Original: SCL-WC: Cross-Slide Contrastive Learning for Weakly-Supervised Whole-Slide Image Classiﬁcation
Revised: SCL-WC: Cross-Slide Contrastive Learning for Weakly-Supervised Whole-Slide Image Classiﬁcation

Original: Abstract
Revised: Abstract

Original: Weakly-supervised whole-slide image (WSI) classiﬁcation (WSWC) is a challenging task where a large number of unlabeled patches (instances) exist within each
Revised: Weakly-supervised whole-slide image (WSI) classiﬁcation (WSWC) is a challenging task where a large number of unlabeled patches (instances) exist within each

Original: WSI (bag) while only a slide label is given.
Revised: WSI (bag) while only a slide label is given.

Original: Despite recent progress for the multipleinstance learning (MIL)-based WSI analysis, the major limitation is that it usuallyfocuses on the easy-to-distinguish diagnosis-positive regions while ignoring positives that occupy a small ratio in the entire WSI.
Revised: Despite recent progress for the multipleinstance learning (MIL)-based WSI analysis, the major limitation is that it usuallyfocuses on the easy-to-distinguish diagnosis-positive regions while ignoring positives that occupy a small ratio in the entire WSI.

Original: To obtain more discriminativefeatures, we propose a novel weakly-supervised classiﬁcation method based oncross-slide contrastive learning (called SCL-WC), which depends on task-agnosticself-supervised feature pre-extraction and task-speciﬁc weakly-supervised featurereﬁnement and aggregation for WSI-level prediction.
Revised: To obtain more discriminativefeatures, we propose a novel weakly-supervised classiﬁcation method based oncross-slide contrastive learning (called SCL-WC), which depends on task-agnosticself-supervised feature pre-extraction and task-speciﬁc weakly-supervised featurereﬁnement and aggregation for WSI-level prediction.

Original: To enable both intra-WSI andinter-WSI information interaction, we propose a positive-negative-aware module(PNM) and a weakly-supervised cross-slide contrastive learning (WSCL) module,respectively.
Revised: To enable both intra-WSI andinter-WSI information interaction, we propose a positive-negative-aware module(PNM) and a weakly-supervised cross-slide contrastive learning (WSCL) module,respectively.

Original: The WSCL aims to pull WSIs with the same disease types closer andpush different WSIs away.
Revised: The WSCL aims to pull WSIs with the same disease types closer andpush different WSIs away.

Original: The PNM aims to facilitate the separation of tumor-likepatches and normal ones within each WSI.
Revised: The PNM aims to facilitate the separation of tumor-likepatches and normal ones within each WSI.

Original: Extensive experiments demonstratestate-of-the-art performance of our method in three different classiﬁcation tasks(e.g.
Revised: Extensive experiments demonstratestate-of-the-art performance of our method in three different classiﬁcation tasks(e.g.

Original: , over 2% of AUC in Camelyon16, 5% of F1 score in BRACS, and 3% of
Revised: , over 2% of AUC in Camelyon16, 5% of F1 score in BRACS, and 3% of

Original: AUC in DiagSet).
Revised: AUC in DiagSet).

Original: Our method also shows superior ﬂexibility and scalability inweakly-supervised localization and semi-supervised classiﬁcation experiments(e.g.
Revised: Our method also shows superior ﬂexibility and scalability inweakly-supervised localization and semi-supervised classiﬁcation experiments(e.g.

Original: , ﬁrst place in the BRIGHT challenge).
Revised: , ﬁrst place in the BRIGHT challenge).

Original: Our code will be online.
Revised: Our code will be online.

Original:  Introduction
Revised:  Introduction

Original: The gold standard for cancer diagnosis is derived by examining pathological slides.
Revised: The gold standard for cancer diagnosis is derived by examining pathological slides.

Original: With theadvance in scanning technology, tissue slides are scanned into whole-slide images (WSIs) for bettermanagement and processing, which facilitates the development of computational pathology [1; 2].
Revised: With theadvance in scanning technology, tissue slides are scanned into whole-slide images (WSIs) for bettermanagement and processing, which facilitates the development of computational pathology [1; 2].

Original: 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.
Revised: 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.

Original: As the dataset size increases, such sufﬁcient labels are obviously impractical.
Revised: As the dataset size increases, such sufﬁcient labels are obviously impractical.

Original: In practice, weakannotations at the WSI level are more readily available in clinical reports, which facilitates theemergence of weakly-supervised WSI classiﬁcation (WSWC) studies.
Revised: In practice, weakannotations at the WSI level are more readily available in clinical reports, which facilitates theemergence of weakly-supervised WSI classiﬁcation (WSWC) studies.

Original: Existing WSWC studies are typically formulated based on multiple instance learning (MIL), whichdeﬁnes each WSI as a bag and patches cropped from the WSI as individual instances [5; 6; 7; 8;9; 10; 11; 12; 13; 14].
Revised: Existing WSWC studies are typically formulated based on multiple instance learning (MIL), whichdeﬁnes each WSI as a bag and patches cropped from the WSI as individual instances [5; 6; 7; 8;9; 10; 11; 12; 13; 14].

Original: It is noted that a positive bag contains at least one positive instance while anegative bag contains all negatives [15].
Revised: It is noted that a positive bag contains at least one positive instance while anegative bag contains all negatives [15].

Original: The training process in the MIL paradigm encompasses two steps: (i) feature encoding for patches cropped from a WSI and (ii) feature aggregation underthe same WSI.
Revised: The training process in the MIL paradigm encompasses two steps: (i) feature encoding for patches cropped from a WSI and (ii) feature aggregation underthe same WSI.

Original: For the feature encoding, the majority of recent methods directly adopt ImageNetpretrained backbone as an off-the-shelf feature extractor [8; 9; 10; 11; 12; 14] and a few studies adoptself-supervised histopathology-pretrained features [13].
Revised: For the feature encoding, the majority of recent methods directly adopt ImageNetpretrained backbone as an off-the-shelf feature extractor [8; 9; 10; 11; 12; 14] and a few studies adoptself-supervised histopathology-pretrained features [13].

Original: For the feature aggregation, deep attentionpooling [10; 11; 12; 13], graph neural network [14], and sequence models [5; 8] are used for effectivefeature aggregation.
Revised: For the feature aggregation, deep attentionpooling [10; 11; 12; 13], graph neural network [14], and sequence models [5; 8] are used for effectivefeature aggregation.

Original: The deep attention method drives the importance of each patch for the ﬁnal WSIprediction, generating interpretable results.
Revised: The deep attention method drives the importance of each patch for the ﬁnal WSIprediction, generating interpretable results.

Original: The graph neural network and sequence models fullyconsider the intra-WSI context and long-range dependencies.
Revised: The graph neural network and sequence models fullyconsider the intra-WSI context and long-range dependencies.

Original: However, these methods still have two limitations.
Revised: However, these methods still have two limitations.

Original: First, previous feature encoders are either trainedon out-of-domain images in a supervised manner or pretrained on limited in-domain data in a selfsupervised manner, which is not infeasible to extend to large datasets of histopathological imagesdue to the difﬁculty in capturing sufﬁcient variability across organs and diseases.
Revised: First, previous feature encoders are either trainedon out-of-domain images in a supervised manner or pretrained on limited in-domain data in a selfsupervised manner, which is not infeasible to extend to large datasets of histopathological imagesdue to the difﬁculty in capturing sufﬁcient variability across organs and diseases.

Original: Thus, there is alack of a universal feature extractor trained on large and diverse histopathological images.
Revised: Thus, there is alack of a universal feature extractor trained on large and diverse histopathological images.

Original: Second,previous feature aggregators unfortunately fail to explore the inter-WSI separability and ignore theglobal feature comparisons across the training WSIs, resulting in limited generalizability for WSIswith a small proportion of disease-positive regions.
Revised: Second,previous feature aggregators unfortunately fail to explore the inter-WSI separability and ignore theglobal feature comparisons across the training WSIs, resulting in limited generalizability for WSIswith a small proportion of disease-positive regions.

Original: To enhance the feature discriminative ability for each patch, we propose a novel WSWC method called
Revised: To enhance the feature discriminative ability for each patch, we propose a novel WSWC method called

Original: SCL-WC that aims to achieve both intra-WSI local patch separation and inter-WSI global featurecontrast.
Revised: SCL-WC that aims to achieve both intra-WSI local patch separation and inter-WSI global featurecontrast.

Original: 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 initializationto alleviate the over-ﬁtting problem.
Revised: 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 initializationto alleviate the over-ﬁtting problem.

Original: Then, we design a novel aggregation algorithm that contains threemodules, namely, class-speciﬁc deep attention (CDA), positive-negative-aware modeling (PNM), andweakly-supervised cross-slide contrastive learning (WSCL).
Revised: Then, we design a novel aggregation algorithm that contains threemodules, namely, class-speciﬁc deep attention (CDA), positive-negative-aware modeling (PNM), andweakly-supervised cross-slide contrastive learning (WSCL).

Original: The CDA follows the previous deepattention paradigm to assign a learnable weight for each patch to indicate its contribution to the WSIprediction.
Revised: The CDA follows the previous deepattention paradigm to assign a learnable weight for each patch to indicate its contribution to the WSIprediction.

Original: The PNM explicitly models the appearance of positive and negative patches within WSIsto capture discriminative feature representations, promoting normal/abnormal tissue separation.
Revised: The PNM explicitly models the appearance of positive and negative patches within WSIsto capture discriminative feature representations, promoting normal/abnormal tissue separation.

Original: The
Revised: The

Original: WSCL constructs diverse feature comparisons across WSIs to reﬁne task-speciﬁc features, where the
Revised: WSCL constructs diverse feature comparisons across WSIs to reﬁne task-speciﬁc features, where the

Original: WSI-level supervision enables more reliable separation capabilities for each class in the contrastivelearning setting, helping capture informative features.
Revised: WSI-level supervision enables more reliable separation capabilities for each class in the contrastivelearning setting, helping capture informative features.

Original: Our contributions can be summarized as follows.
Revised: Our contributions can be summarized as follows.

Original: (i) Pioneeringly, a novel WSCL module is proposedfor global feature contrast across WSIs, which helps extract more distinguishable features to facilitateboth inter-class separability and intra-class compactness.
Revised: (i) Pioneeringly, a novel WSCL module is proposedfor global feature contrast across WSIs, which helps extract more distinguishable features to facilitateboth inter-class separability and intra-class compactness.

Original: (ii)
Revised: (ii)

Original: The PNM is designed to explicitly spliteach WSI feature space into positive and negative subspaces, thus helping exclude uninformativepatches.
Revised: The PNM is designed to explicitly spliteach WSI feature space into positive and negative subspaces, thus helping exclude uninformativepatches.

Original: (iii)
Revised: (iii)

Original: Our proposed SCL-WC achieves a signiﬁcant performance gain compared with other
Revised: Our proposed SCL-WC achieves a signiﬁcant performance gain compared with other

Original: WSWC methods.
Revised: WSWC methods.

Original: By feasibly extending it to a semi-supervised classiﬁcation task, our method wonﬁrst place in the BRIGHT challenge.
Revised: By feasibly extending it to a semi-supervised classiﬁcation task, our method wonﬁrst place in the BRIGHT challenge.

Original:  Related work2.1 Weakly-supervised WSI classiﬁcation
Revised:  Related work2.1 Weakly-supervised WSI classiﬁcation

Original: The WSWC task aims to select representative patches to trigger the corresponding WSI-level labels.
Revised: The WSWC task aims to select representative patches to trigger the corresponding WSI-level labels.

Original: Currently, MIL has been applied to formulate this problem with remarkable success [5; 6; 7; 8; 9;10; 11; 12; 13; 14], which requires two key techniques: patch-level feature encoding and featureaggregation for WSI representation.
Revised: Currently, MIL has been applied to formulate this problem with remarkable success [5; 6; 7; 8; 9;10; 11; 12; 13; 14], which requires two key techniques: patch-level feature encoding and featureaggregation for WSI representation.

Original: These feature encoders can be divided into online and ofﬂine models, where the online networksrequire real-time updates [5; 6; 7], resulting in more training epochs to converge compared to thewell-pretrained ofﬂine models.
Revised: These feature encoders can be divided into online and ofﬂine models, where the online networksrequire real-time updates [5; 6; 7], resulting in more training epochs to converge compared to thewell-pretrained ofﬂine models.

Original: The utilized ofﬂine feature extractors include supervised ImageNetpretrained [8; 9; 10; 11; 12; 14] and unsupervised histopathological-image-pretrained models [13].
Revised: The utilized ofﬂine feature extractors include supervised ImageNetpretrained [8; 9; 10; 11; 12; 14] and unsupervised histopathological-image-pretrained models [13].

Original: However, the natural images (out-of-domain data) are difﬁcult to accurately capture the texturaland morphological characteristics of histopathological images without any ﬁne-tuning.
Revised: However, the natural images (out-of-domain data) are difﬁcult to accurately capture the texturaland morphological characteristics of histopathological images without any ﬁne-tuning.

Original: The used
Revised: The used

Original: Whole-slide images/bags {𝒔 𝑛 } 𝑛=1𝑁
Revised: Whole-slide images/bags {𝒔 𝑛 } 𝑛=1𝑁

Original: Patches (instances) 𝒔
Revised: Patches (instances) 𝒔

Original: 𝑛 ={𝒑 𝑛,𝑙 } 𝑙=1𝐿 𝑛 cropping
Revised: 𝑛 ={𝒑 𝑛,𝑙 } 𝑙=1𝐿 𝑛 cropping

Original: Encoder …
Revised: Encoder …

Original: Features of patches 𝑿 𝑛 ={𝒙 𝑛,𝑙 } 𝑙=1𝐿 𝑛
Revised: Features of patches 𝑿 𝑛 ={𝒙 𝑛,𝑙 } 𝑙=1𝐿 𝑛

Original: MLP projector …
Revised: MLP projector …

Original: Task-specific features 𝑭 𝑛 ={𝒇 𝑛,𝑙 } 𝑙=1𝐿 𝑛
Revised: Task-specific features 𝑭 𝑛 ={𝒇 𝑛,𝑙 } 𝑙=1𝐿 𝑛

Original: R e L U F C
Revised: R e L U F C

Original: AttenTaskk AttenTask
Revised: AttenTaskk AttenTask

Original: FC FC
Revised: FC FC

Original: Tanh
Revised: Tanh

Original: Sigmoid … …
Revised: Sigmoid … …

Original: Classifier ℒ 𝐶𝐷𝐴
Revised: Classifier ℒ 𝐶𝐷𝐴

Original: Attention 𝐴 Feature …
Revised: Attention 𝐴 Feature …

Original: Selection
Revised: Selection

Original: FC FC ReLU
Revised: FC FC ReLU

Original: Attention ሚ𝐴
Revised: Attention ሚ𝐴

Original: Classifier
Revised: Classifier

Original: Classifier ℒ 𝑃𝑁𝑀
Revised: Classifier ℒ 𝑃𝑁𝑀

Original: Feature
Revised: Feature

Original: Feature 𝑾 𝑛 𝑾 𝑛𝑘 𝜽 𝑡𝑎𝑠𝑘 𝜽𝜽 2 Classifier 1− ሚ𝐴 … … .
Revised: Feature 𝑾 𝑛 𝑾 𝑛𝑘 𝜽 𝑡𝑎𝑠𝑘 𝜽𝜽 2 Classifier 1− ሚ𝐴 … … .

Original: [Figure]
Revised: [Figure]

Original: Feature encoding …
Revised: Feature encoding …

Original: [Figure]
Revised: [Figure]

Original: self-supervised model is pretrained on a small number of unlabeled samples, resulting in
Revised: self-supervised model is pretrained on a small number of unlabeled samples, resulting in

Original: The utilized feature aggregation algorithms can be divided into two lines: partial-instance-contributedand full-instance-contributed methods.
Revised: The utilized feature aggregation algorithms can be divided into two lines: partial-instance-contributedand full-instance-contributed methods.

Original: The partial-instance-contributed methods keep a ﬁxed numberof patches in each WSI by randomly sampling from tissue regions [7], selecting the top-k patcheswith high conﬁdence scores [5], or selecting a subset from each patch-level cluster [6; 9].
Revised: The partial-instance-contributed methods keep a ﬁxed numberof patches in each WSI by randomly sampling from tissue regions [7], selecting the top-k patcheswith high conﬁdence scores [5], or selecting a subset from each patch-level cluster [6; 9].

Original: A smallportion of patches may not fully capture the morphological features for each WSI, leading tomisdiagnosis and missed diagnoses.
Revised: A smallportion of patches may not fully capture the morphological features for each WSI, leading tomisdiagnosis and missed diagnoses.

Original: The full-instance-contributed methods adopt deep attentionpooling [10; 11; 12; 13], graph neural network [14], and sequence model [5; 8] to integrate all patcheswithin a WSI, where patches can be assigned appropriate weight parameters by the network trainingto drive a WSI-level representation.
Revised: The full-instance-contributed methods adopt deep attentionpooling [10; 11; 12; 13], graph neural network [14], and sequence model [5; 8] to integrate all patcheswithin a WSI, where patches can be assigned appropriate weight parameters by the network trainingto drive a WSI-level representation.

Original: These methods take into account the contributions of all patchesand are more ﬂexible and adaptable to other tasks than previous limited representative patches.
Revised: These methods take into account the contributions of all patchesand are more ﬂexible and adaptable to other tasks than previous limited representative patches.

Original: 2.2 Contrastive representation learning
Revised: 2.2 Contrastive representation learning

Original: Contrastive representation learning aims to learn a universal feature by pulling samples belonging tothe same class together and pushing samples belonging to different classes apart.
Revised: Contrastive representation learning aims to learn a universal feature by pulling samples belonging tothe same class together and pushing samples belonging to different classes apart.

Original: The most popularunsupervised contrastive learning methods are SimCLR [18; 19] and MoCo [20; 21; 16], which takedata augmentations from the same image as positives and those from different images as negatives.
Revised: The most popularunsupervised contrastive learning methods are SimCLR [18; 19] and MoCo [20; 21; 16], which takedata augmentations from the same image as positives and those from different images as negatives.

Original: Inlater supervised contrastive learning, data annotations help to formulate the correct contrastive pairs,resulting in more representative features [22].
Revised: Inlater supervised contrastive learning, data annotations help to formulate the correct contrastive pairs,resulting in more representative features [22].

Original: Based on previous studies, this work proposes a WSCLmodule in a MIL setting, aiming to extract class-speciﬁc distinguishable features in speciﬁc tasks.
Revised: Based on previous studies, this work proposes a WSCLmodule in a MIL setting, aiming to extract class-speciﬁc distinguishable features in speciﬁc tasks.

Original:  Methods
Revised:  Methods

Original: 3.1 Deﬁnition for the WSWC task
Revised: 3.1 Deﬁnition for the WSWC task

Original: Suppose we have a series of training WSIs { s n } Nn =1 and their corresponding slide-level labels{ y n } Nn =1 , where y n ∈ { 0 , 1 , · · · , C } represents  label of the n th slide, C denotes the number ofclasses (e.g., cancer subtypes) and 0 corresponds to the normal (negative) tissues.
Revised: Suppose we have a series of training WSIs { s n } Nn =1 and their corresponding slide-level labels{ y n } Nn =1 , where y n ∈ { 0 , 1 , · · · , C } represents the label of the n th slide, C denotes the number ofclasses (e.g., cancer subtypes) and 0 corresponds to the normal (negative) tissues.

Original: The n th slide canbe represented as s n = (cid:8) p n,l (cid:9) L n l =1 , where p n,l is the l th patches cropped from the slide by a slidingwindow, L n denotes the number of patches within the slide, which may vary across slides of differentimage sizes.
Revised: The n th slide canbe represented as s n = (cid:8) p n,l (cid:9) L n l =1 , where p n,l is the l th patches cropped from the slide by a slidingwindow, L n denotes the number of patches within the slide, which may vary across slides of differentimage sizes.

Original: It is noted that only slide-level annotations exist and annotations for internal patches arenot explicit.
Revised: It is noted that only slide-level annotations exist and annotations for internal patches arenot explicit.

Original: The WSWC task aims to train a model with weak labels to conduct WSI-level prediction.
Revised: The WSWC task aims to train a model with weak labels to conduct WSI-level prediction.

Original: The overview of our proposed SCL-WC method is shown in Figure 1, which mainly includes afeature encoding process and a task-speciﬁc feature aggregation.
Revised: The overview of our proposed SCL-WC method is shown in Figure 1, which mainly includes afeature encoding process and a task-speciﬁc feature aggregation.

Original: 3.2 Feature encoding
Revised: 3.2 Feature encoding

Original: Self-supervised learning (SSL) has the ability to train a universal feature encoder under the supervisionof data itself, which has been widely used in computer vision and medical image analysis [18; 20;23; 24].
Revised: Self-supervised learning (SSL) has the ability to train a universal feature encoder under the supervisionof data itself, which has been widely used in computer vision and medical image analysis [18; 20;23; 24].

Original: Beneﬁting from its remarkable success, this work applies MoCo V3 framework [20; 16]to pretrain a feature encoder (Swin-Transformer [17]) on 15 million unlabeled patches from TCGA[25] and PAIP [26] datasets.
Revised: Beneﬁting from its remarkable success, this work applies MoCo v3 framework [20; 16] topretrain a feature encoder (Swin-Transformer [17]) on 15 million unlabeled patches from TCGA[25] and PAIP [26] datasets.

Original: Then, the well-pretrained backbone is employed as an ofﬂine featureextractor to transform patches into a series of q -dimensional vectors.
Revised: Then, the well-pretrained backbone is employed as an ofﬂine featureextractor to transform patches into a series of q -dimensional vectors.

Original: For example, the featurevectors of the n th slide can be represented as X n = { x n,l } L n l =1 , where x n,l ∈ R q denotes the vectorof the l th patch and X n ∈ R L n × q is obtained by stacking all patches within the WSI.
Revised: For example, the featurevectors of the n th slide can be represented as X n = { x n,l } L n l =1 , where x n,l ∈ R q denotes the vectorof the l th patch and X n ∈ R L n × q is obtained by stacking all patches within the WSI.

Original: Due to thetask-agnostic characteristic of these pretrained features, we map them into a task-speciﬁc space usingfully connected layers and non-linear activation functions.
Revised: Due to thetask-agnostic characteristic of these pretrained features, we map them into a task-speciﬁc space usingfully connected layers and non-linear activation functions.

Original: Thus, the task-speciﬁc features in the n thslide can be represented as F n = { f n,l } L n l =1 = ReLU (FC ( X n , θ task )) , where F n ∈ R L n × d , θ task istrainable parameters in the fully connected layers.
Revised: Thus, the task-speciﬁc features in the n thslide can be represented as F n = { f n,l } L n l =1 = ReLU (FC ( X n , θ task )) , where F n ∈ R L n × d , θ task istrainable parameters in the fully connected layers.

Original: 3.3 Task-speciﬁc feature aggregation
Revised: 3.3 Task-speciﬁc feature aggregation

Original: Our task-speciﬁc feature aggregator consists of three modules: CDA, PNM, and WSCL.
Revised: Our task-speciﬁc feature aggregator consists of three modules: CDA, PNM, and WSCL.

Original: The CDAaims to parameterize the contribution of each patch to the ﬁnal WSI prediction, helping provideinterpretable results.
Revised: The CDAaims to parameterize the contribution of each patch to the ﬁnal WSI prediction, helping provideinterpretable results.

Original: The PNM is composed of positive-aware loss and negative-aware loss, whichis designed to mitigate the noise caused by the large proportion of normal (negative) subregionsin each WSI.
Revised: The PNM is composed of positive-aware loss and negative-aware loss, whichis designed to mitigate the noise caused by the large proportion of normal (negative) subregionsin each WSI.

Original: The WSCL considers global image information by pulling positive bags closer andpushing negative ones away.
Revised: The WSCL considers global image information by pulling positive bags closer andpushing negative ones away.

Original: The combination of these three modules enables the feature aggregatorto explore the intra- and inter-WSI complementary information, helping tune the task-speciﬁc featurelayers for more discriminative patch-level representations and further improving weakly-supervisedclassiﬁcation and localization performance.
Revised: The combination of these three modules enables the feature aggregatorto explore the intra- and inter-WSI complementary information, helping tune the task-speciﬁc featurelayers for more discriminative patch-level representations and further improving weakly-supervisedclassiﬁcation and localization performance.

Original: Class-speciﬁc deep attention.
Revised: Class-speciﬁc deep attention.

Original: The CDA module acts as a main branch to aggregate these patchlevel features F n = { f n,l } L n l =1 into a slide-level vector ˜F n using deep attention-based MIL pooling[15] that assigns a weight for each patch within WSI to specify its relative contribution to the ﬁnal
Revised: The CDA module acts as a main branch to aggregate these patchlevel features F n = { f n,l } L n l =1 into a slide-level vector ˜F n using deep attention-based MIL pooling[15] that assigns a weight for each patch within WSI to specify its relative contribution to the ﬁnal

Original: WSI prediction.
Revised: WSI prediction.

Original: This deep attention mechanism is complemented by several fully connected layers.
Revised: This deep attention mechanism is complemented by several fully connected layers.

Original: We use A i,ln to denote the weight score of the l th patch in the n th slide for the i th class, which iscalculated as
Revised: We use A i,ln to denote the weight score of the l th patch in the n th slide for the i th class, which iscalculated as

Original: [Equation]
Revised: [Equation]

Original:  where θ 1 ∈ R M × d , θ 2 ∈ R M × d , W in ∈ R 1 × M , and (cid:12) denotes element-wise multiplication.
Revised: (1) where θ 1 ∈ R M × d , θ 2 ∈ R M × d , W in ∈ R 1 × M , and (cid:12) denotes element-wise multiplication.

Original: Theattention score A i,ln ranges from 0 to 1 and the ﬁnal attention matrix for each class in each slide isnormalized
Revised: Theattention score A i,ln ranges from 0 to 1 and the ﬁnal attention matrix for each class in each slide isnormalized

Original: such that the sum of these weights is 1, i.e. , (cid:80) L n l =1 A
Revised: such that the sum of these weights is 1, i.e. , (cid:80) L n l =1 A

Original: i,ln = 1 .
Revised: i,ln = 1 .

Revised: The attention matrix for then th slide is calculated as A n ∈ R L n × C .

Original: And then, the weightedslide-level feature of the i th class can be represented as ˜F in ∈ R d , which is computed by
Revised: And then, the weighted slide-level feature of the i th classcan be represented as ˜F in ∈ R d , which is computed by

Original: [Equation]
Revised: [Equation]

Original: Next, these features are fed into the i th classiﬁer to drive the corresponding predicted probabilityfor the class.
Revised: Next, these features are fed into the i th classiﬁer to drive the corresponding predicted probabilityfor the class.

Original: Then, softmax is applied over each class to normalize the probability distribution.
Revised: Then, softmax is applied over each class to normalize the probability distribution.

Original: Weuse p in to represent the probability that the slide belongs to the i th category and y in to denote theground-truth label of the n th slide in the one-hot form.
Revised: Weuse p in to represent the probability that the slide belongs to the i th category and y in to denote theground-truth label of the n th slide in the one-hot form.

Original: They are used to calculate the MIL-basedslide classiﬁcation loss L mil in the min-batch size of B as follows.
Revised: They are used to calculate the MIL-basedslide classiﬁcation loss L mil in the min-batch size of B as follows.

Original: [Equation]
Revised: [Equation]

Original: In addition to the L mil , we also consider adding an auxiliary instance discrimination loss to furtherenhance the class-speciﬁc features.
Revised: In addition to the L mil , we also consider adding an auxiliary instance discrimination loss to furtherenhance the class-speciﬁc features.

Original: In speciﬁc, we aim to discriminate these high-attention patches(top- k with pseudo labels of 1) from low-attention patches (bottom- k with pseudo labels of 0) withineach WSI, which contains a total of 2 k samples for the classiﬁcation task using a linear classiﬁerwith binary cross-entropy loss as follows.
Revised: Due to the unavailability of the patch-level annotations, wegenerate pseudo labels of 1 for these high-attention patches (i.e., top- k attention score) and 0 for theselow-attention patches (i.e., bottom- k attention score) by sorting the attention scores in a speciﬁedcolumn of A n corresponding to the real class of the WSI, which contains a total of 2 k samples forthe classiﬁcation task using a linear classiﬁer with binary cross-entropy loss as follows.

Original: [Equation]
Revised: [Equation]

Original: where y j and p j denote the pseudo label and predicted probability of the j th instance, respectively.
Revised: where y j and p j denote the pseudo label and predicted probability of the j th instance, respectively.

Original: The ﬁnal CDA-based loss L CDA is the summed as follows:
Revised: The ﬁnal CDA-based loss L CDA is the summed as follows:

Original: L CDA = λ 1 L mil + λ 2 L ins .
Revised: L CDA = λ 1 L mil + λ 2 L ins .

Original: Positive-negative-aware modeling.
Revised: Positive-negative-aware modeling.

Original: We propose a PNM module to consider the presence of alarge number of normal (negative) tissues within positive WSIs, which should be separated fromthe abnormal (positive) regions as much as possible.
Revised: We propose a PNM module to consider the presence of alarge number of normal (negative) tissues within positive WSIs, which should be separated from theabnormal (positive) regions as much as possible.

Original: To achieve this, we split the feature space ofpositive WSI into positive and negative subspaces to enable distinguishable feature learning.
Revised: To achieve this, we split the feature space of positive WSI into positive and negative subspaces to enable distinguishable feature learning.

Original: Inspeciﬁc, we ﬁrst characterize the relevance of the l th patch to the slide-level prediction by calculatingits class-agnostic weight score ˜
Revised: Speciﬁcally, weﬁrst characterize the relevance of the l th patch to the slide-level prediction by calculating its classagnostic weight score ˜

Original: A l .
Revised: A ln through two fully connected layers , i.e., ˜ A ln = sigm( θ 4 (ReLU( θ 3 f (cid:62) n,l ))) .

Original: Then we use 1 − ˜
Revised: Then we use 1 − ˜

Original: A l to weight the slide-level feature that focuseson the negative subregions.
Revised: A ln to weight the slide-level feature that focuses on the negative subregions.

Original: These new weighted slide features are calculated by
Revised: Thesenew weighted slide features are calculated by

Original: [Equation]
Revised: [Equation]

Original: where F posn and F negn are the weighted slide-level feature for the prediction of positive and negativesamples, respectively.
Revised: where F posn and F negn are the weighted slide-level feature for the prediction of positive and negativesamples, respectively.

Original: ˜
Revised: ˜

Original: A l is the weight score for the l th patch within the WSI, which ranges from 0and 1.
Revised: A ln is the weight score for the l th patch within the WSI, which ranges from 0and 1.

Original: Then the slide-level prediction probability for positive p pos n and negative p neg n features can beobtained by feeding F posn and F negn into a fully connected layer with a softmax function.
Revised: Then the slide-level prediction probability for positive p pos n and negative p neg n features can beobtained by feeding F posn and F negn into a fully connected layer with a softmax function.

Original: Then, the
Revised: Then, the

Original: PNM-based classiﬁcation loss can be calculated as
Revised: PNM-based classiﬁcation loss can be calculated as

Original: [Equation]
Revised: [Equation]

Original: where L PNM is a summation of two cross-entropy loss functions: positive-aware classiﬁcation lossand negative-aware classiﬁcation loss.
Revised: where L PNM is a summation of two cross-entropy loss functions: positive-aware classiﬁcation lossand negative-aware classiﬁcation loss.

Original: Weakly-supervised cross-slide contrastive learning.
Revised: Weakly-supervised cross-slide contrastive learning.

Original: Previous contrastive learning methods applied on histopathological images adopt patches as positive/negative units [13], which ignores theinformation interaction across slides and captures only local feature representation.
Revised: Previous contrastive learning methods applied on histopathological images adopt patches as positive/negative units [13], which ignores theinformation interaction across slides and captures only local feature representation.

Original: Different fromthem, we propose a WSCL that aims to generate more discriminative class-speciﬁc features by comparing feature representations across slides, i.e., pulling slides belonging to the same class closer andpushing slides belonging to different classes away.
Revised: Different fromthem, we propose a WSCL that aims to generate more discriminative class-speciﬁc features by comparing feature representations across slides, i.e., pulling slides belonging to the same class closer and pushing slides belonging to different classes away.

Original: Due to the huge heterogeneity within each WSI,direct comparisons between WSIs are susceptible to interference from noise, which is instead harmful to network training.
Revised: Due to the huge heterogeneity within each WSI,direct comparisons between WSIs are susceptible to interference from noise, which is instead harmfulto network training.

Original: Thus, we construct a new series of bags by selecting the most representativepatches within each WSI based on the class-speciﬁc attention obtained above.
Revised: Thus, we construct a new series of bags by selecting the most representativepatches within each WSI based on the class-speciﬁc attention obtained above.

Original: Speciﬁcally, we usethree 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.
Revised: Speciﬁcally, we usethree 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.

Original: Similarly, the top- kand bottom- k patches within each negative WSI are inserted into hard negative and negative bags,respectively.
Revised: Similarly, the top- kand bottom- k patches within each negative WSI are inserted into hard negative and negative bags,respectively.

Original: It is noted that samples belonging to different positive categories should be stored inseparate positive bags.
Revised: It is noted that samples belonging to different positive categories should be stored inseparate positive bags.

Original: Based on the above deﬁnition, our WSCL-based loss can be deﬁned by
Revised: Based on the above deﬁnition, our WSCL-based loss can be deﬁned by

Original: [Equation]
Revised: [Equation]

Original: where B is an anchor bag, z i ( i ∈ B ) is the i th anchor patch from the anchor bag.
Revised: where B is an anchor bag, z i ( i ∈ B ) is the i th anchor patch from the anchor bag.

Original: For an anchor bag,
Revised: For an anchor bag,

Original: P , N , and H are used to represent its corresponding sets of positive, negative, and hard negativebags, and | P | , | N | , and | H | are used to represent their corresponding bag number, respectively.
Revised: P , N , and H are used to represent its corresponding sets of positive, negative, and hard negativebags, and | P | , | N | , and | H | are used to represent their corresponding bag number, respectively.

Original: p r isused to represent the r th bag in the positive set P .
Revised: p r isused to represent the r th bag in the positive set P .

Original: Q = P ∪ N ∪ H denotes the total bags used inthe WSCL calculation process.
Revised: Q = P ∪ N ∪ H denotes the total bags used inthe WSCL calculation process.

Original: The ﬁnal loss function is the summation of the CDA-based loss, PNM-based loss, and WSCLbased loss, which is computed as L total =
Revised: The ﬁnal loss function is the summation of the CDA-based loss, PNM-based loss, and WSCL-basedloss, which is computed as L total =

Original: α L CDA + β L PNM + γ L WSCL , whereα , β, and γ arehyper-parameters used to adjust the contribution of each loss.
Revised: L CDA + β L PNM + γ L WSCL , where β and γ are hyper-parametersused to adjust the contribution of each loss.

Original:  Experiments
Revised:  Experiments

Original: In this section, we construct a series of experiments on four public histopathological image datasetsto verify state-of-the-art performance of our proposed method.
Revised: In this section, we construct a series of experiments on four public histopathological image datasets.

Original: The four datasets are collected fromtwo different organs, including prostate (PANDA [27] and DiagSet [28]) and breast (Camelyon16[29] and BRACS [30]), which are detailed in Table 1.
Revised: The four datasets are collected from two different organs, including prostate (PANDA [27] and DiagSet [28]) and breast (Camelyon16 [29] and BRACS [30]), which are detailed in Table 1.

Original: Datasets.
Revised: Datasets.

Original: Camelyon16 is released for the classiﬁcation of benign tissue (159 WSIs) and metastaticbreast cancer (240 WSIs).
Revised: Camelyon16 is released for metastatic breast cancer detection, which contains 240 WSIswith metastasis and 159 normal WSIs.

Original: These WSIs are split into a training set of 270 WSIs and a test set of [-
-] WSIs by the provider.
Revised: These WSIs are split into a training set of 270 WSIs and a testset of [-
-]129 WSIs by the provider.

Original: Although pixel-level annotations are available in the dataset, they are onlyused to evaluate our weakly-supervised localization performance.
Revised: Although pixel-level annotations are available in the dataset, theyare only used to evaluate our weakly-supervised localization performance.

Original: BRACS is released at the BRIGHT challenge for the classiﬁcation of breast tumor subtyping.
Revised: BRACS is released at the BRIGHT challenge for the classiﬁcation of breast tumor subtyping.

Original: Wefollow the challenge for a 3-class WSI classiﬁcation: non-cancerous (Non. with 288 WSIs), precancerous (Pre. with 155 WSIs), and cancerous (Can. with 260 WSIs).
Revised: Wefollow the challenge for a 3-class WSI classiﬁcation: non-cancerous (Non. with 288 WSIs), precancerous (Pre. with 155 WSIs), and cancerous (Can. with 260 WSIs).

Original: The challenge organizer splitsthe total of 703 WSIs into a training set of 423 WSIs, a test-1 set of 80 WSIs, and a test-2 set of 

Revised: The challenge organizer splitsthe total of 703 WSIs into a training set of 423 WSIs, a test-1 set of 80 WSIs, and a test-2 set of

Original: WSIs.
Revised: WSIs.

Original: It is noted that some well-annotated patches (3566) are also provided for training, which areignored in our WSWC task and are utilized in the training process of semi-supervised classiﬁcationwhen participating in the challenge.
Revised: It is noted that some well-annotated patches (3566) are also provided for training, which areignored in our WSWC task and are utilized in the training process of semi-supervised classiﬁcationwhen participating in the challenge.

Original: PANDA is the largest publicly available WSI data for 2-class prostate cancer classiﬁcation, whichreleases a total of 10,616 WSIs (7,724 Can.
Revised: PANDA is the largest publicly available WSI data for 2-class prostate cancer classiﬁcation, whichreleases a total of 10,616 WSIs (7724 Can.

Original: and 2,892 Non.).
Revised: and 2892 Non.).

Original: We split them into training, validation,and test sets with a ratio of 7:1:2.
Revised: We split them into training, validation,and test sets with a ratio of 7:1:2.

Original: DiagSet contains three subsets of histopathological images for 2-class prostate cancer classiﬁcation:
Revised: DiagSet contains three subsets of histopathological images for 2-class prostate cancer classiﬁcation:

Original: DiagSet-A, DiagSet-B, and DiagSet-C. These subsets are adopted as three external test sets todemonstrate the model generalizability to unseen data.
Revised: DiagSet-A, DiagSet-B, and DiagSet-C. These subsets are adopted as three external test sets todemonstrate the model generalizability to unseen data.

Original: DiagSet-A, DiagSet-B, and DiagSet-C contain430 WSIs (Can: 228 WSIs and Non.: 202 WSIs), 4675 WSIs (Can: 2090 and Non.: 2585 WSIs), and46 WSIs (Can: 37 WSIs and Non.: 9 WSIs), respectively.
Revised: DiagSet-A, DiagSet-B, and DiagSet-C contain430 WSIs (Can: 228 WSIs and Non.: 202 WSIs), 4675 WSIs (Can: 2090 and Non.: 2585 WSIs), and46 WSIs (Can: 37 WSIs and Non.: 9 WSIs), respectively.

Original: Evaluation metrics.
Revised: Evaluation metrics.

Original: For a fair comparison with previous methods, we adopt accuracy (ACC), areaunder the curve (AUC), and F1 score as metrics to evaluate our weakly-supervised classiﬁcationperformance.
Revised: For a fair comparison with previous methods, we adopt accuracy (ACC), areaunder the curve (AUC), and F1 score as metrics to evaluate our weakly-supervised classiﬁcationperformance.

Original: Following the Camelyon16 challenge, free response operating characteristic curves(FROC) is used to assess the tumor localization performance [29].
Revised: Following the Camelyon16 challenge, free response operating characteristic curves [Table] [Table] (FROC) is used to assess the tumor localization performance [29].

Original: The experimental setups can beseen in supplementary materials. [Table] [Table] [Table]
Revised: The experimental setups can beseen in the Appendix.

Original: [Table]

Original: 4.1 Results on Camelyon16 dataset
Revised: 4.1 Results on Camelyon16 dataset

Original: This subsection validates our weakly-supervised classiﬁcation and localization capacities on the
Revised: This subsection validates our weakly-supervised classiﬁcation and localization capacities on the

Original: Camelyon16 dataset by comparing it with state-of-the-art related methods.
Revised: Camelyon16 dataset by comparing it with state-of-the-art related methods.

Original: Detailed results areshown in Table 2, where the best result is bolded, and the second best is underlined.
Revised: Detailed results areshown in Table 2, where the best result is bolded, and the second best is underlined.

Original: It is noted that,except for the CLAM and TransMIL algorithms, previous methods are all reported in [13], whichare implemented using their corresponding ofﬁcial codes along with the SSL-pretrained features by[13].
Revised: It is noted that,except for the CLAM and TransMIL algorithms, previous methods are all reported in [13], whichare implemented using their corresponding ofﬁcial codes along with the SSL-pretrained features by[13].

Original: The CLAM and TransMIL algorithms are implemented by us using their released code and ourpretrained features.
Revised: The CLAM and TransMIL algorithms are implemented by us using their released code and ourpretrained features.

Original: Thus we can directly compare our aggregation algorithm with these methods.
Revised: Thus we can directly compare our aggregation algorithm with these methods.

Original: As shown in Table 2, our method outperforms other WSWC algorithms to a large extent.
Revised: As shown in Table 2, our method outperforms other WSWC algorithms to a large extent.

Original: Forexample, our method outperforms the previous best-performing TransMIL by around 2% in ACC and2% in AUC.
Revised: Forexample, our method outperforms the previous best-performing TransMIL by around 2% in ACC and2% in AUC.

Original: The reason can be analyzed as follows.
Revised: The reason can be analyzed as follows.

Original: In the Camelyon16 dataset, the percentage ofanomalous regions within each WSI is typically below 10%.
Revised: In the Camelyon16 dataset, the percentage ofanomalous regions within each WSI is typically below 10%.

Original: Thus, previous methods are particularlysusceptible to noise, resulting in possibly missed detection.
Revised: Thus, previous methods are particularlysusceptible to noise, resulting in possibly missed detection.

Original: Our proposed SCL-WC enables distinctivecategorical feature extraction by intra-WSI and inter-WSI complementary information converging,helping alleviate the problem of a small percentage of lesions within WSIs.
Revised: Our proposed SCL-WC enables distinctivecategorical feature extraction by intra-WSI and inter-WSI complementary information converging,helping alleviate the problem of a small percentage of lesions within WSIs.

Original: The CDA module assigns a learnable weight for each patch to represent its importance for the WSIprediction, which is combined with the feature reﬁnement process in the WSCL and PNM modulesto promote better lesion localization ability.
Revised: The CDA module assigns a learnable weight for each patch to represent its importance for the WSIprediction, which is combined with the feature reﬁnement process in the WSCL and PNM modulesto promote better lesion localization ability.

Original: The detailed diagnosis-positive localization results areshown in Table 2, Figure 2, and supplementary materials.
Revised: The detailed diagnosis-positive localization results areshown in Table 2, Figure 2, and Appendix.

Original: As shown in Table 2, our method achievesan FROC of 0.5659, which outperforms over 10% than other methods and shows the potentialtobe close to  fully-supervised performance with an FROC of 0.6543.
Revised: As shown in Table 2, our method achieves an FROCof 0.5659, which outperforms over 10% than other methods and shows the potential to be closeto fully-supervised performance with an FROC of 0.6543.

Original: In Figure 2 and supplementarymaterials, the warmer colored subregions imply a higher probability of abnormal tissues, whichvisually demonstrates our superior localization performance even for tiny lesions.
Revised: In Figure 2 and Appendix, the warmercolored subregions imply a higher probability of abnormal tissues, which visually demonstrates oursuperior localization performance even for tiny lesions.

Original: 4.2 Results on BRACS, PANDA, and DiagSet datasets
Revised: 4.2 Results on BRACS, PANDA, and DiagSet datasets

Original: In this subsection, we ﬁrst conduct 3-class weakly-supervised classiﬁcation experiments on the
Revised: In this subsection, we ﬁrst conduct 3-class weakly-supervised classiﬁcation experiments on the

Original: BRACS dataset in Table 3.
Revised: BRACS dataset in Table 3.

Original: The results of these compared methods are obtained according to theirreleased ofﬁcial codes.
Revised: The results of these compared methods are obtained according to theirreleased ofﬁcial codes.

Original: The 3-class classiﬁcation task is very challenging due to the indistinguishablefeatures 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 ductalhyperplasia V.S. ductal carcinoma in situ).
Revised: The 3-class classiﬁcation task is very challenging due to the indistinguishablefeatures 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 ductalhyperplasia V.S. ductal carcinoma in situ).

Original: Thus, the overall classiﬁcation performance is less than90% in the current state-of-the-art methods.
Revised: Thus, the overall classiﬁcation performance is less than90% in the current state-of-the-art methods.

Original: The previous best-performed method is the DSMILmethod in the BRACS dataset, however, it shows suboptimal performance in other datasets (e.g., 
Revised: The previous best-performed method is the DSMILmethod in the BRACS dataset, however, it shows suboptimal performance in other datasets (e.g., [Table] [Table]

Original: Table 2 and Table 5).
Revised: Table 2 and Table 5).

Original: Our proposed SCL-WC remains most effective on all of these datasets, whichreﬂects its stability.
Revised: Our proposed SCL-WC remains most effective on all of these datasets [Table] whichreﬂects its stability.

Original: Then, in Table 4, we show our results when participating in the 2022 BRIGHT challenge, which isachieved using a semi-supervised classiﬁcation scheme extended from our SCL-WC, with the littledifference in whether a small number of well-annotated patches are used.
Revised: Then, in Table [Table] we show our results when participating in the 2022 BRIGHT challenge, which isachieved using a semi-supervised classiﬁcation scheme extended from our SCL-WC, with the littledifference in whether a small number of well-annotated patches are used.

Original: As shown in Table 4, ourmethod ranked ﬁrst in the BRIGHT challenge, which shows the high ﬂexibility and scalability of ourproposed SCL-WC method.
Revised: As shown in Table 4, ourmethod ranked ﬁrst in the BRIGHT challenge, which shows the high ﬂexibility and scalability of ourproposed SCL-WC method.

Original: Next, we validate our algorithm on two larger prostate datasets.
Revised: Next, we validate our algorithm on two larger prostate datasets.

Original: Also, extensively external testsare performed to further conﬁrm the robustness and generalization performance of our algorithm.
Revised: Also, extensively external testsare performed to further conﬁrm the robustness and generalization performance of our algorithm.

Original: The detailed results are summarized in Table 5, where the PANDA dataset is used for algorithmdevelopment and inner validation, and the remaining three subsets of DiagSet are used for externaltests.
Revised: The detailed results are summarized in Table 5, where the PANDA dataset is used for algorithmdevelopment and inner validation, and the remaining three subsets of DiagSet are used for externaltests.

Original: The results of these compared methods are obtained using their released codes.
Revised: The results of these compared methods are obtained using their released codes.

Original: As shownin Table 5, our method consistently performs better on these datasets. Speciﬁcally, our methodoutperforms the CLAM method by around 3% ACC and 2% AUC in the PANDA dataset. In the
Revised: As shown

Original: [Table] [Table]
Revised: [Table] [Table]

Original: (P+N)
Revised: in Table 5, our method consistently performs better on these datasets.

Original: [Table] SSL + L CDA + L PNM + L WSCL (P+N+HN) [Table]
Revised: Speciﬁcally, our methodoutperforms the CLAM method by around 3% ACC and 2% AUC in the PANDA dataset.

Original: [Table] [Table] largest external test set DiagSet-B, our method surpasses DSMIL by around 10% and 3% in ACCand AUC, respectively.
Revised: In thelargest external test set DiagSet-B, our method surpasses DSMIL by around 10% and 3% in ACCand AUC, respectively.

Original: 4.3 Ablation study
Revised: 4.3 Ablation study

Original: We construct a set of ablation experiments in Table 6 to demonstrate the effectiveness of keycomponents in our proposed SCL-WC method, including SSL-based feature extractor, L CDA , L PNM ,and L WSCL (with and without hard negatives).
Revised: We construct a set of ablation experiments in Table 6 to demonstrate the effectiveness of keycomponents in our proposed SCL-WC method, including SSL-based feature extractor, L CDA , L PNM ,and L WSCL (with and without hard negatives).

Original: As shown in Table 6, the results in the ﬁrst two rows show that, compared to the ImageNetpretrained network, the SSL-based histopathology-pretrained feature extractor improves around 3%on Camelyon16 and 2% on BRACS in terms of AUC.
Revised: As shown in Table 6, the results in the ﬁrst two rowsshow that, compared to the ImageNet-pretrained network, the SSL-based histopathology-pretrainedfeature extractor improves around 3% on Camelyon16 and 2% on BRACS in terms of AUC.

Original: As shown in the second and third rows of Table6, our positive-negative-aware loss brings a consistent 1% performance gain across all datasets andmetrics, which veriﬁes the importance of the PNM module.
Revised: Asshown in the second and third rows of Table 6, our positive-negative-aware loss brings a consistent1% performance gain across all datasets and metrics, which veriﬁes the importance of the PNMmodule.

Original: The effectiveness of the WSCL modulecan be seen in the last three rows of Table 6.
Revised: The effectiveness of the WSCL module can be seen in the last three rows of Table 6.

Original: When hard-negative samples are not considered, theperformance is improved by around 2% in both datasets compared to the results on the third row, andthe performance improves further when hard negative samples are added as shown in the last row of Table 6.
Revised: Whenhard-negative samples are not considered, the performance is improved by around 2% in both datasetscompared to the results on the third row, and the performance improves further when hard negativesamples are added as shown in the last row of Table 6.

Original:  Conclusion
Revised:  Conclusion

Original: We propose a novel WSWC method called SCL-WC, which is constructed based on a domain-speciﬁc
Revised: We propose a novel WSWC method called SCL-WC, which is constructed based on a domain-speciﬁc

Original: SSL feature extractor and a task-speciﬁc feature aggregator.
Revised: SSL feature extractor and a task-speciﬁc feature aggregator.

Original: The feature aggregator includes threeeffective modules: CDA, PNM, and WSCL, which are combined for discriminative patch-level featurereﬁnement, providing not only interpretable results but also ﬁne lesion localization.
Revised: The feature aggregator includes threeeffective modules: CDA, PNM, and WSCL, which are combined for discriminative patch-level featurereﬁnement, providing not only interpretable results but also ﬁne lesion localization.

Original: The proposed
Revised: The proposed

Original: SCL-WC method outperforms state-of-the-art WSWC studies over four publicly available datasetsfor the binary/multiple classiﬁcation tasks, which also shows good feasibility and scalability in theweakly-supervised localization and semi-supervised classiﬁcation tasks.
Revised: SCL-WC method outperforms state-of-the-art WSWC studies over four publicly available datasetsfor the binary/multiple classiﬁcation tasks, which also shows good feasibility and scalability in theweakly-supervised localization and semi-supervised classiﬁcation tasks.

Original: However, our method shouldbe extensively validated in larger cohorts from real-world clinical settings before its deployment, andwe will explore this in future work.Checklist For all authors...
Revised: However, our method shouldbe extensively validated in larger cohorts from real-world clinical settings before its deployment, andwe will explore this in future work.

Original: (a) Do the main claims made in the abstract and introduction accurately reﬂect the paper’s

Original: contributions and scope?

Original: [Yes]

Original: (b) Did you describe the limitations of your work?

Original: [Yes] See Section 5.

Original: (c) Did you discuss any potential negative societal impacts of your work?

Original: [Yes] Seesupplementary materials.

Original: (d) Have you read the ethics review guidelines and ensured that your paper conforms tothem?

Original: [Yes] I read it carefully and this work ﬁts them perfectly.

Original: If you are including theoretical results...

Original: (a) Did you state the full set of assumptions of all theoretical results?

Original: [Yes] See Section 3.

Original: (b) Did you include complete proofs of all theoretical results?

Original: [Yes] See Section 3.

Original: If you ran experiments...

Original: (a) Did you include the code, data, and instructions needed to reproduce the main experi-

Original: mental results (either in the supplemental material or as a URL)?

Original: [Yes] See Section 4and supplementary materials.

Original: (b) Did you specify all the training details (e

Original: g., data splits, hyperparameters, how theywere chosen)?

Original: [Yes] See Section 4 and supplementary materials.

Original: (c) Did you report error bars (e

Original: g., with respect to the random seed after running experiments multiple times)?

Original: [Yes] See supplementary materials.

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Original: g., typeof GPUs, internal cluster, or cloud provider)?

Original: [Yes] See supplementary materials.

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Original: g., code, data, models) or curating/releasing new assets...

Original: (a) If your work uses existing assets, did you cite the creators?

Original: [Yes] See Section 4.

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Original: [No] We are unable to ﬁnd the license for

Original: these publicly available dataset.

Original: (c) Did you include any new assets either in the supplemental material or as a URL?

Original: [Yes]

Original: See supplementary materials.

Original: (d) Did you discuss whether and how consent was obtained from people whose data you’re

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Original: [Yes] These datasets we used are publicly available.

Original: (e) Did you discuss whether the data you are using/curating contains personally identiﬁable

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Original: [No] These publicly available data have been

Original: desensitized.

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Original: applicable?

Original: [N/A]

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Original: [N/A

Original: c) Did you include the estimated hourly wage paid to participants and the total amount

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Original: [N/A]