions on cybernetics, 50(9):3950-396) 2019.
[37] Yuandou Wang, Neel Kanwal, Kjersti Engan, Chunming Rong, and Zhiming Zhao. Towards privacy- preserving distributed cloud service for preprocessing very large medical images. 2023 IEEE Interna- tional Conference on Digital Health (ICDH), pages 325 327. IEEE, 2023.
[38] An Yang, Baosong Yang. Beichen Zhang, Binyuan Hui. Bo Zheng. Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2. technical report. arXiv preprint arXiv:2412.55115, 2024.
[39] Jiawen Yao, Xinliang Zhu, Jitendra Jonnagaddala. Nicholas Hawkins, and Junzhou Huang. Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. Medical image analysis, 63:101789, 2020.
[40] Hongrun Zhang, Yanda Meng. Yitian Zhao, Yihong Qiao, Xiaoyun Yang, Sarah E Coupland, and Yalin Zheng. Dtfd-mil: Double-tier tier feature distillation multiple instance learning for histopathology whole slide image classification. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 18802 18812, 2022.
[41] Yunlong Zhang, Honglin Li, Yunxuan Sun, Sunyi Zheng, Chenglu Zhu, and Lin Yang. Attention- challenging multiple instance learning for whole slide image classification. In European Conference on Computer Vision, pages 125- 143. Springer, 2024.
[42] Zheng, Rushin H Gindra, Emily Green, Eric Burks, Margrit Betke, Jennifer E Beane, and Vijaya Kolachalama. A graph-transformer for whole slide image classification. IEFE transactions on medical imaging, 41 303303-331,, 2022.
[43] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Conditional prompt learning for vision- language models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 168 16816-16825, 2022.
[44] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision, 13009):237--288 2022.
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Appendix for
"MAPLE: Multi-scale Atribbute-enhanced Prompt Learning for Few-shot Whole Slide Image Classification"
Appendix organization:
Prompt Construction
A.1 Algorithms
21
Examples
22
B Experimental Details
22
I Dataset Details
22
B.2 Descriptions of Compared Methods
24
B.3 Computational Complexity
25
Additional Results
25
C.I
Comparisons with SOTAs using CLIP
25
Comparisons between PLIP and CLIP
26
Comparisons with SOTAs using CONCH
26
4
Comparisons with multi-scale MII, methods
26
More Ablation Studies
27
D.I
Number of Neighbors
27
D.2 Number of Entities
27
Number of Tumor-related Patches
28
D.4 Impact of Lambda
28
D.5 Impact of Large Language Models
29
More Visualization Results
29
Discussion
30
Limitations
30
F22
Broader Impacts
30
A
Prompt Construction
A.1 Algorithms
The detailed prompt construction process described in Section 4.1 can conducted by using an LLM (e.g., GPT-4) outlined in Algorithm [] This includes iterative entity-level prompt discovery, and slide-level prompt synthesis. Specifically, we begin by iteratively expanding pool of histologically meaningful entities, where each selected entity highlights distinct structures relevant to subtype classification. For each entity, the LLM is queried to generate both general visual description and subtype-specific morphological characteristics. These outputs are formatted into structured
21

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Algorithm LLM-powered Prompt Construction
Require: Subtype list C {c1, ........ cc); maximum number of entities Ne.
Ensure: Entity-level prompts P entiyy slide-level prompts slide
1: Initialize entity pool 8x t- 0. prompts P
2: while |89] < <N, do
3:
discriminative histological entity not in & that helps distinguish subtypes in C at scale 8²)
QUERYLLLM(""uugges"
4: Add to 8
5: gen,s + QUeRYLLM generic visual characteristics of at
scale 8*))
6:
for each subtype e E C do
7: Pr.a - QUERYLLM("Describ how e appears subtype at scale s²)
8: end for
9: P FORMATPROMPT(E, gu :PE
10: Append pl to Ponity
11: end while
Initialize Paliee
13: for each subtype C do contect. & COLLECTENTITIESCC, Pe Cattt)
15: Pelide, s + QUERYLLM( a WSI of с at scale s based on: contexta)
Append pe p.lide.s to P slide
17: end for return Peiiiyy Pliie
prompts used for fine-grained entity-level alignment. After constructing the entity-level prompts, we generate slide-level prompts by prompting the L.M to compose holistic WSI descriptions. These are conditioned on the entity attributes associated with each subtype, thereby enriching slide-level prompts with fine-grained context. This approach enables our model to jointly capture both entity- level and slide-level semantic cues essential for few-shot classification.
A.2 Examples
provide examples of the constructed prompts at low and high resolutions on the TCGA-NSCLC
in Fig. and respectively.
B Experimental Details
B.1 Dataset Details
TCGA-BRCA. This dataset contains 1,054 whole-slide images (WSIs) of breast invasive carcinoma (BRCA) collected from TCGA It comprises 843 slides of invasive ductal carcinoma (IDC) and 21 slides of invasive lobular carcinoma (II.C.
TCGA-RCC.
This dataset includes 873 WSIs of renal cell carcinoma (RCC) obtained from TCGA. It consists of 455 chromophobe RCC (CHRCC), 121 papillary RCC (PRCC), and 297 clear cell RCC (CCRCC) slides.
This dataset comprises 1,039 WSIs non-small cell lung cancer (NSCLC) from TCGA, including 530 slides of lung adenocarcinoma (LUAD) and 509 slides of lung squamous cell carcinoma (LUSC).
TCGA-NSCLC.
https://portal.gee cancer gov
22

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Examples
"low":
"entitics":
"name": "Architecture,,
"general feature": "Overall pattemn arrangement of tumor cells and structures within tumor region visible low magnification.",
"attributes": {
"lung adenocarcinoma". "Shows glandular, acinar, or papillary arrangements with well-formed gland-like spaces and frequent areas of lepidic (growth along alveolar spaces) pattern.".
"lung squamous cell carcinoma": "Displays solid nests, sheets, or islands of tumor cells with evidence of keratinization central necrosis (keratin pearls), lacking glandular or papillary structures."
"name": "Stroma",
"general feature": "The connective tissue and extracellular matrix surrounding tumor cell nests: appears fibrous desmoplastic regions.".
"attributes": {
"lung adenocarcinoma": "Typically features prominent desmoplastic reaction with
loose, myxoid, or fibrous stroma separating irregular glandular structures; stroma may appear lighter and less dense low magnification.",
"lung squamous cell carcinoma": "Displays denser and collagenized stroma, often with fewer desmoplastic changes and tendency stromal tissue tightly encase solid sheets nests tumor cells."
"name": "Lumina".
"general feature": "Round emply spaces gland-like areas within tumor cell
nests seen under magnification.",
"attributes": {
"lung adenocarcinomaa: "Frequent presence of well-formed glandular lumina,
sometimes with mucin, demonstrating distinct glandular differentiation in the tumor nests.". *.
"lung squamous carcinoma": "Rare absent lumina; if present, irregular, slit-like, or represent necrotic debris rather than glandular differentiation, with tumor nests generally appearing solid."
Figure 5: Example of the constructed prompts at low resolution on the TCGA-NSCLA dataset.
23

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Examples
"high":
"entities": [
"name": "Cytoplasmic Border",
"general feature": "The outline or edge of the cytoplasm surrounding the nucleus,
often seen as the boundary between adjacent tumor cells.",
"attributes": {
"lung adenocarcinoma:: "Cytoplasmic borders are generally indistinct and poorly defined, with cells ofien demonstrating overlapping poorly separated cytoplasm. Cell cohesion low.".
"lung squamous carcinoma": "Cyloplasmic burders are well-delineated and sharp, with cell-to-cell boundaries. Tumor colls exhibit strong cohesion and polygonal shapes."
"name": "Nucleolus",
"general feature": "Prominent, round oval intranuclear structure, appears
distinct spot within the nucleus under high magnification.",
"attributes": {
"lung adenocarcinoma". "Frequently small, inconspicuous or variably prominent; may be solitary or few per nucleus with generally fine chromatin background.", "lung squamous cell carcinoma": "Typically large, prominent, and sometimes multiple; often eosinophilic with hyperchromatic surrounding chromatin and increased irregularity."
"name": "Keratinization",
"general feature": "Presence and appearance cosinophilic, dense, concentric
cytoplasmic material or whorls within tumor cells.",
"attributes": {
"lung adenocarcinoma": "Typically absent; tumor cells rarely show keratin production or keratin pearls.".
"lung squarnus cell carcinoma": "Frequently present; keratin pearls and dense cosinophilic cytoplasmic material are prominent, often forming concentric whorled structures."
Figure 6: Example of the constructed prompts at high resolution on the TCGA-NSCLC dataset.
B.2 Descriptions of Compared Methods
ABMIL introduces an attention-basse multiple instance learning framework that assigns importance weights to individual instances, enabling the model aggregate instance features into a slide-level representation with improved interpretability.
ABMIL 15].
TransMIL employs a transformer architecture to capture both morphological features and spatial relationships among instances, enabling effective performance across binary and multi-class classification tasks.
TransMIL 32
GTMIL 42
GTMIL proposes graph-based vision transformer that integrates structural WSI representations with transformer-based feature modeling for whole slide image classification.
WiKG conceptualizes WSI dynamic knowledge graph, where neighbor instances and edge embeddings are dynamically constructed based on semantic relationships, enabling context- aware graph reasoning.
WiKG 19
24

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ТОP [29].
TOP introduces two-level prompt learning strategy that incorporates linguistic prior knowledge to guide both instance- and slide-level feature aggregation within vision-language modeling framework.
ViLa-MIL 34]. ViLa-MIL designs dual-scale visual prompts using a frozen LLM to enhance vision-language alignment, effectively boosting few-shot performance pathology tasks.
MSCPT [i MSCPT presents graph-based prompt tuning module to encode contextual depen- dencies across WSI patches, followed by a cross-guided non-parametric aggregation scheme for WSI-level representation learning.
FOCUS [0). FOCUS integrates foundation models and language-guided patch selection to prior- itize diagnostically relevant regions, enabling focused and efficient analysis in weakly supervised settings.
Table 4: Comparisons of model complexity and efficiency. We report the number of trainable parameters (MB). inference time per slide (ms). training time per epoch (s) TCGA-NSCLC dataset under the 16-shot setting.
Trainable Params
Inference Time (ms slide)
Training Time (s (s/cpon epoch)
Methods
TOP 229
1,71 M
40.50 + 4.75
8.91 + 0.31
ViLa-MIL
34
2.32 M
19.03 2.07
5.42 0.20
MSCPT
1.35 M
41.86 + 1.00
6.47 0.40
FOCUS [0
1.32 M
132.41 2.26
30.11 1.93
MAPLE
1.86 M
45.88 + 1.18
10.52 = 0.19
B.3 Computational Complexity
We analyze the computational efficiency of MAPLE in comparison with existing few-shot methods. As shown in Table we report the number of trainable parameters, inference time per slide, and training time per epoch on the TCGA-NSCLC dataset. Among all methods, ViLa-MIL requires the largest number of trainable parameters (2.32M) due to its dual-scale visual prompt tuning approach, though it achieves the fastest inference and training speeds. contrast, FOCUS has relatively small parameter count (1.32M) but incurs the highest computational cost during both inference (132.41 ms/slide) and training (30.11 s/epoch). MSCPT maintains low parameter count and reasonable efficiency but requires an additional preprocessing step to select low-resolution patches, which is not reflected in the runtime measurements. MAPLE strikes balanced trade-off between model complexity and computational efficiency. While it requires moderately more parameters (1.86M) than TOP, MSCPT, and FOCUS, its inference and training times remain comparable to TOP and significantly lower than FOCUS. Overall, MAPLE achieves strong performance in few-shot scenarios without introducing significant additional trainable parameters or increasing much inference and training time.
C
Additional Results
C.1 Comparisons with SOTAS using CLIP
In addition to the main experiments using PLIP [14] as the feature extractor, we conducted parallel experiments using CLIP [30 extract visual and textual features. Tab. presents the few-shot weakly-supervised learning results on the same three datasets (TCGA-BRCA) BRCA, TCGA-RCC. and TCGA-NSCLC) across 4-shot, 8-shot, and 16-shot settings. With features extracted by the CLIP encoder, MAPLE achieves the best results in nearly all configurations across the three datasets. Particularly, the 16-shot setting, MAPLE attains the highest AUC scores of 70. 1,, 94.6%, and 76.0% on TCGA-BRCA, TCGA-RCC, and TCGA-NSCLE respectively. The performance advantage of MAPLE is maintained even in the challenging 4-shot scenario, where it achieves competitive or superior results compared to other methods.
25

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Table 5: Few-shot WSI classification results using CLIP encoder ICGA-BRCA, TCGA-RCC, and TCGA- NSCLC datasets under 4-shot, 8-shot, and 16-shot settings. The best results are in bold, and the second-best results are underlined.
TCGA-BRCA
TOGA-RCU
TOGA-SSCLC
Dataset
Methods
AUC
Fl
ACC
AUC
F1
NCC
AUC'
Fl
ACC
ABMIL
0.549 1 0.083
0,428
a 0.104
0,526 1: 0,173
0,825 4 0.033
0.596
1 0,077
0.597 1. 0.056
0,589 1. 0,052
0,545 2: 0.042
0.557 2 0.057
TrnsMIL
0.560
0,058
0.396
0.107
0.75 0.52
0,482
0.081
0,518
0.050
0.064
0.519 0.048
0.531
GIMIL
0.570 1: 0.108
0,446
1. 0.126
0.456 0.154
0.816 2: 0.005
0.598
1. 0,050
0,006
1. 0.115
0.579 1 0.044
0.476 2. 0.044
0,531 0.027
WIGG
0.581 à: 0.089
0,415 a: 0.036
0615=0000
0,820 : 0045
0.572
a 0,099
0.592 à: 0.121
0.600 0.063
0.567 = 0.154
0,576 0.055
0.573
0.866
0.534
+.111
0.857
ViLa-MIL
0.614 + 0.066
0.499
+ 0.082
0.560
= 0.120
0,806 = 0083
0.525
+ 0.129
0,565 + 0.115
0.665 + 0.080
0.585 0.041
0.606 = 0.066
MSCPT
0.10 0.071
0,426
6: 0,050
0.544
= 0.133
0828 : 0027
0.608
: 0,078
0.613 + 0.079
0.581 1: 0.032
0,685 +: 0.062
0,531
0021
0.602
0.483
0.046
0.23 0.044
0.19
0618 0.105
0.059
0.511
0.111
0,548
MАPLE
0.13 0,000
0,525 :: 6,043
9.637
0.091
6.31 0085
0,85
:: 0,080
0,31 1: 0.086
0,78 a 0.057
0,589 :: 0.086
0.616
0JI11
ABMIL
0.77
0.079
0,435
0.111
0.534
0.198
0,830
0.23
0.618
0,023
0,664
1: 0.048
0,590
0.079
0,548 a: 0.059
0,561
0.099
TransMIL
0.53
00051
0.451 +: 0.071
0.135
0.777 1 0.064
0.068
0,5%
0.071
0.051
0.474
0.078
0.526
GTMIL
0.594
1 0,065
0.496 1 0,024
0.565
0.055
0,877 1 0.013
0.665
1 0.022
0,684
1: 0.020
0.699 1 0.100
0,550
1: 0.050
0,563 à. 0.051
S-hoot
WIKG
0.622
0.033
0.504
0,080
0.119
0,874
0,043
0,090
0.046
0.09
0.545
0.053
TOP
0.632
0.067
0.536
0.115 0.548
0.113
0.756 + 0.34
0.580
0.065
0.625
0.059
0.020
0.461
0.078
0,813
ViLa-MIL
0.645
à 0,099
0,537
à: 0,134
0.558
=: 0.174
0,865 : 0.022
0.685 :: 0.041
0.714 0:044
0.52 + 0.136
0.406
+ 0.112
0.615 + 0.24
MSCPT
0.620
0.524
0.048
0.04
0.507
0.072
0,531
0.090
FOCUS
0.641
1 0.074
0,541 1 0.064
0,605
a 1.0
0,888 1 0.029
0,695
1. 0.037
0,719 1: 6.044
0.599 1 0.088
0,567
1: 0.072
0.595
0.064
MAPLE
0.688
0,872
0.566 + 0.045
0.631
0.882
(1886 + 0.025
0.689
+ 0.018
0,708 0.048
0.668 + 0,043
0,612 + 0.055
0.620 0.042
0.629
0.536
0,059
0.05
0.618
TranMMIL
0,590 1: 0.001
0,464 1. 0.114
0.537 1 0.169
0,850 2: 0.029
0.587 1. 0.127
0,614 1. 0.115
0.084 1 0.016
0,599 1: 0.033
0,618
0.027
GTMIL
0.652
0.082
0.532
:: 0,053
0,595 0.092
0,917 0.226
0.757
0,045
0,066 1: 0.054
0.693 0.037
0.632 :: 0.036
0.615 0036
WIKG
0.685
0,067
0,515
0.098
0.558
0.130
0.926 1 0,010
0.757
0.041
0:777 0.034
0.680 : 0.031
0.684
TOP
0.630
+ 0.053
0.483
# 0.102
0.505
= 0.160
0,914 = 0,000
0.745 :: 0.024
0.760 + 0.042
0.672 + 0.063
0,518 = 0.846
0.624 = 0.043
ViLo
0.690
0,047
0.533
0,083
0.106
0.33
0.779
0,026
0.774 0.024
0.057
0.674 0.062
0
MSCPT
0.669
0.073
0.540
+ 0.050
0.601
0.092
0.925 = 0.010
0.755
0.048
0.773
0.048
0.056
0.658
0.043
0.675
FOCTS
662
+ 0.073
0.528
e 0.065
0.575
: 0.076
0.91 : 0012
0.789 : 0.000
0:788 1: 0.020
0.732 à 0.050
0,666
:: 0.050
0.659 0.048
0,701
0,149
0.997
0.025
0.027
0.687
0
C.2 Comparisons between PLIP and CLIP
Comparing the results obtained with CLIP (Tab. and PLIP (Tab. I in the main paper), we observe that PLIP generally provides better feature representations for pathology images, resulting in higher overall performance across all metrics and settings. This observation is consistent with previous studies [T suggesting that PLIP, which is pre-trained specifically on pathology images, captures more relevant pathological features than the general-pure purppss CLIP model.
C.3 Comparisons with SOTAS using CONCH
We provide further results with stronger pathology VLMs such as CONCH on the three datasets (TCGA-BRCA, TCGA-RCC and TCGA-NSCICC in Tab. 6 MAPLE consistently outperforms baseline methods across different datasets and few-shot settings. Specifically, in the 16-shot setting, MAPLE achieves the highest AUC scores of 91.6%, 98.4%, and 98.1 % on TCGA-BRCA, TCGA- RCC, and TCGA-NSCLC, respectively. In the challening 4-shot setting, MAPLE maintains its superior performance with AUC scores of 84.4%, 94.7% and 88.9%. In summary, MAPLE can achieve consistently superior prediction results under different vision- ssion-language foundation models (e.g., CLIP, PLIP, CONCH). highlighting the advantages of our MAPLE to jointly integrate multi- scale visual semantics and perform prediction at both the entity and slide levels.
C.4 Comparisons with multi-scale MIL methods
We further compare MAPLE with representative multi-scale MIL baselines, including DTFD- MIL 40). Dual-Stream-MIL [8] and Cross-Scale MIL [8]. As shown in Table (7) MAPLE con- sistently surpasses these approaches under all the settings across different datasets, highlighting the advantage of our multi-scale prompt-guided VLL--baeed method. These results further confirm that the performance gain of MAPLE arises not merely from multi-scale integration, but from the combination of multi-scale modeling and language-guide prompt supervision via vision- languaee models.
26

---

Table 6: Few-shot WSI classification results using CONCH encoder on TCGA-BRCA, TCGA-RCC, and TCGA- NSCLC datasets under 4-shot, 8-shot, and 16-shot settings. The best results are in bold, and the second-best results are underlined.
Dutaset
Methods
TCGA-BRCA
TCGA-ROC
TCGA-NSCIC FT
ALC
Fl
ACC
AUC
F1
ACC
ALC
ARMIL
0.770 +: 0.000 0.591 0.070
0.688
0.105
0.934 + 0.026
0.755 + 0.063
0.773 + 0.0K2
0.522 + 0.041
0.714 + 0.049
0.721 + 0.044
TranMILL
0.757
0602
0.111
16722 0:136
0.938 0.099
0.761
0.088
0.778
0.099
0.43 0.059
0.756
0.125
0.762 0.109
CTTMI..
0.728++0.000
0.551 + 0.994
0..0+00.141
0923 + 0.051
0.343 + 0.070
0.765 + 0.054
0.86 0.090
0.750 + 0.050
0.754 + 0.061
4-4bot
WIKG
0665
0651
09925 0.010
0.76
0.03M
0.777
0.136
0.800 0,067
0.730
0.733 0.054
0.03
TOP
0.228
0.130 0.583
+.38.00.000
0916 0.03
0.343
0.0057
0.758
+ 0.059
0816+0.00
0.63 0681++0.190 0.707 0.092
ViLa-MIL
0.733
d: 0.100
0,590 h: 0.110
0.35 0.130
0.919 à 0.030
0.768
à 0.055
0.793
à 0.051
0
0.759
a: 0.051 0.756 à: 0.082
MSCPT
0.782
0660
0.072
0.33 1 0.019
0.770
0.057
0.785
0.050
0842 0.059
0.730
0.748
POCUS
0.115
0.632++0.11
0.667 0.166
0.930 + 0.033
0.767
+ 0.077
0.790
+ 0.065
0.83 + 0.077
0.762 +: 0.0K0 0.769 + 0.061
MAPLE
0.444
0:947 0.017
0.791
0.086
0.805
0.069
0,889
0.774
0.786
ARMILL
0857
0.049
0,705
0.057
0.763 0.088
0941 + 0.013
0885
0.055
0844
0.047
0,925 0.013
0835
0.021
0835
TranMILL
0.853 1. 0.044
0.710 1 0.044
0.771
1 0.016
0.980 1 0.012
0.840
1 0.030
0.851 1 0.024
0.916 0.010
0.821 1: 0.027
0.821 1 0.027
0.050
0.006
0928
0.020
M-shont
WiKG
0851
0.045
0.685
0.850
0.241 + 0.050
0.47 + 0.012
0.34 + 0.025
08855 + 0.030
0.19 00007
0.834 0.021
0.834 + 0.020
TOP
1859:10.000
0.701
0.050
0.758
0.054
0929 1 0.020
0814 1 0.019
0.828 1: 0.035
0.908 0,043
08817
0818 0.067
VilaMMn
0.061
0.226
0.11
0.776
+ 0.090
0,44 + 0.008
0885 + 0.037
0888 + 0.032
0.04
0.86+00001
0857 0.05
MSCPT
0.00
0.720
+: 0.132
0.774 0.136
0.930 + 0.011
0.849 +: 0.043
01851
+: 0.041
0
0.849 =: 0.055
(1849 +: 0.055
POCUS
0.000
0.719
0.347+ 0.145
0.990 0.008
0.875
MAPLE
0,900 = 0,082
0L748
0,110
(L797 + 0.120
0L971 + 0.011
0.8SS + 0.025
0,899 + 0.023
0,964 + 0.032
0L894 + 0.053
(L394 + 0.034
ADMIL
0.876
0.017
0,759
0.020
0,789
0.018
0.954 1 0.00
0.857
1 0.009
0.859
1 0.007
0.935
0,013
0.865
0,021
0.865
0.021
TrnnMILL
0.844
0.030
0.761
0.057
U.795
0.057
0LUSS à 0.003
0.854
0.030
0.860
:: 0.013
0.926
0.014
0.851
0.027
0.851
GTMIL
0.891
0.025
0.770
0.073
0,803
0.083
0.962 1 0.004
0.865
1 0.008
0.875
1. 0.013
0.938 0.016
0.874 1: 0.020
0.874 1 0.020
16-4hot
WIKG
0.013
0.018
0.010
1:0.03
0.021
TOP
0.867
1 0.011
0.768
0.016
0.790 1: 0.015
0.94 1 0.003
0.829
1. 0.027
0,835 1. 0.027
0.924 1: 0.013
0.859
0.025
0.859 0.025
VIL=MIL
0.902
0.033
0,775
0.038
0812
0.042
0.966 1 0.006
0.862
1 0.024
0.872
1. 0.016
0.941 0.023
0.877
0,028
0,871
0.017
MSCPT
0.018 0.894
0.767
0.027
0.808
0.034
0.958 1 0.004
0,859
0.021
0.025
0.934 0.017
0.866
0.031
0.867
0.031
TOCUS
0.017 0.893
0.764
1 0.041
0,805 0.042
0.974 1 0.006
0.881
1 0.045
0.891 1 0.051
0.961 1 0.007
0.891 1 0,052
0.895 0,050
MAPLE
Table 7: Comparisons of few-shot WSI classification results using multi-scale MIL methods on RCC, and TCGA-NSCLC datasets under 4-shot, 8-shot, and 16-shot settings.
Methods
BRGA
TCGA-RCC
TOGA-SSCLC
AUC
Fl
ACC
AUC
FI
ACC
AUC
FI
ACC
D/TED-MIL
0.530
0.05
0.590
0.626 0.057
0.000
0,555 0,511
0.06
Dusl Strrom MIL.
0.00
0.02
0,522
0112
0A23-
0023-01100
0877 5: 0,009
0.084 a: 0,037
0.63
0,036
0.589 1 0.121
0.564 à 0.00
0,90
0.000
Cmoso-Scale Mil..
0.73
- 0.97
0.55
0051
0615
-0098
0.876 H 0,019
0.667
+ 0.017
01600
0.099
0.661 + 0.101
0,560 + 0.991
0.885 6 0.044
MAPLE
0.722
0.063
0.591
0.644
0.134
0,999 0,020 0.705
0,855
0,728
0.057
6,740 0,056 0,663
0,852
DIED
0.33
0.048
0.516
00051
0.056
0.722 0,336
0,748
0.046
0.632 à 0,0022
0.082
Sunl-n MIL.
0.758
0.003
0.568
0.071
0.576
0.058
0.926 :: 0,025
0.761
4: 0.344
0.759 :: 0.087
0.752 :: 0.074
0,651 4: 0.032
0.667 à: 0.034
Cross Scale MIL
0.756
= 0.062
0.554
0.063
0,588 = 0.075
0.924 0,023
0.757
2: 0.028
0.001 0.782
0,748 0.037
0,645
1: 0,075
0.657 0,071
MAPLE
0.018
0.024
6.762
DIFDD-MIL
0.04k
0.633
0.064
0.679
0.088
0.919
0.019
0,762
à: 0.051
0.799
0.040
0.812 0.047
0.742
0.044
0.043
Dusl Stroom MIL.
0752 = 0.08
0.666
00059
0.690
(1952
019466
a 0,014
0885
8: 0,000
01877
0.002
0,824
0.299
0,960 & 0.25)
0.702 0,033
CrocoSeasle Mil. 0259 - 0.852
0.60
-
0.055
0.688
(1950
(1948
0.019
08815
+ 0.094
(1830
0.015
0.890
0.42.
0.764
+ 0.028
0.768 0.028
MAPLE
6.031
0.672
0.0
0,014
0.83
0,034
0.867
6.001
0033
0,806
0,600
D More Ablation Studies
D.1 Number of Neighbors
To analyze the effect of the number of neighbors used in the cross-scale entity graph, we set n& E (1,3,5, 5, 7,7,, 7,9, 11, 13). As shown in Fig. 7 the performance improves n& increases, and achieves the best result at ng of 7. It demonstrates the importance of contextual information propagation between semantically related entities. However, further increasing n& leads to slight degradation, likely due to over- over-smoothing caused by excessive message propagation across weakly related entities.
D.2 Number of Entities
We further investigate how the number of selected entities per scale affects the results. As illustrated in Fig. 8 increasing the number of entities from 4 to 8 improves the performance, indicating the importance of providing enough entities to capture different fine-grained subtype-specific patterns. Beyond entities, performance plateaus and even slightly decreases, suggesting that additional entities may lack discriminative phenotypic attributes relevant to cancer subtype classification. These
27

---

TCGA-BRCA
TCGA-RCC
TCGA-NSCLC
0.72
0/41
0.01
0.10
0.00
0.50
Metriz2
1.:44
0.70
0.52
6:76
0.00
11
12
0.75
Number of Neighbors
Number Neichbors
Number of Neighbors
(a)
(b)
(c)
Figure 7: Impact of the number of neighbor nodes across three datasets under the 16-shot setting.
TCGA-BRCA
TCGA-RCC
TCGA-NSCLC
0.32
0:41
0.82
3.70
0.00
Motrics
Metrics
0.68
6.78
0.72
3.0
6.76
3:00
18
0:75
28
0.75
13
30
Number Entities
Numbee of Entities
Number of Entilie
(b)
Figure 8: Impact of the number of entities across three datasets under the 16-shot sctting.
redundant entities do not contribute to improving the model's performance but increase computational complexity and potentially introduce noise that interferes with the model's s decision-making process.
D.3 Number of Tumor-related Patches
To assess the effects of the number of tumor-related patches, we vary r from 0.1 to 0.9, As shown in Fig. 9 performance improves steadily as increases from 0.1 to 0.7, demonstrating the importance of incorporating sufficient tumor-related patches for accurate diagnosis. Further increasing r yields the slight performance degradation. This decline can be attributed to the inclusion of less informative or non-tumor regions that compromise the quality of the selected patch set, introducing noise that interferes with entity-level feature extraction.
D.4 Impact of Lambda
To balance contributions from entity-level and slide-level classification, we vary the fusion weight from 0 to I in increments of 0. (Fig. 10). Results indicate that À = 0.3 offers the best trade-off, confirming the importance of leveraging both entity-level and slide-level information for few-shot WSI classification.
TCGA-BRCA
TCGA-RCC
TCGA-NSCLCC
0.74
0.02
0.72
0.1
Metric
Metrics
0.64
0.79
0.00
3.00
0.72
0.64
082
0.6
35
Top Srr Tumor-related Patches
Top 9r Tumor-related Patches
Top Krr Tumar-nelated Patches
(a)
(b)
(c)
Figure 9: Impact of the number of tumor-related patches across three datasets under the 16-shot setting.
28

---

TCGA-BRCA
TCGA-RCC
TCGA-NSCLC
3.72
FESON
0.1
11 2M
Metrics
Metrics
0.79
0.82
086
0.70
Lambda
Lamoda
Lambda
(a)
(b)
(c)
Figure 10: Impact of À across three datasets under the 16-shot setting.
Table 8: Results of different large language models on the three datasets under the 16-shot setting.
LLMs
AUC
TCGA-BRCA
F1
ACC
AUC
TCGA-RCC Fl
ACC
AEC
TOGA-NSCLC
Fi
ACC
Claude 3.5 Sonnet
0.786 1: 0.031
0.67 1 0.083
0.719 a 0.107
0,966 1 0.015
0.22 a 0.022
0.55 1: 0,073
3,866 1 0.009
0,791 a 0.133
0.795 1: 0,013
Owen2.5 5
08)
0,795 1 0.041
0.644
0.031
0.730
a 0.084
0
1 0.017
0811
1 0,55
03:5
1 1.0855
0883
1 0.018
0,790
0021
0.794 1: 0.021
Deepoees V5 P2
0,799
1 0.027
0.665
0.019
0.12
1 0.045
0,968
1 0.013
0,83
1 0038
0363
1 0.0007
0.93
1 0,027
0,813
0.12
0.817 1 0,012
GPT-
0 0.031
0672 1 6.076
0.738 : 0,839
8,909 : 0.014
0,88
0.31
0.867 0.003
0.03 1 0.033
0,806
0.000
0310 0.055
D.5
Impact of Large Language Models
To investigate how the choice large language model affects performance of MAPLE, we evaluate
four LLMs for prompt construction: Claude 3.5 Sonnet, Qwen2.5 (38], Deepseek-V2 V3 [22], and
GPT-4 [2]. For each LLM, we use it to generate both entity-level and slide-level prompts following
the same query templates. We present the results the three datasets under 16-shot setting in Tab, 8
As shown in Tab. B all four LLMs deliver strong performance across the three datasets, indicating the robustness of method to I.LMs. GPT-4 achieves the best overall results, particularly on TCGA-BRCA and TCGA-RCC datasets, while Deepseek-V? performs comparably and even slightly outperforms GPT-4 on TCGA-NSCLE in terms of F1 score and accuracy. Qwen2.5 and Claude 3.5 Sonnet also demonstrate competitive performance, with marginally lower metrics compared to GPT-4 and Deepseek-V3 V3. The findings also indicate that LLMs with stronger language modeling capabilities can further enhance performance of MAPLE.
Ently: Claster (Lov-rendation)
Lardy
LEUAALL-ASS
TERGA-AG-AZIK
Altribute
Tummr calle firm eobrave oods or linggla delters
Tomorodlecrre engle-tie linax stends scall, aut-colliivs
Eaity:: Call Adhndde (1ighnneletion)
Erite-elizii
Enahy-elazd
10k
Attribatt
Attributc
Timor odlk diclyy emong crlleneell rébeson Sogansly forning velesvee grapp
Timer cilloypcllly echihi eehini redered or dodd col-t-eel alooies
Figure 11: Visualization of relevant patches selected by the entity- guided cross- coss-attentiin module for invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) on the TCGA-BReS dataset. Top rows show patches and their corresponding entity attributes (e.g., cell cluster) low resolution, while bottom rows show patches and their corresponding entity attributes (e.g., cell adhesion) at high resolution.
E More Visualization Results
In this section, provide more visualization results of entities and their relevant patches the TCGA-BRCA and TCGA-RCC Fig. II and 12 respectively.
As shown in Fig. [] at low resolution, cell cluster-related patches from invasive ductal carcinoma (IDC) consistently form cohesive nests, while those from invasive lobular carcinoma (II.C) are arranged in small, non-cohesive groups Similarly, at high resolution, cell adhesion-related patches

---

from IDC display strong cell-to-cell adhesion, whereas ILC patches exhibit reduced or absent intercellular adhesion. These visualizations align with the subtype-specific attributes of these entities described in our L..M-generate prompts.
We observe similarly patterns in the multi-class TCGA-RCC dataset. As illustrated in Fig. 12 at low resolution, stroma-related patches exhibit clear subtype-specific characteristics: clear cell renal cell carcinoma (CCRCC) shows delicate and inconspicuous stroma with highly vaseular background; chromophobe renal cell carcinoma (CHRCC) displays dense, hyalinized fibrous stroma with less prominent blood vessels; and papillary renal cell carcinoma (PRCC) features prominent fibrovascular cores supporting papillary fronds with abundant stroma. At high resolution, aacuole-related patches from CCRCC displays prominent, large vacuoles; patches from CHRCC exhibits multiple small, well-defined cytoplasmic vacuoles; while patches from PRCC shows small, inconspicuous vacuoles that are typically less pronounced. These visualizations confirm that MAPLE effectively captures the histological entities and their attributes for subtype classification.
TOGA KO-S0W
TOGA--QQ-T49
CCRCO
CHACO
PREE
Attribatc
Attritatc Donie kndiniid (Tosso strens Meed veuds proczent
Altibutc
Defastcrr woo.gigneeeeeeeeeeeee sbore bit5 mestle mukprraad
Pronsece, Gbooooll oxce sepporiing petion evera teabendarr
Eairy waadppat
Aftribut
Aftributt
Aftribati
cmpty Pousiant, kapg. uptinilly copto--rraann apaaaninggng www.eeteccogrggg...o.o. cytephos
Makipli welkkiisnl oytepariic vocella
Seall inceaptcaa sepaoooss
rytcally
Visualization of entity-relevant patches selected by the cutity-guidee cross cross-attention attention module for clear cell renal cell carcinoma (CCRCC), chromophobe renal cell carcinoma (CHRCC) and papillary renal cell carcinoma (PRCC) the TCGA-RCC dataset. Top rows show patches and their corresponding entity attributes (e-g., stroma) low resolution, while bottom rows show patches and their corresponding entity attributes (e.g.. vacuole) at high resolution.
Figure 12:
Discussion
F.1 Limitations
The construction of entity and attribute prompts currently relies solely on LLMs such as GPT-4. Although LLMs provide rich semantic priors, they may introduce hallucinations generate clinically irrelevant descriptions. This may limit the reliability of the derived prompts real-world settings. In future work, aim to incorporate domain expertise from pathologists into the prompt design process. This could involve human-in-the-loop strategies for verifying or refining entity-attribute relationships, integrating expert-annotated diagnostic criteria guide prompt construction.
E.2 Broader Impacts
MAPLE has the potential to significantly impact clinical pathology practice and cancer diagno- sis workflows. By providing accurate few-shot WSI classification with interpretable entity-level predictions, our method could help address critical challenges computational pathology: First, MAPLE could reduce the annotation burden for pathologists by enabling accurate diagnosis with limited labeled examples, particularly valuable for rare cancer subtypes where collecting large labeled datasets is challenging. Additionally, the hierarchical framework may also enhance collaboration between Al systems and pathologists by providing entity-level interpretations that align with human diagnostic reasoning.
30


Reviews:
[{"review_id": "a2zSiKM11f", "reviewer": "Reviewer_TGVt", "summary": "The work presents prompting-based learning for few-shot WSI classification. An LLM is used to form prompts at both the entity level and the slide level, at both low and high resolutions. A cross-attention mechanism is used to extract features for each entity, and cosine similarity is then computed with subtype-specific prompts to generate logits for classification. A Graph Attention Network is employed to account for correlations among various entities. Additionally, slide-level representation is formed in a similar manner, where the LLM is prompted to identify slide-level features. Slide-level logits and entity-level logits are then averaged using a parameter lambda to produce the final logits. The effectiveness of the proposed method is evaluated on three WSI datasets across three few-shot settings.", "questions": "1) Please include baseline results for ABMIL and TransMIL using state-of-the-art pathology foundation models such as Virchow 2 or UNI2-h in Table 1, evaluated across the three datasets and all three few-shot settings. The PLIP model used in this study is a relatively much weaker feature extractor. \n\n2) Provide results for the 32-shot, 64-shot, and fully supervised (all-label) settings, and compare them with the existing baselines in Table 1. These results will offer a clearer perspective on the generalizability of the proposed method across varying levels of label availability.\n\n3) Include a comparison of the proposed method with ConcepPath across multiple few-shot settings (Table 1) to highlight differences in performance.\n\n4) Provide an ablation study evaluating the effect of using different VLMs, such as CONCH or QuiltNet, on the performance of both the proposed method and baseline methods, at least on one representative dataset.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1) The method is clearly and effectively presented.\n\n2) MAPLE explores the use of scale-specific prompts, which is intuitive and well-motivated.\n\n3) The integration of the Graph Attention Network (GAT) is both interesting and effective.\n\n4) Exhaustive ablation studies are provided for each component of the method.\n\n\nWeaknesses:\n\n1) While the proposed method depends on a VLM-based pathology foundation model, the baselines using traditional MIL approaches such as ABMIL and TransMIL could benefit from stronger image-only foundation models like Virchow 2 or UNI2-h. The study lacks a fair comparison in which MIL methods are trained using these state-of-the-art pathology feature encoders.\n\n2) All experiments are conducted with at most a 16-shot setting, which limits the generalizability of the proposed approach to more realistic scenarios where around fifty to several hundred WSIs per class may be available.\n\n3) The paper lacks any comparison with the ConcepPath method, which is highly relevant as it also decomposes slide-level prompts into concept-level prompts and proposes a guided aggregation strategy. Although ConcepPath was developed under a fully supervised setting, it can be readily adapted to few-shot scenarios as well.", "was_revised": true, "final_justification": "I thank the authors for the thorough rebuttal.", "rebuttal": "We thank the reviewer for the comments and for the time spent reviewing our paper. We address the weaknesses (W) and questions (Q) as follows:\n\n---\n\n>**[W1 and Q1] SOTA pathology foundation models for MIL baselines.** \n\nWe agree that traditional MIL approaches like ABMIL and TransMIL could indeed benefit from stronger image-only foundation models such as Virchow 2 or UNI2-h. In our main experiments (Table 1 and 5), we use the same feature extractor (i.e., PLIP and CLIP) across all baselines and MAPLE to ensure a fair comparison. To address the reviewer's concern, we additionally conduct the experiment including ABMIL and TransMIL with stronger image-only foundation models (e.g., UNI2-h), and MAPLE with a stronger VLM (e.g., CONCH). As shown in the table below, we can derive the following observations. Firstly, both ABMIL and TransMIL with UNI2-h exhibit noticeable performance gains, validating the importance of encoder strength. Secondly, MAPLE with CONCH can consistently beat the MIL based methods with latest image-only foundation model (i.e., UNI2-h), demonstrating the scalability and effectiveness of our method. Moreover, we compare MAPLE with all baseline methods under CONCH for a fair comparison in the response to [Q4]. MAPLE achieves the best performance across different few-shot settings and datasets, highlighting its effectiveness under different VLMs. In summary, these findings confirm that while backbone strength contributes to overall performance, MAPLE consistently delivers superior results.\n||Methods|AUC(RCC)|F1(RCC)|ACC(RCC)|AUC(BRCA)|F1(BRCA)|ACC(BRCA)|AUC(NSCLC)|F1(NSCLC)|ACC(NSCLC)|\n|-|-|-|-|-|-|-|-|-|-|-|\n|**4-shot**|ABMIL-UNI2|0.787±0.045|0.604±0.073|0.651±0.085|0.932±0.012|0.763±0.031|0.781±0.031|0.806±0.054|0.704±0.029|0.713±0.028|\n||TransMIL-UNI2|0.779±0.075|0.587±0.198|0.631±0.228|0.941±0.017|0.768±0.046|0.785±0.058|0.815±0.078|0.719±0.089|0.729±0.081|\n||MAPLE-CONCH|0.844±0.109|0.653±0.126|0.695±0.166|0.947±0.017|0.791±0.086|0.805±0.069|0.889±0.055|0.774±0.032|0.786±0.033|\n|**8-shot**|ABMIL-UNI2|0.855±0.030|0.710±0.068|0.742±0.070|0.953±0.008|0.861±0.024|0.872±0.020|0.927±0.023|0.819±0.038|0.830±0.038|\n||TransMIL-UNI2|0.847±0.052|0.701±0.062|0.730±0.073|0.961±0.002|0.870±0.011|0.878±0.014|0.915±0.045|0.808±0.068|0.811±0.065|\n||MAPLE-CONCH|0.900±0.082|0.748±0.110|0.797±0.120|0.971±0.011|0.888±0.025|0.899±0.023|0.964±0.032|0.894±0.033|0.894±0.034|\n|**16-shot**|ABMIL-UNI2|0.884±0.031|0.767±0.048|0.790±0.040|0.962±0.003|0.865±0.012|0.868±0.005|0.961±0.011|0.891±0.023|0.897±0.023|\n||TransMIL-UNI2|0.897±0.026|0.772±0.104|0.804±0.121|0.969±0.002|0.874±0.011|0.879±0.012|0.949±0.026|0.885±0.036|0.885±0.036|\n||MAPLE-CONCH|0.916±0.024|0.790±0.029|0.825±0.030|0.984±0.006|0.910±0.016|0.919±0.015|0.981±0.005|0.914±0.050|0.914±0.047|\n\n>**[Q2 and W2] Generalizability beyond 16-shot settings.**\n\nFor few-shot WSI classification, we follow prior works (e.g., MSCPT and FOCUS) and set the shots as 4, 8 and 16. We agree that evaluating the performance under larger-shot settings (e.g., 32-shot and 64-shot) is helpful for assessing MAPLE to more realistic scenarios where more WSIs per class are available. To this end, we conduct additional experiments under 32-shot and 64-shot settings, and present the results in the table below. As shown, MAPLE continues to outperform existing baselines, demonstrating its generalizability under larger-shot settings. Due to character limitation, we report the fully supervised results directly in the text. Specifically, our MAPLE achieves the best AUC of 0.903, 0.990 and 0.966 across three datasets, demonstrating significant improvements over the second-best method FOCUS with AUC of 0.880, 0.975 and 0.955.\n||Methods|AUC(RCC)|F1(RCC)|ACC(RCC)|AUC(BRCA)|F1(BRCA)|ACC(BRCA)|AUC(NSCLC)|F1(NSCLC)|ACC(NSCLC)|\n|-|-|-|-|-|-|-|-|-|-|-|\n|**32-shot**|ViLa-MIL|0.830±0.029|0.704±0.018|0.775±0.023|0.954±0.014|0.806±0.020|0.837±0.023|0.892±0.017|0.809±0.018|0.809±0.018|\n||MSCPT|0.816±0.038|0.670±0.044|0.729±0.052|0.949±0.014|0.801±0.054|0.828±0.051|0.899±0.028|0.823±0.036|0.823±0.036|\n||FOCUS|0.826±0.021|0.696±0.032|0.769±0.050|0.961±0.009|0.836±0.037|0.849±0.032|0.909±0.028|0.850±0.028|0.851±0.028|\n||MAPLE|0.849±0.025|0.727±0.050|0.784±0.065|0.975±0.015|0.855±0.049|0.869±0.038|0.926±0.022|0.856±0.029|0.857±0.029|\n|**64-shot**|ViLa-MIL|0.859±0.025|0.715±0.044|0.772±0.048|0.966±0.003|0.861±0.012|0.884±0.012|0.924±0.006|0.851±0.010|0.851±0.010|\n||MSCPT|0.849±0.014|0.687±0.036|0.742±0.040|0.953±0.005|0.857±0.018|0.864±0.013|0.928±0.013|0.853±0.021|0.854±0.021|\n||FOCUS|0.852±0.017|0.710±0.035|0.772±0.036|0.967±0.004|0.864±0.014|0.885±0.013|0.930±0.018|0.859±0.015|0.861±0.014|\n||MAPLE|0.873±0.025|0.733±0.027|0.796±0.034|0.980±0.001|0.889±0.018|0.899±0.014|0.942±0.031|0.876±0.029|0.876±0.029|\n\n>**[W3 and Q3] Comparison with ConcepPath.**\n\nWe thank the reviewer for pointing out the relevance of ConcepPath. We report the results of ConcepPath in the table below. MAPLE consistently outperforms ConcepPath across different datasets and few-shot settings, highlighting the effectiveness of our method in few-shot WSI classification.\n||Methods|AUC(RCC)|F1(RCC)|ACC(RCC)|AUC(BRCA)|F1(BRCA)|ACC(BRCA)|AUC(NSCLC)|F1(NSCLC)|ACC(NSCLC)|\n|-|-|-|-|-|-|-|-|-|-|-|\n|**4-shot**|ConcepPath|0.672±0.031|0.555±0.091|0.605±0.086|0.877±0.026|0.654±0.057|0.676±0.043|0.657±0.045|0.601±0.053|0.604±0.031|\n||MAPLE|0.722±0.063|0.594±0.076|0.664±0.134|0.909±0.020|0.705±0.055|0.728±0.057|0.740±0.056|0.663±0.052|0.675±0.053|\n|**8-shot**|ConcepPath|0.759±0.072|0.571±0.093|0.635±0.142|0.920±0.032|0.753±0.068|0.774±0.066|0.750±0.034|0.668±0.033|0.680±0.035|\n||MAPLE|0.786±0.070|0.618±0.024|0.673±0.018|0.957±0.015|0.791±0.024|0.806±0.024|0.855±0.041|0.762±0.031|0.766±0.030|\n|**16-shot**|ConcepPath|0.764±0.068|0.631±0.073|0.694±0.069|0.938±0.031|0.817±0.035|0.835±0.032|0.831±0.053|0.753±0.046|0.756±0.039|\n||MAPLE|0.801±0.031|0.672±0.076|0.735±0.039|0.969±0.014|0.838±0.034|0.867±0.031|0.903±0.033|0.806±0.060|0.810±0.055|\n\n>**[Q4] Evaluating the effect of using different VLMs.**\n\nIn addition to the experiments using CLIP and PLIP in the original paper, we provide further results with stronger pathology VLMs such as CONCH in the table below. MAPLE consistently outperforms baseline methods across different datasets and few-shot settings, highlighting the effectiveness of our method. We will include these results in the revised paper.\nFurthermore, we conduct a comprehensive ablation study evaluating the impact of different pathology VLMs including QuiltNet, PLIP, and CONCH, as detailed in our response to [W3 and Q2] of Reviewer yzxQ.\n||Methods|AUC(RCC)|F1(RCC)|ACC(RCC)|AUC(BRCA)|F1(BRCA)|ACC(BRCA)|AUC(NSCLC)|F1(NSCLC)|ACC(NSCLC)|\n|-|-|-|-|-|-|-|-|-|-|-|\n|**4-shot**|ABMIL|0.770±0.069|0.591±0.070|0.648±0.105|0.924±0.026|0.755±0.063|0.773±0.062|0.832±0.041|0.714±0.049|0.721±0.044|\n||TransMIL|0.757±0.127|0.602±0.111|0.672±0.126|0.938±0.019|0.761±0.098|0.778±0.099|0.848±0.059|0.756±0.125|0.762±0.109|\n||GTMIL|0.728±0.102|0.552±0.094|0.630±0.141|0.923±0.031|0.743±0.070|0.765±0.054|0.836±0.093|0.750±0.080|0.754±0.081|\n||WiKG|0.768±0.109|0.605±0.105|0.651±0.123|0.925±0.010|0.761±0.038|0.777±0.036|0.820±0.087|0.730±0.084|0.733±0.083|\n||TOP|0.728±0.170|0.583±0.117|0.638±0.105|0.916±0.033|0.743±0.057|0.758±0.053|0.816±0.066|0.683±0.130|0.707±0.092|\n||ViLa-MIL|0.783±0.108|0.590±0.110|0.635±0.130|0.919±0.030|0.768±0.058|0.793±0.051|0.853±0.073|0.759±0.081|0.756±0.082|\n||MSCPT|0.782±0.087|0.605±0.072|0.632±0.088|0.931±0.019|0.770±0.057|0.785±0.050|0.842±0.059|0.730±0.068|0.748±0.066|\n||FOCUS|0.810±0.115|0.632±0.135|0.667±0.160|0.930±0.032|0.767±0.075|0.780±0.065|0.875±0.077|0.762±0.060|0.769±0.061|\n||MAPLE|0.844±0.109|0.653±0.126|0.695±0.166|0.947±0.017|0.791±0.086|0.805±0.069|0.889±0.055|0.774±0.032|0.786±0.033|\n|**8-shot**|ABMIL|0.857±0.049|0.705±0.057|0.763±0.058|0.941±0.013|0.835±0.055|0.844±0.047|0.925±0.013|0.835±0.021|0.835±0.021|\n||TransMIL|0.853±0.044|0.710±0.044|0.771±0.046|0.940±0.012|0.840±0.030|0.851±0.024|0.916±0.010|0.821±0.027|0.821±0.027|\n||GTMIL|0.861±0.052|0.711±0.068|0.780±0.065|0.945±0.006|0.845±0.020|0.857±0.016|0.928±0.016|0.844±0.020|0.844±0.020|\n||WiKG|0.851±0.045|0.685±0.050|0.741±0.060|0.947±0.012|0.834±0.025|0.855±0.020|0.919±0.007|0.834±0.021|0.834±0.020|\n||TOP|0.859±0.028|0.701±0.050|0.758±0.054|0.929±0.020|0.814±0.039|0.828±0.035|0.908±0.043|0.817±0.068|0.818±0.067|\n||ViLa-MIL|0.880±0.081|0.726±0.117|0.776±0.090|0.945±0.008|0.835±0.037|0.858±0.032|0.934±0.037|0.856±0.051|0.857±0.051|\n||MSCPT|0.882±0.091|0.720±0.132|0.774±0.136|0.950±0.011|0.849±0.043|0.851±0.041|0.924±0.043|0.849±0.055|0.849±0.055|\n||FOCUS|0.875±0.060|0.719±0.114|0.747±0.145|0.959±0.008|0.871±0.033|0.875±0.031|0.949±0.030|0.873±0.043|0.873±0.042|\n||MAPLE|0.900±0.082|0.748±0.110|0.797±0.120|0.971±0.011|0.888±0.025|0.899±0.023|0.964±0.032|0.894±0.033|0.894±0.034|\n|**16-shot**|ABMIL|0.876±0.017|0.759±0.020|0.789±0.018|0.954±0.004|0.857±0.009|0.859±0.007|0.935±0.013|0.865±0.021|0.865±0.021|\n||TransMIL|0.884±0.030|0.761±0.057|0.795±0.057|0.955±0.003|0.854±0.010|0.860±0.013|0.926±0.014|0.851±0.027|0.851±0.027|\n||GTMIL|0.891±0.025|0.770±0.073|0.803±0.083|0.962±0.004|0.865±0.008|0.878±0.013|0.938±0.016|0.874±0.020|0.874±0.020|\n||WiKG|0.882±0.013|0.762±0.019|0.796±0.018|0.957±0.010|0.839±0.031|0.856±0.031|0.939±0.007|0.864±0.021|0.864±0.020|\n||TOP|0.887±0.011|0.768±0.016|0.790±0.015|0.944±0.003|0.829±0.027|0.835±0.027|0.924±0.013|0.859±0.025|0.859±0.025|\n||ViLa-MIL|0.902±0.033|0.775±0.038|0.812±0.042|0.966±0.006|0.862±0.024|0.872±0.016|0.941±0.023|0.877±0.028|0.877±0.017|\n||MSCPT|0.894±0.018|0.767±0.027|0.808±0.024|0.958±0.004|0.859±0.021|0.871±0.025|0.934±0.017|0.866±0.031|0.867±0.031|\n||FOCUS|0.893±0.017|0.764±0.041|0.805±0.042|0.974±0.006|0.884±0.045|0.891±0.051|0.964±0.007|0.894±0.052|0.895±0.050|\n||MAPLE|0.916±0.024|0.790±0.029|0.825±0.030|0.984±0.006|0.910±0.016|0.919±0.015|0.981±0.005|0.914±0.050|0.914±0.047|"}, {"review_id": "VGqa5NKuVf", "reviewer": "Reviewer_dMRb", "summary": "This paper addresses the limitation of existing few-shot WSI classification methods that lack fine-grained, entity-level prompts. It proposes a Multi-scale Attribute-enhanced Prompt Learning approach, which incorporates both slide-level and entity-level prompts. The method fully leverages the capabilities of large language models (LLMs) throughout the process. Furthermore, an entity graph is constructed to enhance instance features and derive entity-level representations. Finally, the predictions from both the entity-level and slide-level are integrated to produce the final classification result.", "questions": "please refer to Weaknesses", "limitations": "please refer to Weaknesses", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. To achieve prompt learning aligned with fine-grained features, this paper designs components such as entity name extraction, hierarchical entity graphs, and an entity-guided cross-attention module—all of which are validated through ablation studies.\n 2. By introducing entity-level prompt learning, the paper enables more interpretable predictions at the entity level.\n\nWeaknesses:\n1. The font size in Figure 1 is too small and difficult to read; it is recommended to enlarge the text for better readability.\n2. In Figure 2, the meaning of the arrows between the entity-level features and the learnable prompts is unclear. It is suggested to add symbols or annotations to clarify their purpose.\n3. How does the use of LLM ensure comprehensive and accurate extraction of entity names? Is there a possibility of incomplete or missing entity names? If the extraction is not sufficiently comprehensive, will it affect model performance, and how is this issue addressed?\n4. In the Region Selection module, is there a high degree of overlap among the selected instances, meaning that most image patches come from the same region? Is this phenomenon beneficial or detrimental to the final model performance?", "was_revised": true, "final_justification": "The authors’ responses have addressed most of my concerns. I will maintain my score.", "rebuttal": "We thank the reviewer for the comments and for the time spent reviewing our paper. We address the weaknesses (W) as follows:\n\n---\n\n>**[W1] The font size in Figure 1 is too small and difficult to read.**\n\nWe thank the reviewer for pointing this out. We will revise Figure 1 by enlarging the font size and optimizing the layout to ensure clarity and readability in the final manuscript.\n\n>**[W2] In Figure 2, the meaning of the arrows between the entity-level features and the learnable prompts is unclear.**\n\nWe appreciate the reviewer’s suggestion. The arrows represent the computation of similarity scores between the extracted entity-level features and the learnable prompts. We will revise Figure 2 by adding appropriate annotations to clarify their meaning and ensure interpretability.\n\n>**[W3] Reliability and completeness of entity extraction by LLMs.**\n\nWe thank the reviewer for this insightful comment. To ensure comprehensive and accurate extraction of entities, we carefully design the LLM instructions in the Prompt Construction stage to prioritize entities with higher clinical importance. For example, we incorporate prompts such as \"iteratively suggest a discriminative histological entity not in the current entity set\" (see Appendix B.1 for details), thereby reducing the likelihood of missing critical entities. This prioritization encourages the LLM to focus on highly discriminative entities.\n\nTo further evaluate the accuracy of the selected entities, we examine the entities generated from GPT4 by querying different LLMs (e.g., Claude 3.5 Sonnet, DeepSeek-V3 and Qwen2.5) using the prompt: \"Do you think [ENTITY] is a key entity relevant to distinguishing different subtypes in [DATASET]?\" All of these LLMs affirm the relevance and importance of the generated entities, which can demonstrate the accuracy of selected entities.\n\nFurthermore, as shown in our ablation study on the number of entities (Figure 8 in the original paper), increasing the number of entities beyond a certain threshold does not improve performance and may even slightly decrease. This suggests that redundant entities may lack discriminative phenotypic attributes relevant to cancer subtype classification and potentially introduce noise that hinders the model's decision-making process. This observation supports the completeness and effectiveness of our selected entity set.\n\nAs acknowledged in our Limitations section, we recognize that LLM-generated entities may still suffer from hallucinations or generate clinically irrelevant descriptions in the absence of expert curation. To mitigate this, we propose the following future directions:\n- Incorporating expert review or clinical guidelines to refine and validate the extracted entities;\n- Aggregating outputs from multiple LLMs (e.g., a mixture-of-experts approach) to achieve more robust and reliable entity sets and descriptions rather than relying solely on a single model.\n\nWe believe these strategies will enhance the reliability and clinical applicability of LLM-driven entity construction, and we plan to explore them in future versions of MAPLE.\n\n>**[W4] Patch overlap and the region selection module.**\n\nThe Region Selection module in MAPLE is designed to accurately identify tumor-relevant regions while suppressing irrelevant background areas, thereby guiding the model to focus on informative patches that align well with the entity-level prompts. Since patch instances are partitioned in a non-overlapping manner and the region selection ratio is set to r = 0.7, the selected patches can effectively cover a variety of heterogeneous tumor-related regions within a WSI, and thus do not exhibit a high degree of spatial redundancy or overlap. We also investigate the impact of r in our ablation study (Section E.3 of the main paper). As shown in Figure 9, higher values of r (e.g., r > 0.7) yields the slight performance degradation. This decline can be attributed to the inclusion of less informative or non-tumor regions that compromise the quality of the selected patch set. In contrast, lower values (e.g., r < 0.7) tend to overly concentrate on a few localized regions, resulting in performance degradation."}, {"review_id": "UyoZtUFB8G", "reviewer": "Reviewer_RUWi", "summary": "Few-shot WSI classifiers often miss fine-grained, subtype-specific histological variations crucial for accurate diagnosis. To remedy this, the authors introduce MAPLE, which uses a frozen language model to generate both entity-level prompts for detailed histological attributes and slide-level prompts for global tissue context. A cross-attention module guided by these prompts, together with a cross-scale entity graph, refines and aligns multi-resolution features before aggregating them for prediction, boosting both accuracy and interpretability.", "questions": "1. Prompt reliability seems crucial for this study. How did the authors generate prompts that prevent hallucinations or the omission of clinically critical attributes? It is helpful to provide results with and without expert correction to quantify the performance gains from text guidance. The authors also claim that MAPLE’s hierarchy mirrors diagnostic practice, yet they offer no user study or expert evaluation / references to validate this alignment. \n\n2. Although qualitative examples show that selected patches visually match the LLM-described attributes, there are no region-level ground-truth annotations with heatmap visualizations (or IoU / Dice metrics) to demonstrate slide-level alignment. \n\n3. Both the low-resolution-only and high-resolution-only models perform nearly as well as the multi-scale model, which only marginally outperforms the single-scale variants. How can we assess that each scale provides complementary information rather than redundant features? Furthermore, how can we ensure that 10× magnification captures sufficient cellular morphology detail compared with 20× or 40×?\n\n4. The paper does not compare MAPLE against existing multi-scale MIL methods [1, 2, 3]. Could the authors include these baselines to isolate the benefits of their language-guided enhancements?\n\n[1] DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification\n[2] Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning\n[3] Cross-scale multi-instance learning for pathological image diagnosis", "limitations": "MAPLE depends on uncurated LLM prompts, risking hallucinations without expert oversight. It lacks pixel-level annotations for rigorous, quantitative region–prompt validation, relying instead on indirect ablation and qualitative examples.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "1. Strengths:\n\n(1) MAPLE consistently outperforms leading MIL and prompt-learning baselines across different few-shot settings on multiple datasets.\n\n(2) By fusing entity-level features from both low and high magnification with slide-level cues, MAPLE surpasses single-scale variants. Ablation studies show that every key module (language-guided instance selection, entity-guided cross-attention, and cross-scale graph learning) contributes positively.\n\n(3) With just less trainable parameters and inference/training times comparable to simpler baselines, MAPLE strikes a practical balance between accuracy and efficiency.\n\n2. Weaknesses:\n\n(1) Prompt quality depends entirely on raw LLM descriptions, which can hallucinate or omit clinically crucial details. The authors note this limitation and suggest future human-in-the-loop refinement.\n\n(2) Without ground-truth entity labels, the paper lacks directly quantification on how well selected patches match textual prompts—relying instead on indirect ablation and qualitative figures.\n\n(3) Although the paper highlights gains from multi-scale integration, it doesn’t benchmark against established multi-scale MIL architectures, leaving it unclear how much improvement stems from language cues versus simply using multiple resolutions.", "was_revised": true, "final_justification": "The author addressed all my concerns with explicit illustrations and supporting experiments, which have improved the quality of the manuscript. Therefore, I would like to increase my score and recommend accepting the paper.", "rebuttal": "We thank the reviewer for the comments and for the time spent reviewing our paper. We address the weaknesses (W) and questions (Q) as follows:\n\n---\n\n>**[W1 and Q1] Prompt reliability and MAPLE’s hierarchy.**\n\nWe appreciate the reviewer’s insightful comments regarding the reliability of LLM-generated prompts and the clinical plausibility of MAPLE’s hierarchical design. \n\nRegarding the first point, we address this concern in detail in our response to [W3] of Reviewer dMRb. In current step, we carefully design the LLM instructions in the Prompt Construction stage and evaluate the reliability of GPT-4 outputs by querying multiple independent LLMs (e.g., Claude 3.5 Sonnet, DeepSeek-V3, and Qwen2.5), aiming to minimize the potential impact of hallucinations and increase the trustworthiness of the extracted entities. In future work, we plan to incorporate expert correction and clinical guidelines to further refine and validate entity construction.\n\nOn the second point, MAPLE’s hierarchical design is motivated by real-world diagnostic workflows. We clarify this hierarchy and provide more explanations to validate it. Specifically, the hierarchy in MAPLE is reflected in two aspects:  multi-scale (high magnification and low resolution) and multi-level (entity-level and slide-level) design: \n* One the one hand, at high magnification, pathologists analyze cellular components such as nuclear pleomorphism and cytoplasmic features for cancer diagnosis. As to the image at low magnification, they distinguish different cancer subtypes by examining tissue architecture such as gland formation and tumor-stroma interfaces [1][2]. \n* On the other hand, clinical diagnosis is rarely based on isolated observations. Instead, pathologists synthesize cues from multiple key tissue entities (e.g., mitotic activity, necrosis, glandular structures) and integrate them with global tissue context to reach a diagnosis [3]. For instance, necrosis alone may be inconclusive, but when co-occurring with abnormal mitoses and specific inflammatory patterns, the combination supports malignancy or tumor subtyping [4]. Our design emulates this diagnostic reasoning by jointly modeling localized entity-level and global slide-level descriptions.\n\n[1] Kumar et al. Whole slide imaging (wsi) in pathology: current perspectives and future directions. Journal of digital imaging, 33(4):1034–1040, 2020.\\\n[2] Li et al. A multi-resolution model for histopathology image classification and localization with multiple instance learning. Computers in biology and medicine, 131:104253, 2021.\\\n[3] Heba et al. A comprehensive review of the deep learning-based tumor analysis approaches in histopathological images: segmentation, classification and multi-learning tasks. Cluster Computing 2023.\\\n[4] Wesseling et al. The pathological diagnosis of diffuse gliomas: towards a smart synthesis of microscopic and molecular information in a multidisciplinary context[J]. Diagnostic Histopathology, 2011, 17(11): 486-494.\n\n>**[W2 and Q2] Lack direct quantification on how well selected patches match textual prompts.**\n\nTo the best of our knowledge, public WSI datasets typically lack spatial annotations that directly correspond to textual entities, making it infeasible to compute IoU or Dice metrics. In our current setup, we follow common practice adopted by prior works [1, 2], where semantic alignment is indirectly validated by visualizing the top-k patches with the highest attention scores from the entity-guided module and confirming that they visually reflect the corresponding textual descriptions.\n\nTo further address the reviewer’s concern and provide quantitative insights, we additionally compute the similarity scores between selected patches and their corresponding entity descriptions. Specifically, for each entity, we select the top-50 and bottom-50 patches based on the similarity scores from entity-guided attention, and then use the CONCH model to compute similarity scores with the entity description and the patches from different sets. Finally, we can derive the average scores of the top-50 and bottom-50 patches. We report the scores for the examples (Figure 4 of the original paper) in the table below. The results confirm that the selected patches align well with their associated textual descriptions, further validating the effectiveness of our method.\n\n||Stroma (TCGA-55-6970 from LUAD)|Nucleolus (TCGA-55-6970 from LUAD)|Stroma  (TCGA-58-A46M from LUSC)|Nucleolus  (TCGA-58-A46M from LUSC)|\n|-|-|-|-|-|\n|top-50 score|0.32|0.34|0.25|0.27|\n|bottom-50 score|0.15|0.12|0.08|0.07|\n\n[1] Lu et al. Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images. CVPR 2023.\\\n[2] Jaume et al. Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction. CVPR 2024. \n\n>**[Q3] Complementarity of multi-scale features and selection of magnifications.**\n\nMAPLE is designed to capture distinct histological entities at different magnifications. Different resolution levels in WSIs naturally correspond to different semantic scales: low magnification (e.g., 5×) reveals tissue architecture and global organizational patterns, while high magnification (e.g., 10×) focuses on cellular morphology and nuclear detail. These features are inherently complementary rather than redundant. As demonstrated in Table 2 of the original paper, integrating both scales consistently outperforms using either scale alone. To further validate this observation, we perform the paired t-test comparing multi-scale and single-scale results. The resulting p-values are consistently below 0.05, confirming that the performance gains from multi-scale integration are statistically significant.\n\nOur resolution choices follow ViLa-MIL [1], which also employs dual-scale inputs at 5× and 10× for few-shot slide-level classification. We agree that higher magnification (e.g., 20×) may offer finer morphological cues. To explore this, we conduct experiments using 20× as the high resolution, and find that 10× and 20× perform comparably, suggesting that both magnifications can effectively capture high-resolution histological features. Considering that 20× images significantly increase memory usage and computational cost, we adopt 10× in our main experiments to achieve a balance between performance and efficiency.\n\n|Dataset|Methods|AUC(RCC)|F1(RCC)|ACC(RCC)|AUC(BRCA)|F1(BRCA)|ACC(BRCA)|AUC(NSCLC)|F1(NSCLC)|ACC(NSCLC)|\n|-|-|-|-|-|-|-|-|-|-|-|\n|**4-shot**|MAPLE-20×|0.714±0.052|0.583±0.118|0.647±0.181|0.904±0.027|0.691±0.075|0.717±0.080|0.739±0.061|0.654±0.121|0.666±0.074|\n||MAPLE-10×|0.722±0.063|0.594±0.076|0.664±0.134|0.909±0.020|0.705±0.055|0.728±0.057|0.740±0.056|0.663±0.052|0.675±0.053|\n|**8-shot**|MAPLE-20x|0.784±0.075|0.621±0.092|0.680±0.109|0.958±0.013|0.796±0.060|0.813±0.062|0.847±0.073|0.751±0.088|0.760±0.082|\n||MAPLE-10×|0.786±0.070|0.618±0.024|0.673±0.018|0.957±0.015|0.791±0.024|0.806±0.024|0.855±0.041|0.762±0.031|0.766±0.030|\n|**16-shot**|MAPLE-20x|0.796±0.034|0.688±0.077|0.746±0.098|0.960±0.012|0.831±0.019|0.857±0.021|0.909±0.041|0.793±0.022|0.805±0.023|\n||MAPLE-10×|0.801±0.031|0.672±0.076|0.735±0.039|0.969±0.014|0.838±0.034|0.867±0.031|0.903±0.033|0.806±0.060|0.810±0.055|\n\n[1] Shi et al. Vila-mil: Dual-scale vision-language multiple instance learning for whole slide image classification. CVPR 2024.\n\n>**[W3 and Q4] Comparison against existing multi-scale MIL methods.**\n\nWe appreciate the reviewer’s suggestion and conduct additional experiments to include existing multi-scale MIL baselines such as DTFD-MIL, Dual-Stream MIL, and Cross-Scale MIL under the same 5×/10× magnification settings used in MAPLE. As shown in the table below, MAPLE consistently outperforms these baselines across all datasets, highlighting the advantage of our multi-scale prompt-guided VLM-based method. These results further confirm that the performance gain of MAPLE arises not merely from multi-scale integration, but from the combination of multi-scale modeling and language-guide prompt supervision via vision-language models.\n\n|Dataset|Methods|AUC(RCC)|F1(RCC)|ACC(RCC)|AUC(BRCA)|F1(BRCA)|ACC(BRCA)|AUC(NSCLC)|F1(NSCLC)|ACC(NSCLC)|\n|-|-|-|-|-|-|-|-|-|-|-|\n|**4-shot**|DTFD-MIL|0.648±0.050|0.520±0.065|0.593±0.068|0.869±0.031|0.626±0.057|0.660±0.070|0.624±0.049|0.555±0.051|0.581±0.046|\n||Dual-Stream MIL|0.669±0.052|0.562±0.132|0.623±0.162|0.877±0.039|0.664±0.037|0.693±0.036|0.649±0.122|0.564±0.094|0.590±0.080|\n||Cross-Scale MIL|0.673±0.097|0.552±0.061|0.615±0.068|0.876±0.019|0.667±0.037|0.690±0.039|0.651±0.103|0.560±0.093|0.586±0.084|\n||MAPLE|0.722±0.063|0.594±0.076|0.664±0.134|0.909±0.020|0.705±0.055|0.728±0.057|0.740±0.056|0.663±0.052|0.675±0.053|\n|**8-shot**|DTFD-MIL|0.733±0.048|0.546±0.061|0.603±0.086|0.907±0.024|0.722±0.036|0.748±0.046|0.729±0.040|0.632±0.042|0.652±0.030|\n||Dual-Stream MIL|0.758±0.073|0.548±0.071|0.576±0.088|0.926±0.025|0.761±0.041|0.789±0.037|0.752±0.074|0.651±0.032|0.667±0.034|\n||Cross-Scale MIL|0.756±0.062|0.554±0.063|0.588±0.075|0.924±0.023|0.757±0.028|0.782±0.031|0.748±0.037|0.645±0.075|0.657±0.071|\n||MAPLE|0.786±0.070|0.618±0.024|0.673±0.018|0.957±0.015|0.791±0.024|0.806±0.024|0.855±0.041|0.762±0.031|0.766±0.030|\n|**16-shot**|DTFD-MIL|0.738±0.044|0.623±0.064|0.679±0.088|0.919±0.019|0.762±0.051|0.799±0.040|0.812±0.047|0.742±0.044|0.747±0.042|\n||Dual-Stream MIL|0.752±0.038|0.636±0.059|0.696±0.062|0.946±0.014|0.813±0.030|0.827±0.032|0.824±0.029|0.760±0.037|0.762±0.032|\n||Cross-Scale MIL|0.759±0.052|0.632±0.055|0.698±0.060|0.948±0.019|0.815±0.034|0.830±0.035|0.830±0.042|0.764±0.029|0.768±0.028|\n||MAPLE|0.801±0.031|0.672±0.076|0.735±0.039|0.969±0.014|0.838±0.034|0.867±0.031|0.903±0.033|0.806±0.060|0.810±0.055|"}, {"review_id": "EGbRPquZ60", "reviewer": "Reviewer_yzxQ", "summary": "This work leverages large language models (LLMs) to generate both entity-level and slide-level prompts, integrating fine-grained and coarse-grained visual and textual information for few-shot whole-slide image classification. The proposed MAPLE method consistently outperforms existing SOTA models in this field. The integration of entity-level knowledge is promising to boost few-shot classification performance, especially in the era of LLMs.", "questions": "1. Reliability of using LLMs to generate knowledge-required descriptions for entities. Although it’s common practice to utilize LLMs for prompt or diagnosis description generation, it would be better to demonstrate the reliability of the generated prompts. For example, do they accurately describe the slides’ morphology of a certain cancer? I see that Table 6 provides an ablation of the impact of using different LLMs for prompt generation. One thing I’d like to discuss further is that existing works mostly conduct this kind of ablation, yet sometimes the results are not consistent due to different environments or settings. For example, in FOCUS, they show that Claude demonstrates the strongest performance for prompt generation. This kind of difference leads to difficulty in choosing appropriate LLMs in clinical scenarios. Could the authors share some insights regarding this problem?\n2. As mentioned in the Weaknesses part, could the authors provide more explanation regarding the choice of using PLIP as the feature extractor (although there is a comparison between PLIP and CLIP, CLIP achieves even worse performance)? There have been many powerful pathology foundation models (or feature extractors) in recent years, such as UNI [1], GPFM [2], and Virchow [3]. A major concern here is that existing models might use different feature encoders to extract features, for example, FOCUS uses CONCH [4] as the visual encoder. Thus, this lack of sufficient ablation study on feature encoders could lead to unfair comparisons.\n3. Meanwhile, from Table 1, it could be seen that the results are relatively low compared to ones reported from other works. Still take FOCUS for example, the 16-shot classification results of TCGA-NSCLC are around 0.95 AUC for several models. Yet in this work, the same task under the same few-shot setting only achieves around 0.9 AUC. I believe this also stems from the choice of feature extractor, i.e., PLIP.\n4. Could the authors explain more about the visualization details in section 4.3? Are the patches shown for each entity selected from the ones with the highest attention score? \n5. An additional question is that, if the entity can be precisely identified, then what’s the point of using the global description and WSI-level representation for further integration and analysis?\n\n[1] Chen, Richard J., et al. \"Towards a general-purpose foundation model for computational pathology.\" Nature Medicine 30.3 (2024): 850-862.\n\n[2] Ma, Jiabo, et al. \"Towards a generalizable pathology foundation model via unified knowledge distillation.\" arXiv preprint arXiv:2407.18449 (2024).\n\n[3] Vorontsov, Eugene, et al. \"A foundation model for clinical-grade computational pathology and rare cancers detection.\" Nature medicine 30.10 (2024): 2924-2935.\n\n[4] Lu, Ming Y., et al. \"A visual-language foundation model for computational pathology.\" Nature Medicine 30.3 (2024): 863-874.", "limitations": "N/A", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "### Strengths:\n1. This work introduces entity-level descriptions generated from LLMs to provide fine-grained information for visual-textual alignment.\n2. The performance of the proposed MAPLE model is promising, surpassing existing SOTAs.\n3. Experiments are relatively solid, covering three cancer cohorts from the TCGA dataset.\n\n### Weaknesses:\n1. Visuals in this work (for example, Figure 1) are not precise. For instance, the third paragraph of Introduction mentioned that “TOP [38] introduces instance-level phenotypic prompts to guide patch aggregation into slide-level features, while ViLa-MIL [43] leverages learnable visual prototypes to guide the fusion process of patch features and considers dual-scale visual descriptive text prompt to boost the performance.”, however, Figure 1(a) only illustrates the slide-level similarity computation process, which is too simplified and does not correspond to the texts. What’s more, last paragraph of Introduction mentions “MAPLE … across different scales, as illustrated in Fig. 1”, however, Fig. 1 clearly does not contain multi-scale visualization. \n2. Similar concerns are in the texts as well. For example, the second paragraph of Section 2.2 describes the learning process of few-shot WSI analysis. However, it is way too simplified and only mentions the slide-level similarity computation for logits generation. Most existing works computes the logits more than this, such as ViLa-MIL [1] and FOCUS [2], which also apply patch-level similarity computation.\n3. Lack of certain ablation studies. It is mentioned that PLIP is used as the feature extractor for the visual branch of MAPLE, however, as mentioned in FOCUS [2] (Figure 2 Ablation Study on Foundation Models), PLIP typically performs the worst in few-shot WSI classification. The choice of using PLIP is not well explained. Please refer to the Questions section for more related details.\n\n[1] Shi, Jiangbo, et al. \"ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.\n\n[2] Guo, Zhengrui, et al. \"Focus: Knowledge-enhanced adaptive visual compression for few-shot whole slide image classification.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.", "was_revised": true, "final_justification": "After carefully reviewing the authors' response and other reviewers' comments, I believe the revised manuscript after rebuttal would satisfy NeurIPS's standards.", "rebuttal": "We thank the reviewer for the comments and for the time spent reviewing our paper. We address the weaknesses (W) and questions (Q) as follows:\n\n---\n\n>**[W1] Clarification on Figure 1.**\n\nWe thank the reviewer for pointing this out. Actually, Figure 1 is intended to emphasize the key difference in alignment strategy between our MAPLE and the existing studies (e.g., TOP and ViLa-MIL), where the existing methods only perform the slide-level alignment (shown in Fig.1(a)), while our method considers additional entity-level features and incorporates subtype-specific phenotypic attributes for more interpretable and precise alignment (shown in Fig.1(b)). Since multi-scale modeling also appears in methods like ViLa-MIL, we do not explicitly distinguish this in the figure, but note it in the caption. We will further revise the figure and related text in the final version to avoid confusion.\n\n>**[W2] Concerns on texts in Section 2.2.**\n\nWe acknowledge that different methods may adopt varying strategies to compute logits, often involving additional architectural components and complex aggregation strategies. However, the purpose of Section 2.2 is to provide a concise and coherent overview of the general paradigm, rather than to detail the full implementation of all existing approaches. Therefore, we believe that our formulation captures the essential inference process commonly adopted in prompt-based few-shot WSI classification. We would be happy to further clarify any part of this formulation if needed.\n\n>**[Q1] Reliability of LLM and insights of choosing appropriate LLMs.**\n\nWe acknowledge the reviewer's concern regarding the reliability of LLM-generated prompts, and we provide the detailed discussion in the response to [W3] of Reviewer dMRb. To assess the reliability of our generated prompts, we examine the entities generated from GPT4 by querying different LLMs (e.g., Claude 3.5 Sonnet, DeepSeek-V3 and Qwen2.5) using the prompt: \"Do you think [ENTITY] is a key entity relevant to distinguishing different subtypes in [DATASET]?\" All of these LLMs affirm the relevance and importance of the generated entities, which can demonstrate the accuracy of selected entities.\n\nFor practical usage of choosing appropriate LLMs in clinical scenarios, we suggest selecting recently released LLMs that benefit from improved medical knowledge coverage and instruction-following ability. In addition, combining different outputs from multiple LLMs and incorporating domain expert verification may offer a more robust and trustworthy solution than relying solely on a single model. We consider a more systematic exploration of the reliability and choice of different LLMs is an important direction for future work.\n\n>**[W3 and Q2] The choice of using PLIP and CONCH as VLMs.**\n\nVision-language models (VLMs) have experienced explosive growth in the visual domain, however, CLIP is still widely regarded as a standard backbone for few-shot learning research [1][2]. In this work, we further explore the applicability of CLIP and its variant in pathology domain, PLIP, as our backbone. PLIP retains the architecture of CLIP while being fine-tuned on large-scale pathology-specific image-text data, making it suitable for investigating the effectiveness of prompt learning in few-shot WSI classification. Compared to CONCH, which is based on the more complex CoCa architecture, PLIP has lighter parameters and a structure more consistent with CLIP, facilitating direct and fair comparisons.\n\nWe appreciate the reviewer’s suggestion to consider other strong pathology foundation models such as UNI, GPFM, and Virchow. However, we note that these are image-only models that do not include a compatible text encoder, and therefore cannot be directly integrated into our prompt-learning pipeline, which relies on vision–language alignment. To this end, we instead conduct the ablation study using pathology VLMs, including CLIP-based methods (QuiltNet, PLIP) and the CoCa-based method (CONCH). As shown in the table below, CONCH provides the best classification performance as it is a stronger foundation model.\n\nNext, for the purpose of illustrating the advantage of our MAPLE under different foundation models, we also compare it with all baselines using CONCH as the backbone. The experimental results shown in the response to [Q4] of Reviewer TGVt clearly verify the advantage of our MAPLE for few-shot WSI classification. We will include these CONCH-based results in the revised paper.\n\nIn summary, we can observe that the choice of different VLM backbones could affect the few-shot WSI classification results. However, our MAPLE can achieve consistently superior prediction results under different vision-language foundation models (e.g., CLIP, PLIP, CONCH), highlighting the advantages of our MAPLE to jointly integrate multi-scale visual semantics and perform prediction at both the entity and slide levels.\n\n|Dataset|Methods|AUC(RCC)|F1(RCC)|ACC(RCC)|AUC(BRCA)|F1(BRCA)|ACC(BRCA)|AUC(NSCLC)|F1(NSCLC)|ACC(NSCLC)|\n|-|-|-|-|-|-|-|-|-|-|-|\n|**4-shot**|MAPLE-QuiltNet|0.688±0.063|0.560±0.050|0.630±0.068|0.914±0.017|0.720±0.060|0.748±0.061|0.728±0.063|0.641±0.086|0.651±0.064|\n||MAPLE-PLIP|0.722±0.063|0.594±0.076|0.664±0.134|0.909±0.020|0.705±0.055|0.728±0.057|0.740±0.056|0.663±0.052|0.675±0.053|\n||MAPLE-CONCH|0.844±0.109|0.653±0.126|0.695±0.166|0.947±0.017|0.791±0.086|0.805±0.069|0.889±0.055|0.774±0.032|0.786±0.033|\n|**8-shot**|MAPLE-QuiltNet|0.760±0.107|0.595±0.075|0.653±0.090|0.948±0.016|0.794±0.041|0.824±0.038|0.832±0.049|0.742±0.044|0.748±0.040|\n||MAPLE-PLIP|0.786±0.070|0.618±0.024|0.673±0.018|0.957±0.015|0.791±0.024|0.806±0.024|0.855±0.041|0.762±0.031|0.766±0.030|\n||MAPLE-CONCH|0.900±0.082|0.748±0.110|0.797±0.120|0.971±0.011|0.888±0.025|0.899±0.023|0.964±0.032|0.894±0.033|0.894±0.034|\n|**16-shot**|MAPLE-QuiltNet|0.791±0.027|0.660±0.033|0.715±0.033|0.964±0.009|0.846±0.032|0.884±0.027|0.891±0.039|0.805±0.052|0.806±0.052|\n||MAPLE-PLIP|0.801±0.031|0.672±0.076|0.735±0.039|0.969±0.014|0.838±0.034|0.867±0.031|0.903±0.033|0.806±0.060|0.810±0.055|\n||MAPLE-CONCH|0.916±0.024|0.790±0.029|0.825±0.030|0.984±0.006|0.910±0.016|0.919±0.015|0.981±0.005|0.914±0.050|0.914±0.047|\n\n[1] Zeng et al. Local-Prompt: Extensible Local Prompts for Few-Shot Out-of-Distribution Detection. ICLR 2025.\\\n[2] Pan et al. NLPrompt: Noise-Label Prompt Learning for Vision-Language Models. CVPR 2025.\n\n>**[Q3] Results in Table 1 are relatively low, which stems from the choice of feature extractor.**\n\nWe appreciate the reviewer’s observation. We agree that CONCH is a significantly stronger foundation model compared to PLIP, which may partly explain the AUC difference observed in Table 1 of original paper. To address this, we conduct additional experiments using CONCH as the visual encoder, with results presented in the response to [Q4] of Reviewer TGVt. Notably, MAPLE still achieves the best performance under this stronger backbone, reinforcing the effectiveness and generalizability of our method across different VLMs. We will include these CONCH-based results in the revised paper. \n\n>**[Q4] Explanation on visualization details in Section 4.3.**\n\nThe visualized patches are selected based on attention scores from the entity-guided cross-attention module. For each entity, we rank the attention scores and visualize the top-k scoring patches, which reflect the most relevant regions associated with that entity. We will clarify this detail explicitly in Section 4.3 of the revised paper.\n\n>**[Q5] The point of using the global description and WSI-level representation if the entity can be precisely identified.**\n\nIn MAPLE, entity-level prompts are designed to capture fine-grained and localized entities. However, WSIs are inherently heterogeneous, and accurate diagnosis often requires integrating evidence from multiple spatial and semantic cues, rather than relying on isolated entities alone. For instance, the presence of necrosis alone may not be diagnostically conclusive. However, when necrosis co-occurs with abnormal mitotic activity and specific inflammatory patterns, the combination strongly supports malignancy or tumor subtyping [1]. In such cases, global representations are essential for capturing diagnostic dependencies and interrelationships across the entire slide. Based on the above consideration, our slide-level representation is designed specifically to provide a holistic view that aggregates entity features. As confirmed in Table 2 of the main paper, MAPLE combined with entity-level and slide-level representations consistently outperforms using either alone, highlighting the effectiveness of both entity-level and slide-level information.\n\n[1] Wesseling et al. The pathological diagnosis of diffuse gliomas: towards a smart synthesis of microscopic and molecular information in a multidisciplinary context[J]. Diagnostic Histopathology, 2011, 17(11): 486-494."}]

Author rebuttal:

Meta-review: This paper proposes MAPLE, a multi-scale attribute-enhanced prompt learning framework for few-shot WSI classification. By combining entity-level prompts (generated via LLMs) with slide-level prompts, and refining features through entity-guided cross-attention and cross-scale graph learning, MAPLE achieves consistent improvements across multiple cancer datasets and few-shot regimes.

The paper is well-motivated and supported by extensive ablations. Reviewers initially raised concerns about fairness of baselines (stronger encoders, larger-shot settings, comparisons with ConcepPath and multi-scale MIL methods), clarity of figures, and reliability of LLM-generated prompts. The authors provided comprehensive additional experiments and clarifications during rebuttal, which convincingly addressed these points. While prompt reliability and lack of direct region–prompt alignment remain limitations, they are acknowledged with clear future directions.

Overall, reviewers converged towards acceptance after rebuttal, with multiple raising scores. I recommend accept as a solid and meaningful contribution, though not at spotlight/oral level given the prompt reliability and limited clinical validation.
Decision: Accept (poster)