SCL-WC: Cross-Slide Contrastive Learning for Weakly-Supervised Whole-Slide Image Classification
Authors: Xiyue Wang
Jinxi Xiang
Jun Zhang
Sen Yang
Zhongyi Yang
Ming-Hui Wang
Jing Zhang
Yang Wei
Junzhou Huang
Xiao Han
Venue: NeurIPS 2022 Accept (2022)
Keywords: Histopathology
Whole slide image
Multiple instance learning
Contrastive Learning

Abstract: Weakly-supervised whole-slide image (WSI) classification (WSWC) is a challenging task where a large number of unlabeled patches (instances) exist within each WSI (bag) while only a slide label is given. Despite recent progress for the multiple instance learning (MIL)-based WSI analysis,  the major limitation is that it usually focuses on the easy-to-distinguish diagnosis-positive regions while ignoring positives that occupy a small ratio in the entire WSI. To obtain more discriminative features, we propose a novel weakly-supervised classification method based on cross-slide contrastive learning (called SCL-WC), which depends on task-agnostic self-supervised feature pre-extraction and task-specific weakly-supervised feature refinement and aggregation for WSI-level prediction. To enable both intra-WSI and inter-WSI information interaction, we propose a positive-negative-aware module (PNM) and a weakly-supervised cross-slide contrastive learning (WSCL) module, respectively. The WSCL aims to pull WSIs with the same disease types closer and push different WSIs away. The PNM aims to facilitate the separation of tumor-like patches and normal ones within each WSI. Extensive experiments demonstrate state-of-the-art performance of our method in three different classification tasks (e.g., over 2% of AUC in Camelyon16, 5% of F1 score in BRACS, and 3% of AUC in DiagSet). Our method also shows superior flexibility and scalability in weakly-supervised localization and semi-supervised classification experiments (e.g., first place in the BRIGHT challenge). Our code will be available at https://github.com/Xiyue-Wang/SCL-WC. 



SCL-WC: Cross-Slide Contrastive Learning for Weakly-Supervised Whole-Slide Image Classification
Xiyue Wangh 1. Jinxi Xiang" Jun Zhang" Sen Yang': Zhongyi Yang'. Minghui , Jing Zhang' Wei Yang'. Junzhou Huang', and Xiao Han"
CCollege of Biomedical Engineering, Sichuan University, Chengdu, China 610065
"College of Computer Science, Sichuan University, Chengdu, China 610065
Tenneet AI Lab, Shenzhen, China 518057
mungxiyue@stt.seu..m wangminghui @ @scu.edu.cu jing zhang @ scu.edu.en,
(jinxixiang, junejzhang, seusenyang, zhongyiyang, willyang, jochhuang, haroldhan | @teecent.com
Abstract
seakkl--uperrviee whole-slide image (WSI) classification (WSWC) is challeng- ing task where a large number of unlabeled patches (instances) exist within each WSI (bag) while only slide label is given. Despite recent progress for the multiple instance learning (MIL)-based WSI analysis, the major limitation is that it usually focuses the easy-to-diss -o--iiiiiittiihh diagnosis-positive regions while ignoring pos- itives that occupy a small ratio in the entire WSI. To obtain more discriminative features, we propose a novel weakly-supervised classification method based on cross-slide contrastive learning (called SCL-WC), which depends on task-agnosti self-supervised feature pre-extraction and task-specific weakly-supervise feature refinement and aggregation for WSI-Ievel prediction. To enable both intra-W: WSI and module
Anter-WS information interaction, we propose a seesstive-negatveee. (PNM) and reakly-supervised cross-slide contrastive learning (WSCL) mod- ule, respectively. The WSCL aims to pull WSIs with the same disease types closer and push different WSIs away. The PNM aims to facilitate the separation tumor-like patches and normal ones within each WSI. Extensive experiments demonstrate state-of-the-an performance of our method in three different classi- fication tasks (c.g., over 2% AUC in Camelyon16, 5% Fl score in BRACS, and 3% of AUC in DiagSet). Our method also shows superior flexibility and scalability in -spervy-upereieed localization and semi-supervised classification experiments (e.g., first place in the BRIGHT challenge). Our code will be available at https://gitubb..o//iyuuu
wwt.t//o/ww.e.eeaaa/SS......... Wang/SCL-WC.
Introduction
The gold standard for cancer diagnosis is derived by examining pathological slides. With the advance in scanning technology, tissue slides are scanned into whole-slide images (WSIs) for better management and processing, which facilitates the development of computational pathology 2 Due to the gigapixel size of WSIs and their wide variations (e.g., tumor types and staining protocols) 3 4, acquiring exhaustive pixel/patch-level annotations is very time-consuming and expensive. As the dataset size increases, such sufficient labels are obviously impractical. In practice weak annotations at the WSI level are more readily available in clinical reports, which facilitates the emergence of weakly-supervised WSI classification (WSWC) studies.
"Coneesonding author
36th Conference on Neural Information Processing Systems (NcurIPS 2022).

---

Existing WSWC studies are typically formulated based on multiple instance learning (MIL), which defines each WSI as a bag and patches cropped from the WSI as individual instances S 6 g It is noted that positive bag contains at least one positive instance while a negative bag contains all negatives [15] The training process in the MIL. paradigm encompasses two steps: (i) feature encoding for patches cropped from a WSI and (ii) feature aggregation under the same WSI. For the feature encoding, the majority of recent methods directly adopt ImageNet- pretrained backbone as an off-the-shelf feature extractor [8 9 I0
9 10 12 13 14.
I1 [4] and a few studies adopt self-supervised histopathology serattooogg---etrineeeeeee features [33. For the feature aggregation, deep attention pooling [1 [2 [3]. graph neural network [14], and sequence models [38 are used for effective feature aggregation. The deep attention method drives the importance of each patch for the final WSI prediction, generating interpretable results. The graph neural network and sequence models fully
consider the intra-WSS context and long-range dependencies.
However, these methods still have two limitations. First, previous feature encoders are either trained on out-of-domain images supervised manner or pretrained on limited in-domain data in a self- supervised manner, which is not infeasible to extend to large datasets of histopathological images due to the difficulty in capturing sufficient variability across organs and diseases. Thus, there is a lack of a universal feature extractor trained on large and diverse histopathological images. Second, previous feature aggregators unfortunately fail to explore the inter- WSI separability and ignore the global feature comparisons across the training WSIs, resulting in limited generalizability for WSIs with a small proportion of disease- posttssve regions.
To enhance the feature discriminative ability for each patch, we propose novel WSWC method called SCL-WC that aims to achieve both WSS local patch separation and inter-WSI global feature contrast. Specifically, we first apply the MoCo v3 framework [16] to pretrain a Swin Transformer [17] that is then adopted as an offline feature encoder for all patches, which provides a proper initialization to alleviate the over-fitting problem. Then, we design novel aggregation algorithm that contains three modules, namely, class-specific deep attention (CDA), positivennegativ-aaan modeling (PNM), and weakly-suuervised cross-slide contrastive learning (WSCL). The CDA follows the previous deep attention paradigm to assign a learnable weight for each patch to indicate its contribution to the WSI prediction. The PNM explicitly models the appearance of positive and negative patches within WSIs to capture discriminative feature representations, promoting normal/abnormal tissue separation. The WSCL constructs diverse feature comparisons across WSIs to refine task-specific features, where the WSI-level supervision enables more reliable separation capabilities for each class in the contrastive learning setting, helping capture informative features.
Our contributions can be summarized as follows. (i) Pioneeringly, a novel WSCL module is proposed for global feature contrast across WSIs, which helps extract more distinguishable features to facilitate both inter-class separability and intra-class compactness. (ii) The PNM is designed to explicitly split each WSI feature space into positive and negative subspaces, thus helping exclude uninformative patches. (iii) Our proposed SCL-WE WC achieves a significant performance gain compared with other WSWC methods. By feasibly extending it to semi-supervised classification task, method won first place in the BRIGHT challenge.
2 Related work
2.1 Weakly-supervised WSI classification
The WSWC task aims to select representative patches to trigger the corresponding WSI-level labels. Currently, MII. has been applied to formulate this problem with remarkable success [5] 6 7 8 9
10 MT 12: 13, 14), which requires two key techniques: patch-level feature encoding and feature aggregation for WSI representation.
These feature encoders can be divided into online and offline models, where the online networks require real-time updates [3] 6)
(7), resulting in more training epochs to converge compared to the well-pretrained offline models. The utilized offline feature extractors include supervised ImageNet-
pretrained [8] 9 I0, [2] 14] and unsupervised cistooathhlooical-imagee -magg--rettrraeeee models [13] However, the natural images (out-of-domain data) are difficult to accurately capture the textural and morphological characteristics of histopathological images without any fine-tuning. The used self-supervised model pretrained on a small number of unlabeled samples, resulting in limited feature representations.
pretrained

---

Whole-allde mages//app (A,
Patches (inetances) Ky (Pazliiss
Feature encoding
patches X
Encoder
MIP
Taskapaaliic
CDA module
Attention
Unilee
Tiick
FC
LcDA
:
WE
Classfier
AttsTTakk K.
Stametd
PC
Bush
Idlecion
module
PMM module
Reature
Classfeer
ReLU
FC
Lynnse
Tieturee
Clansifer
Figure The pipeline of our proposed SCL-WC method, which consists of two parts: feature encoding and task-specifit feature aggregation. In the feature encoding process, each WSI (bag) is first cropped into patches that are then encoded by SSL-pretrained encoder (Swin Transformer), We use an MLP projector to map the offline features into task-specific features that will be tuned later. The feature aggregator contains three modules: CDA, PNM, and WSCL.
The utilized feature aggregation algorithms be divided into two lines: partial-instanee instance-contributed and full-instanc--comrribbeee methods. The of patches in each WSI by randomly sampling from tissue regions (7]. selecting the top-k patches with high confidence scores [5), or selecting a subset from each patch-level cluster 6 9]. A small portion of patches may fully capture the morphological features for each WSI, leading misdiagnosis and missed diagnoses. The full- menstance-contribuue pooling
methods keep fixed number
methods adopt deep attention graph neural network (14), and sequence model [ integrate all patches within a WSI, where patches can be assigned appropriate weight parameters by the network training drive WSI-level representation. These methods take into account the contributions of all patches and are more flexible and adaptable to other tasks than previous limited representative patches.
2.2 Contrastive representation learning
Contrastive representation learning aims to learn a universal feature by pulling samples belonging to the same class together and pushing samples belonging to different classes apart. The most popular unsupervised contrastive learning methods data augmentations from the same image as positives and those from different images as negatives. In later supervised contrastive learning, data annotations help formulate the correct contrastive pairs, resulting in more representative features [22]. Based on previous studies, this work proposes WSCL module a MIL setting, aiming to extract class-specific distinguishable features in specific tasks.
SimCLR 18 19 and MoCo (20] [6] which take
Methods
3.1 Definition for the WSWC task
Suppose we have a series of training WSIs [ and their corresponding slide-level labels [y where yn (0, 1.... ,C} represents the label of the thth slide, C' denotes the number of

---

classes (e.g., cancer subtypes) and 0 corresponds to the normal (negative) tissues. The n slide can be represented as S, {Pn where P.. the ldl patches cropped from the slide by a sliding window, L. denotes the number of patches within the slide. which may vary across slides of different image sizes. It is noted that only slide-level annotations exist and annotations for internal patches are not explicit. The WSWC task aims train a model with weak labels to conduct WSI-level prediction. The overview of our proposed SCL-WC method is shown in Figure feature encoding process and task-specific feature aggregation.
[ which mainly includes
3.2 Feature encoding
Self-supervised learning (SSL) has the ability to train a universal feature encoder under the supervision of data itself, which has been widely used in computer vision and medical image analysis [8] 20
23 24). Benefiting from its remarkable success, this work applies MoCo v3 framework [20: 16 to
(17) 15 million unlabeled patches from TCGA 25 and PAIP [26] datasets. Then, the well-pretrained backbone is employed as an offline feature extractor transform patches into a series of q-dimensional vectors. For example, the feature vectors of the nh slide can be represented as X,, [xn,) where X,, E R2 denotes the vector of the 1th patch and X,, E R Xq is obtained by stacking all patches within the WSI, Due to the task-agnostic characteristic of these pretrained features, map them into task-specific space using fully connected layers and non-linear activation functions. Thus, the task-specific features in the th
pretrain feature encoder
slide can be represented as Fn [f,,)) /., ReLU (FC (Xn, m))k)) where Fn. E IR xd 0usk is trainable parameters in the fully connected layers.
3.3 Task-specific feature aggregation
Our task-specific feature aggregator consists of three modules: CDA, PNM, and WSCL. The CDA aims to parameterize the contribution of each patch to the final WSI prediction. helping provide interpretable results. The PNM is composed of positive-aware loss and negative-aware loss, which is designed mitigate the noise caused by the large proportion of normal (negative) subregions in each WSI. The WSCL considers global image information by pulling positive bags closer and pushing negative ones away. The combination of these three modules enables the feature aggregator to explore the intra- and Siter-WSI complementary information, helping tune the task-specific feature layers for more discriminative patch-level representations and further improving weakly-supervise classification and localization performance.
Class-specific deep attention. The CDA module acts as a main branch to aggregate these patch- level features F,, {f,,,)/- into a slide-level vector F, F, using deep attention- based MIL pooling [15] that assigns weight for each patch within WSI to specify its relative contribution to the final WSI prediction. This deep attention mechanism is complemented by several fully connected layers. We use A to denote the weight score of the 1 patch in the nhth slide for the 7th class, which is calculated as
exp wi (tanh (OifTT.) O sigm (osfT.))
Atd
Σ/n exp [w: (tanh (0) fTT,)) O sigm (02f (F)
where 0, E Rx 02 E Mxd w E 1IxM and O denotes element-wise multiplication. The attention score ALd ranges from 0 to and the final attention matrix for each class in each slide is normalized such that the sum of these weights 1, i.e., Σ/n A = 1. The attention matrix for the n slide is calculated as An E RL,xC' And then, the weighted slide-level feature of the ith class can be represented as F E Rdd, which is computed by
Ln Σ
F
A
Next, these features are fed into the 7th classifier to drive the corresponding predicted probability for the class. Then, softmax is applied over each class to normalize the probability distribution. We

---

use Pt, to represent the probability that the slide belongs to the it category and y to denote the ground-truth label of the th slide in the one-hot form. They are used to calculate the Mll.-based slide classification loss Cmal in the min-batch size of B as follows.
B M
1 B
Lmal-
vin log (p'))
In addition the Lmits we also consider adding an auxiliary instance discrimination loss further enhance the class-specifer features. Due to the umavailabilityy of the patch-level annotations, we generate pseudo labels of for these high-atee sigh-attention patches (i.e., top-k attention score) and 0 for these low-attention patches (i.e., bottom-k attention score) by sorting the attention scores in a specified column of A, corresponding to the real class of the WSI, which contains total of 2k samples for the classification task using linear classifier with binary cross-entropy loss as follows.
2k
Lins
1
E(s 5=1
Us log (p,) (1 y3) log (1 P2));
(4)
2k
where yj and Py denote the pseudo label and predicted probability of the jth instance, respectively. The final CDA-based loss LCDA is the summed as follows: LCDA Å Cmil + A2Cins.
Positive-negativ--aaare modeling, We propose a PNM module to consider the presence of a large number of normal (negative) tissues within positive WSIs, which should be separated from the abnormal (positive) regions as much as possible. To achieve this, we split the feature space of positive WSI into positive and negative subspaces to enable distinguishable feature learning. Specifically, we first characterize the relevance of the 1th patch to the slide-level prediction by calculating its class- agnostic weight score A" through two fully connected layers, i.e., A sigm (0 (Rel.U(O,fT.)))
Then we use 1 A/ to weight the slide-level feature that focuses on the negative subregions. These new weighted slide features are calculated by
L
F n
Σ (1- C1-Ah)6..t C,,,, T
1
(5)
I
where Fros and F are the weighted leide-level feature for the prediction of positive and negative samples, respectively. At, is the weight score for the 4th patch within the WSI, which ranges from 0 and 1. Then the slide-level prediction probability for positive ph and negative p features can be obtained by feeding F PNM-based classification loss can be calculated
n and Fu into a fully connected layer with softmax function. Then, the
B
LPNM
1
Σ
(logrä" To log (.05),
B
nml
where LPNM is summation of two cross-entropy loss functions: positive-aware classification loss and negative-aware classification loss.
Weakly-supervised cross-slide contrastive learning. Previous contrastive learning methods ap- plied on histopathological images adopt patches as positive/negative units [13], which ignores the information interaction across slides and captures only local feature representation. Different from them, we propose WSCL that aims to generate more discriminative class-specific features by com- paring feature representations across slides, i.c., pulling slides belonging the same class closer and pushing slides belonging to different classes away. Due to the huge heterogeneity within each WSI, direct comparisons between WSIs are susceptible interference from noise, which is instead harmful to network training. Thus, we construct new series of bags by selecting the most representative patches within each WSI based on the class-specific attention obtained above. Specifically, we use three types of sub-memory banks to store positive, negative, and hard negative bags, respectively. We take the top-k patches within each positive WSI into the positive bags. Similarly, the top-k and bottom-k patches within each negative WSI are inserted into hard negative and negative bags,
5

---

respectively. is noted that samples belonging to different positive categories should be stored in separate positive bags. Based on the above definition, our WSCL-based loss can be defined by
LWSCL =
Σ E ZEB
Σ
Σ
log
exp (z: 2z/T)
k PP E,
Encq exp (Z; Za/T)"
where B is an anchor bag, (z: E B) is the hth anchor patch from the anchor bag. For an anchor bag, P. N, and H are used to represent
corresponding sets of positive, negative, and hard negative bags, and [P], [N|, and H are used to represent their corresponding bag number, respectively. P, is used to represent the th bag in the positive set P. Q = P SUPPUR PUNU denotes the total bags used in the WSCL calculation process.
The final loss function is the summation of the CDA-based loss, PNM-based loss, and WSCL-based loss, which is computed Lital LCDA + where and are hyper-parameten used to adjust the contribution of each loss.
Experiments
In this section, we construct a series of experiments five public histopathological image datasets. The five datasets are collected from three different organs, including prostate (PANDA (27) and DiagSet (28)), breast (Camelyon16 [29] and BRACS (30], and colorectum (TCGA-CRC-DX), which are detailed in Table
Datasets. Camelyon16 is released for metastatic breast cancer detection, which contains 240 WSIs with metastasis and 159 normal WSIs. These WSIs are split into training set of 270 WSIs and test set of 129 WSIs by the provider. Although pixel-level annotations are available in the dataset, they only used evaluate our weakly-superrised localization performance.
BRACS is released at the BRIGHT challenge for the classification of breast tumor subtyping. We follow the challenge for 3-class WSI classification: non-cancerous (Non. with 288 WSIs), pre- cancerous (Pre. with 155 WSIs), and cancerous (Can. with 260 WSIs). The challenge organizer splits the total of 703 WSIs into training set of 423 WSIs, test-1 I set of 80 WSIs, and test-2 set of 200 WSIs. It is noted that some well-annotated patches (3566) are also provided for training, which are ignored in WSWC task and are utilized in the training process of semi-supervised classification when participating in the challenge.
PANDA is the largest publicly available WSI data for 2-class prostate cancer classification, which releases a total of 10,616 WSIs (7724 Can. and 2892 Non.). We split them into training, validation, and test sets with ratio of 7:1:2.
DiagSet contains three subsets of histopathological images for 2-class prostate cancer classification: DiagSet-A, DetgSet-B, and DiagSet-C. These subsets are adopted as three external test sets to demonstrate the model generalizability to unseen data. DiagSet-A, DiagSet-B, and DiagSet-C contain 430 WSIs (Can: 228 WSIs and Non.: 202 WSIs), 4675 WSIs (Can: 2090 and Non.: 2585 WSIs), and 46 WSIs (Can: 37 WSIs and Non.: 9 WSIs), respectively.
TCGA-CRC-DX (The Cancer Genome Atlas colon and rectal cancer) 31 is used to test the perfor- mance of our SCL-WC method for microsatellite instability prediction, which contains total of 428 WSIs (62 WSIs with microsatellite instability and 366 WSIs with microsatellite stability). The data splitting process is kept consistent with the original reference [3] i.c., 4-fold pross-validation based on the publicly released splitting table.
Evaluation metrics. For a fair comparison with previous methods, we adopt accuracy (ACC), area under the curve (AUC), and Fl score as metrics to evaluate our weakly-supervise classification performance. Following the Camelyon16 challenge, free response operating characteristic curves (FROC) is used to assess the tumor localization performance [29]. The experimental setups can be seen in the supplementary material.

---

Table 1: Datasets (No.: number). These WSIs are cropped into patches with a size of 224 224 pixels and a resolution of 1.0 microns per pixel.
Table 2: supkll-- classification results on Camelyon dataset
Methods
ACC
AUC
FROC
WSI No.
Patch No.
Full-sup
0.9302
0.9762
0.6543
Camelyon16
399
920,119
Human 29
/
0.9660
0.7325
BRACS
703
1,552,263
Mean-post lean-pooling
0.7984
0.7620
0.1162
PANDA
10616
1,843,968
Max-pooling
0.8295
0.8641
0.3313
DiagSet-A
430
513,274
MIL-RNN
0.8062
0.8064
0.3048
DiagSet-B
4675
4,819.345
ABMIL 15
0.8450
0.8653
0.4056
DiagSet-C
46
50,845
DSMIL
0.8992
0.9165
0.4371
TCGA-CRC-DX
428
1,264,344
CLAM [T]
0.8682
0.9121
0.4104
TransMIL 8
0.8992
0.9337
/
Total
15676
9,699,814
Ours
0.9147
0.9566
0.5659
Table 3: Weakly-supervised classification results
Table 4: Semi-supervised classification results on BRIGHT challenge in terms of Fl score met-
on BRACS
ACC
AUC
F1
ric
Rank
Ave.
Non.
Pre.
Can.
Mean pooling
0.7333
0.7294
0,2932
Max pooling
0.7458
0.7992
0.4386
1 (Ours)
0.716
0.725
0.623
0.800
ABMIL [13]
0.7291
0.8055
0.4842
2
0.643
0.564
0.580
0.786
TransMIL 8
0.7208
0.7863
0.5602
3
0.599
0.675
0.455
0.667
CLAM 10
0.7583
0.8158
0.5611
4
0.520
0.637
0.244
0.680
DSMIL. 13
0.7644
0.8314
0.6349
5
0.480
0.530
0.331
0.580
Ours
0.8208
0.8650
0.6886
6
0.459
0.388
0.416
0.571
4.1 Results on Camelyon16 dataset
This subsection validates our weakly-supervised classification and localization capacities on Camelyon16 6 dataset by comparing it with state-of-the-art related methods. Detailed results are shown Table 2 where the best result is bolded, and the second best is underlined. except for the CLAM and TransMIL algorithms, previous methods are all reported in [13], which are implemented using their corresponding official codes along with the SSL-pretrained features by [3]. The CLAM and TransMIL algorithms implemented by using their released code and our pretrained features. Thus we can directly compare our aggregation algorithm with these methods.
noted that,
As shown in Table our method outperforms other WSWC algorithms to large extent. For example, our method outperforms the previous best-performing TransMIL by around 2% ACC and 2% in AUC. The reason can be analyzed follows. In the Camelyon 1 dataset, the percentage of anomalous regions within each WSI is typically below 10%. Thus, previous methods are particularly susceptible to noise, resulting in possibly missed detection. Our proposed SCL-WC enables distinctive categorical feature extraction by intra- WSI and inter- WSI complementary information converging, helping alleviate the problem of a small percentage of lesions within WSIs.
The CDA module assigns a learnable weight for each patch to represent its importance for the WSI prediction, which is combined with the feature refinement process in the WSCL and PNM modules to promote better lesion localization ability. The detailed diagnosis-position
localization results are shown in Table 2 Figure 2] and supplementary material. As shown in Table 2 our method achieves an FROC of 0.5659, which outperforms over 10% than other methods and shows the potential to be close to fully-supervised performance with an FROC of 0.6543. In Figure supplementary material, the warmer colored subregions imply a higher probability of abnormal tissues, which visually demonstrates superior localization performance for tiny lesions.

---

Figure 2: The visualization of weakly-supervised localization results. These six samples taken from the Camelyon16 6 dataset. In the image pair, the left image represents the ground truth with green lines mark lesion regions and the right image is predicted results by our model where these lesion regions are highlighted by warm color.
4.2 Results BRACS, PANDA, and DiagSet datasets
In this subsection, we first conduct 3-class weakly-supervised classification experiments on the BRACS dataset in Table 3 The results of these compared methods are obtained according to their released official codes. The 3-class classification task is very challenging due to the indistinguishable features between some non-cancerous and pre-cancerous regions (e.g., pathological benign V.S. flat epithelial atypia) or between some pre-cancerous and cancerous tissues (e.g., atypical ductal hyperplasia V.S. ductal carcinoma in situ). Thus, the overall classification performance is less than 90% in the current statc-of-the-ar methods. The previous best-performed method is the DSMIL method in the BRACS dataset, however, it shows suboptimal performance in other datasets (e.g.. 2 and Table (5). Our proposed SCL-WC remains most effective on all of these datasets, which reflects its stability.
Then, in Table we show our results when participating in the 2022 BRIGHT challenge, which is achieved using semi-supervised classification scheme extended from our SCL-WC, with little difference whether small number of well-annotated patches are used. As shown in Table our method ranked first in the BRIGHT challenge, which shows the high flexibility and scalability of our proposed SCL-WC method.
Next, we validate our algorithm on two larger prostate datasets. Also, extensively external tests are performed further confirm the robustness and generalization performance of our algorithm. The detailed results are summarized in Table 5] where the PANDA dataset is used for algorithm development and inner validation, and the remaining three subsets DiagSet are used for external tests. The results of these compared methods are obtained using their released codes. As shown Table 5 our method consistently performs better on these datasets. Specifically, our method outperforms the CLAM method by around 3% ACC and 2% AUC in the PANDA dataset. In the largest external test set DiagSet-B, our method surpasses DSMIL. by around 10% and 3% ACC and AUC, respectively.

---

Table 5: Results on the prostate datasets
PANDA
DiagSet-A
DiagSet-B
DiagSet-C
ACC
AUC
ACC
AUC
ACC
AUC
ACC
AUC
Mean pooling
0.8407
0.9386
0.8313
0,9237
0.8197
0.8914
0.8478
0.9159
Max pooling
0.8847
0.9508
0.7330
0.9315
0.7377
0.9371
0.8695
0.9489
ABMIL [S
0.8804
0.9514
0.7845
0.9145
0.8032
0.9105
0.8695
0.9609
TransMIL 8
0,8715
0.9408
0.8290
0,9290
0.8246
0.9334
0,9130
0.9669
DSMIL 13
0.8751
0.9444
0.7072
0,9242
0.8146
0.9431
0.8876
0,9489
CLAM
[O
0.8874
0.9532
0.7822
0,9033
0.8035
0.9051
0.8913
0.9579
Ours
0.9194
0.9753
0.8960
0.9560
0.9191
0.9730
0.9565
0.9939
4.3
Comparison between different feature extractors and feature aggregators
The proposed feature extractor is compared with existing feature extractors that are widely used for histopathological image analysis tasks, including an SmageNet-pretraiine feature extractor and two other SSL-based feature extractors pretrained on histopathological images (SimCLR and DINO) 32, 33.. To this end, we have conducted new experiments investigate the performance of our SCL-WC method when using different feature extractors for microsatellite instability prediction on the TCGA-CRC-DD data and breast tumor subtyping on BRACS data. These existing feature extractors have been pretrained by previous studies and can be used as off-the-shelf feature extractors for comparison. The detailed results can be seen in Table 6 can be seen that the best performance is achieved by combining the proposed feature extractor with the proposed feature aggregator. Comparing different feature extractors tested on TCGA-CRC-DX data, it is also shown that our feature extraction method outperforms the previous best-performing DINO-VIT by around 7% using both aggregation methods.
Table 6 also shows the effectiveness of the proposed feature aggregation scheme by keeping the same feature extractor while testing different feature aggregators (CLAM and ours). Comparing the corresponding columns of TCGA-CRC-DX data in Table it is seen that our aggregation scheme outperforms that of the CLAM method by around 4% for any of the four different feature extractors.
Table 6: Results of weakly supervised classification using different feature extractors and feature aggregators. (Datasets: TCGA-CRC-DX and BRACS: Metric: AUC)
Feature extractors
Feature aggregators
CLAM
Ours
CLAM
Ours
TCGA-CRC-DX
BRAC
CraageNetRReNNefte
0.7568
0.7806
0.8158
0,8446
SinCLR-ResNetL3 ResNet18 332
0.7716
0.8108
0.7634
0,8008
DINO- ViT 33
0.7853
0.8246
0.8108
0.8345
Ours-Swiinnransffrreer
0.8530
0.8954
0.8378
0.8650
4.4 Ablation study
We construct a set of ablation experiments in Table 7 to demonstrate the effectiveness of key components our proposed SCL-WC method, including SSL-based feature extractor. LCDA, LPNM- and LWSCL (with and without hard negatives). As shown in Table 7 the results in the first two rows show that, compared to the ImageNet-pretrained network, the SSL-based histopathology--peerrii feature extractor improves around 3% on Camelyon16 and 2% on BRACS in terms of AUC. As shown in the second and third rows of Table [ our Dessttve-negativee-ae 1 % performance gain across all datasets and metrics, which verifies the importance of the PNM module. The effectiveness of the WSCL module can seen last three rows of Table 7 When hard-negative samples are not considered, the performance is improved by around 2% in both datasets compared to the results on the third row, and the performance improves further when hard negative samples are added as shown in the last row of Table
loss brings a consistent

---

Table 7: Results of ablation study
Camelyon16
BRACS
ACC
AUC
ACC
AUC
ImageNer + LCDA
0.8370
0.8730
0.7523
0.8114
SSL LCDA
0.8759
0.9080
0.7635
0.8335
SSI. LCDA + LPNM
0.8814
0.9190
0.7725
0.8403
SSL LODA + LPNM EWSCL (P+N)
0.9069
0.9330
0.7926
0.8512
SSL LCDA + LPNM + LWSCL (P+N+HN)
0.9147
0.9566
0.8208
0.8650
Conclusion
We propose novel WSWC method called SCL-WC, which is constructed based on a domain-specific SSL feature extractor and task-specific feature aggregator. The feature aggregator includes three effective modules: CDA, PNM, and WSCL, which are combined for discriminative patch-level feature refinement, providing not only interpretable results but also fine lesion localization. The proposed SCL-WC method outperforms oott-----he-ant WSWC studies over five publicly available datasets for the binary/multiple classification tasks, which also shows good feasibility and scalability in the -eakkiy-uperrvied localization and semi-supervised classification tasks. However, our method should be extensively validated in larger cohorts from real-world clinical settings before its deployment, and we will explore this in future work.
6 Acknowledgment
This research was in part funded by the Science & technology department of Sichuan Province (No. 2020YFG0081) I) and the National Key R&D Program of China (No. SQ2022YFC2400144)
References
[1] M. K. K. Niazi, A. V. Parwani, and M. N. Gurcan, "Digital pathology and artificial intelligence," The lancel oncology, vol. 20, no. 5, pp. e253-e261, 2019.
[2] L. Barisoni, K. J. Lafata, S. M. Hewitt, A. Madabhushi, and U. G. Balis, "Digital pathology and computational image analysis in nephropathology." Nature reviews nephrology, vol. 16, no. 11, pp. 669-685, 2020.
Nature
[3] H. R. Tizhoosh, Diamandis, C. J. Camphell, A. Safarpoor, S. Kalra, D. Maleki, A. Riasatian, and M. Babaie, "Searching images for consensus: can AI remove observer variability in pathology?" The American journal of pathology, vol. 191, no. 10, pp. 1702-1708. 2021.
[4] X. Zhu, X. Li, K. Ong, W. Zhang. W. Li, L. Li, D. Young, Y. Su, B. Shang, L.. Peng et al., "Hybrid Al-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears," Nature communications vol. 12, no. 1, pp. 1-12, 2021.
[5] G. Campanella, M. G. Hanna, L. Geneslaw, A. Miraflor, V. Werneck Krauss Silva, K. J. Busam, E. Brogi, V. E. Reuter, D. S. Klimstra, and T. J. Fuchs, "Clinical-grai grade computational pathology using weakly supervised deep learning on whole slide images," Nature medicine, vol. 25, no. 8, pp. 1301-1330 1309, 2019.
[6] Y. Sharma, A. Shrivastava, L. Ehsan, C. A. Moskaluk, S. Syed, and D. Brown, "Cluster-to- conquer: A framework for end-to-end multi-instance learning for whole slide image classifica- tion." in MIDL, 2021, pp. 682-698.
[7] N. Hashimoto, D. Fukushima, R. Koga, Y. Takaagi K. Ko, K. Kohno, M. Nakaguro, S. Nakamura, H. Hontani, and I. Takeuchi, "Multi-scale domain-adversaria multiple-instance CNN for cancer subtype classification with unannotated histopathological images," " in CVPR, 2020, pp. 3852- 3861.
10

---

[8] Z. Shao, H. Bian, Y. Chen, Y. Wang, J. Zhang, X. Ji et al., "TransMIL: Transformer based correlated multiple instance learning for whole slide image classification," NeurIPS, vol. 34, 2021.
[9] Yao, X. Zhu, Jonnagaddala, N. Hawkins, and Huang, "Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks," Medical image analysis, vol. 65, p. 101789, 2020.
[10] M. Y. Lu, D. F. Williamson, T. Y. Chen. R. J. Chen, M. Barbieri, and F. Mahmood, "Data- efficient and weakly supervised computational pathology on whole-slide images," Nature biomedical engineering, vol. 5, no. 6, pp. 555-570, 2021.
[11] M. Y. Lu, Y. Chen, D. F. Williamson, M. Zhao, M. Shady, J. Lipkova, and F. Mahmood, "Al-based pathology predicts origins for cancers of unknown primary," Nature, vol. 594, no. 7861, pp. 106-110, 2021.
[12] R. J. Chen, M. Y. Lu, W.-H. Weng. T. Y. Chen, D. F. Williamson, T. Manz, M. Shady, and Mahmood, "Multimodal co-attention transformer for survival prediction in gigapixel whole slide images," in ICCV, 2021, pp. 4015-4025.
[13] B. Li, Y. Li, and K. W. Eliceiri, "Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning," in CVPR, 2021, pp. 14 318- 14 328.
[14] R. Chen, M. Y. Lu, M. Shaban, C. Chen, Y. Chen, D. Williamson, and Mahmood, "Whole slide images are 2D point clouds: Context-aware survival prediction using patch-based graph convolutional networks," MICCAI, 2021, pp. 339-349.
[15] M. Ilse, J. Tomezak, and M. Welling, "Attention-basee deep multiple instance learning," in ICML, 2018, pp. 2127-2136.
[16] X. Chen, S. Xie, and K. He, "An empirical study of training self-supervised vision transformers," ICCV, 2021, pp. 9640-9649.
[17] Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo. "Swin Transformer: Hierarchical vision transformer using shifted windows," " ICCV, 2021, pp. 10012-10022.
[18] T. Chen, S. Korbliihh, M. Norouzi. and G. Hinton, "A simple framework for contrastive learning of visual representations," " in ICML, 2020, pp. 1597-1607.
[19] T. Chen, S. Korblith, K. Swersky, M. Norouzi, and G. E. Hinton, "Big self supervide models are strong supe-supe learners," NeurIPS, vol. 33, pp.
2020.
[20] K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, "Momentum contrast for unsupervised visual representation learning," in CVPR, 2020, pp. 9729-9738.
[21] X. Chen, H. Fan, Girshick, and K. He, "Improved baselines with momentum contrastive learning," arXiv preprint arXiv:2003.04297 2020.
[22] Khosla, Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and
D. Krishnan, "Supervised contrastive learning," NeurIPS, vol. 33, pp. 18 661-18677 2020.
[23] S. Azizi, B. Mustafa, F. Ryan, Z. Beaver, J. Freyberg, Deaton, A. Loh, A. Karthikesalingam,
S. Kornblith, T. Chen et al., "Big self-supervised models advance medical image classification." ICCV, 2021, pp. 3478-3488.
[24] A. Taleb, W. Loetzsch, N. Danz, J. Severin, T. Gaertner, B. Bergner, and C. Lippert. "3D self-supervised methods for medical imaging," NeurlPS, vol. 33, pp. 18 158-18 172, 2020.
[25] J. N. Weinstein, E. A. Collisson, G. B. Mills, K. R. Shaw, B. A. Ozenberger, K. Ellrott, Shmulevich, C. Sander, and M. Stuart, "The cancer genome atlas pan-cancer analysis project," Nature genetics, vol. 45, no. 10, pp. 1113-1120, 2013.
11

---

[26] J. Kim, H. Jang, K. Lee, S. Park, S.-G. Min, C. Hong. J. H. Park, K. Lee, J. Kim, W. Hong et al., "PAIP 2019: Liver cancer segmentation challenge," Medical image analysis, vol. 67, p. 101854, 2021.
[27] W. Bulten, K. Kartasalo, P.-H. C. Chen, P. Ström, H. Pinckaers, K. Nagpal, Y. Cai, D. F. Steiner, H. van Boven, R. Vink et al., "Artificial intelligence for diagnosis and gleason grading of prostate cancer: the PANDA challenge," Nature medicine, pp. 1-10, 2022.
[28] M. Koziarski, B. Cyganek, B. Olborski, Antosz, M. Zydak, B. Kwolek, P. Wasowicz, A. Bukala, J. Swadžba, and P. Sitkowski, "DiagSet: dataset for prostate cancer histopathologi- cal image classífication," " arXiv preprint arXiv:2105. 04014, 2021.
[29] B. E. Bejnordi, M. Veta, P. J. Van Diest, B. Van Ginneken, N. Karssemeijer, G. Litjens, J. A. Van Der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol el al., "Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer," JAMA, vol. 318, no. 22, pp. 119-22210 2200,22 2017.
[30] N. Brancati, A. M. Anniciello, P. Pati, D. Riccio, G. Scognamiglio, G. Jaume, G. De Pietro, M. Di Bonito, A. Foncubierta, G. Botti et al., "BRACS: A dataset for BReAst carcinoma subtyping in H&E histology images," urXiv preprint arXiv:21 1tt:://11.044 044440, 2021.
[31] M. Bilal, S. E. A. Raza, A. Azam, S. Graham, M. Ilyas, I. A. Cree, D. Snead, F. Minhas, and N. M. Rajpoot, "Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: retrospective study," The Lancel Digital Health, vol. 3, no. 12, pp. e763- c772, 2021.
[32] O. Ciga, T. Xu, and A. L. Martel, "Self supervised contrastive learning for digital histopathology." Machine Learning with Applications, vol. 7, p. 100198, 2022.
[33] R. J. Chen, C. Chen, Y. Li, T. Y. Chen, A. D. Trister, R. G. Krishnan, and F. Mahmood, "Scaling vision transformers to gigapixel images via hierarchical self-supervised learning," in CVPR, 2022, pp. 16 164-16 155.
Checklist
1. For all authors...
(a) Do the main claims made in the abstract and introduction accurately reflect the paper's
contributions and scope? [Yes]
(b) Did you describe the limitations of your work? [Yes] See Section 5
(c) Did you discuss any potential negative societal impacts of your work? [Yes] See supplementary materials.
(d) Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes] I read it carefully and this work fits them perfectly.
2. If you are including theoretical results...
(a) Did you state the full set of assumptions of all theoretical results? [Yes] See Section
(b) Did you include complete proofs of all theoretical results? [Yes] See Section 3
3. If you ran experiments..
(a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either the supplemental material or as a URL)? [Yes] See Section and supplementary materials.
(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Section supplementary materials.
(c) Did you report error bars (e.g., with respect to the random seed after running experi- ments multiple times)? [Yes] See supplementary materials.
(d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, cloud provider)? [Yes] See supplementary materials.
12

---

4. If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...
(a) If your work uses existing assets, did you cite the creators? [Yes] See Section 4
(b) Did you mention the license of the assets? [No] We are unable to find the license for these publicly available dataset.
(c) Did you include any new assets either in the supplemental material or as a URL? [Yes] See supplementary materials.
(d) Did you discuss whether and how consent obtained from people whose data you' 're using/curating? [Yes] These datasets we used are publicly available. (e) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content?
[No] These publicly available data have been
desensitized.
5. If you used crowdsourcing or conducted research with human subjects...
(a) Did you include the full text of instructions given to participants and screenshots, applicable? [N/A]
(b) Did you describe any potential participant risks, with links Institutional Review Board (IRB) approvals, if applicable? [N/A]
(c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation? [N/A]
13


Reviews:
[{"review_id": "mDRtBVVUn6A", "reviewer": "Reviewer_nwVH", "summary": "This paper presents a weakly supervised contrastive learning approach for the classification of whole-slide images (WSI). Specifically, the proposed method consists of two main steps: 1) a domain-specific self-supervised feature extractor pretrained using a Swin-Transformer, and 2) task-specific feature aggregation modules to achieve distinguishable features that can facilitate both inter-class separability and intra-class compactness for WSI representation. The proposed method has been validated on four publicly available large-scale datasets, across two different organs, including prostate (PANDA and DiagSet) and breast (Camelyon16 and BRACS), for binary and multi-class classification tasks. The proposed technique shows significant improvement over the other weakly supervised benchmark methods across all four datasets, with several ablation experiments that demonstrate the efficacy of the proposed idea. \n\nOverall, in my opinion, the idea is impressive, and it is novel in the context of weakly-supervised learning for WSI classification.", "questions": "I have a few minor concerns with this paper:\n\n— In Section 3.3, Equation 4 (instance-level loss), it is unclear how these pseudo-labels are obtained from the attention network since we don’t have any patch-level labels in a weakly-supervised setting. Further, what is the chosen value of top-K and bottom-k in the experimentation for identifying high-attention and low-attention patches?\n\n— For SSL pretraining, was the method trained on complete TCGA datasets across multiple organs, or was it trained on a specific subset of TCGA. Please, clarify this in the paper.", "limitations": "Yes, the authors have adequately addressed the limitations and potential negative societal impact of their work", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": " — A series of novel feature aggregation modules (such as class-specific deep attention (CDA), positive-negative-aware modeling (PNM), and weakly-supervised cross-slide contrastive learning (WSCL)) presented in this paper is a clever and well-engineered technique that captures both intra- and inter-WSI feature separability. This strategy is very impressive and generic, which can be extended well beyond other weakly-supervised applications such as survival predictions or treatment outcomes in computational pathology. \n          \n— Comparison against a robust set of baseline methods (such as CLAM and Trans-MIL) and ablation experiments that have been conducted demonstrates the usefulness of the proposed technique in challenging four weakly-supervised WSI classification tasks.\n          \n— Overall, the paper is well written and easy to follow the core idea.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "RzBgtajyihJ", "reviewer": "Reviewer_s5WU", "summary": "Summary:\nThis paper explores the multiple instance learning problems, i.e. the whole slide image classification task. The authors proposed a weakly supervised WSI classification method using cross-slide contrastive learning. Their presented methodology is dependent on self-supervised feature pre-extraction and task-specific weakly-supervised feature refinement and aggregation for WSI-level prediction. The main goal of the weakly-supervised cross-slide constructive learning module is to pull WSIs with the same disease types closer and push different WSIs away. \n\n", "questions": "The authors need to evaluate the performance of their approach on different cancer indications. For example, there are 32 different cancer indications. I would like to see if your model still performs well enough on a large proportion of those indications.\n\nIn addition, the implementation specifications are missing in the manuscript. \n\nThe methodology part is not well described and I cannot see its comparison with state-of-the-art. I would suggest the authors provide more clarity on what is the main contribution of this work and how it is different from others. ", "limitations": "\nThe authors have used a pre-trained feature extractor, i.e. swin transformer from unlabeled patches from TCGA and PAIP, however, they have provided neither intuition behind their choice of feature extractor network compared to other approaches such as SimCLR nor provided their class-specific distribution nor patch sizes, etc. It is unclear to the reviewer why the authors have not provided how well the pre-trained feature extractor has worked in comparison to existing pre-trained models. I would suggest authors take a systematic comparison between different components of their model, for example, compare your model, WSCL using different feature extractors, and empirically demonstrate the performance of your choice of feature extractor. Table 6 could have been extended ImageNet (P+N+HN) analysis as well.\nIn addition, the pre-trained model is on TCGA slides whereas they have evaluated their model performance on Camelyon16, PANDA, BRACS, and DiagSet. While these datasets are relatively small, why there is no evaluation on the TCGA database? The authors could have demonstrated the generalizability of their approach on larger cancer indications from TCGA instead of focusing only on prostate cancer and breast cancer. \nWhat is the difference between the utilized class-specific deep attention (section 3.3) and the presented attention pooling mechanism in reference 15: Attention-based Deep Multiple Instance Learning? Equation 1 in the manuscript is the same as equation 9 in reference 15. \nIn table 1. what are the patch sizes? what is the magnification level of your readings? There is a very limited description regarding the implementation specifications, which would make the readers more confused.\nAuthors could have benefited from a better comparison of their presented approach with its counterparts. For example, they could have described how their attention pooling is different from other state-of-the-art methodologies. In addition, the comparisons between their evaluations with other studies are limited. It is unclear how well their approach is generalizable to other cancer indications. ", "rating": 3, "confidence": 5, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\nThe authors have investigated one of the challenging research questions in the field, i.e. whole-slide image classification. \nThe authors have demonstrated the superior performance of their approach to breast cancer and prostate cancer. \n\nWeaknesses:\nThe authors have used a pre-trained feature extractor, i.e. swin transformer from unlabeled patches from TCGA and PAIP, however, they have provided neither intuition behind their choice of feature extractor network compared to other approaches such as SimCLR nor provided their class-specific distribution nor patch sizes, etc. It is unclear to the reviewer why the authors have not provided how well the pre-trained feature extractor has worked in comparison to existing pre-trained models. I would suggest authors take a systematic comparison between different components of their model, for example, compare your model, WSCL using different feature extractors, and empirically demonstrate the performance of your choice of feature extractor. Table 6 could have been extended ImageNet (P+N+HN) analysis as well.\nIn addition, the pre-trained model is on TCGA slides whereas they have evaluated their model performance on Camelyon16, PANDA, BRACS, and DiagSet. While these datasets are relatively small, why there is no evaluation on the TCGA database? The authors could have demonstrated the generalizability of their approach on larger cancer indications from TCGA instead of focusing only on prostate cancer and breast cancer. \nWhat is the difference between the utilized class-specific deep attention (section 3.3) and the presented attention pooling mechanism in reference 15: Attention-based Deep Multiple Instance Learning? Equation 1 in the manuscript is the same as equation 9 in reference 15. \nIn table 1. what are the patch sizes? what is the magnification level of your readings? There is a very limited description regarding the implementation specifications, which would make the readers more confused.\nAuthors could have benefited from a better comparison of their presented approach with its counterparts. For example, they could have described how their attention pooling is different from other state-of-the-art methodologies. In addition, the comparisons between their evaluations with other studies are limited. It is unclear how well their approach is generalizable to other cancer indications. \n\n\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "LdT01EnaHFm", "reviewer": "Reviewer_B23m", "summary": "The paper proposed a novel framework for Weakly-Supervised Whole-Slide Image Classification. It uses a self-supervised contrastive representation encoder trained with large-scale data. It implements three components to aggregate the obtained patch-level representations:  deep attention-based class-specific MIL aggregation(CDA), a positive-negative-aware model that pushes away positive and negative patches given its relevance to the slide-level prediction (PNM), and a weakly-supervised cross-slide contrastive learning with both hard negative and negative sample mining. \n", "questions": "1. How is the class-agnostic weight score computed precisely in line 163? \n2. How are the hyperparameters selected ($\\alpha$, et.al)?\n3. The presentation of tables on page 7 needs refinements. ", "limitations": "The author mentioned about validating in larger cohorts from real-world clinical setting as future work. ", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "* The paper adapted existing ideas to a different application given the characteristics of the problem they aim to solve.\n* The paper is well written and easy to follow. The main contributions in terms of the method are well-explained. \n* The proposed methods are well justified and intuitively understandable. They also achieved very strong performance on multiple datasets. \n* Ablation studies are conducted to help understand the importance of each proposed component. \n  ", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "H6E7VKaqzlh", "reviewer": "Reviewer_oepB", "summary": "This paper proposed a novel weakly-supervised whole-slide image classification method called SCL-WC, which is constructed based on a domain-specific self-supervised learning feature extractor and a task-specific feature aggregator. The proposed SCL-WC method outperforms state-of-the-art weakly-supervised whole-slide image classification studies over four publicly available datasets for the binary/multiple classification tasks.", "questions": "1. Why use swin transformer as the feature encoder?\n2. Why use MoCo V3 as self supervised learning? what is the detailed implementation process of Moco V3 in the whole framework ?", "limitations": "see weakness", "rating": 5, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strength：\n1. The overall framework is novel and interesting. The use of positive-negative-aware module (PNM) and a weakly-supervised cross-slide contrastive learning (WSCL) module to achieve both intra-WSI local patch separation and inter-WSI global feature contrast is smart.\n2. The motivation and introduction are clear.\n3. The multi-class classification used in multi-instance learning is novel.\n\nWeakness：\n3. The application of contrastive learning, i.e., MoCov3, in histopathological image classification is very common. \n4. The assignment of the three hyper-parameter α，β，γ of the loss function mentioned in this paper lacks sufficient experiments.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}]

Author rebuttal:

Decision: Accept