Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology
Authors: Dhananjay Tomar
Alexander Binder
Andreas Kleppe
Venue: NeurIPS 2024 poster (2024)
Keywords: Deep learning
domain generalization
histopathology
computational pathology
digital pathology
computer vision
single domain generalization
Primary area: machine_vision
TL;DR: Focusing on shape and organisation of nuclei (domain invariant features) leads to improved single domain generalisation and shows that nuclei have sufficient information to detect cancer.

Abstract: Domain generalisation in computational histopathology is challenging because the images are substantially affected by differences among hospitals due to factors like fixation and staining of tissue and imaging equipment. We hypothesise that focusing on nuclei can improve the out-of-domain (OOD) generalisation in cancer detection. We propose a simple approach to improve OOD generalisation for cancer detection by focusing on nuclear morphology and organisation, as these are domain-invariant features critical in cancer detection. Our approach integrates original images with nuclear segmentation masks during training, encouraging the model to prioritise nuclei and their spatial arrangement. Going beyond mere data augmentation, we introduce a regularisation technique that aligns the representations of masks and original images. We show, using multiple datasets, that our method improves OOD generalisation and also leads to increased robustness to image corruptions and adversarial attacks. The source code is available at https://github.com/undercutspiky/SFL/

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology
Dhananjay Tomar University Oslo Mamanjt@ifi. teifi.uio.no... no
Alexander Binder
Soo-von--Guriche University Magdeburg, Singapore Institute of Technology alexander. binder@ovgu.de
Andreas Kleppe*
Oslo University Hospital, University of Oslo, UiT The Arctic University of Norway andrekle@if fi. wwt.iiiiiii.i....mm .oo.no
Abstract
Domain generalisation in computational histopathology challenging because the images are substantially affected by differences among hospitals due to factors like fixation and staining tissue and imaging equipment. hypothesise that focusing nuclei can improve the out-of-domain (OOD) generalisation in cancer detection. We propose a simple approach to improve OOD generalisation for cancer detection by focusing nuclear morphology and organisation, these domain invariinnn features critical in cancer detection. Our approach integrates original images with nuclear segmentation masks during training, encouraging the model to prioritise nuclei and their spatial arrangement. Going beyond mere data augmentation, we introduce a regularisation technique that aligns the representations of masks and original images. We show, using multiple datasets, that our method improves OOD generalisation and also leads increased robustness image corruptions and adversarial attacks. The source code is available at https: /ttp//gttttb/.t//////// com/ undercutspikyySSLL
Introduction
Domain generalisation in histopathology is a crucial challenge because domain shifts naturally occur among hospitals and even within single hospital or laboratory, e.g., temporally among human operators and observers such as pathologists. Non-biological factors that substantially alter the images include differences in scanners, staining protocols, fixation of tissue, and even minor aspects like the manufacturer and storage conditions of stains [I]
Collecting data from numerous hospitals to address these domain shifts is often impractical and may not adequately reflect the full variability present routine clinical practice, thus making difficult to build computational histopathology models that generalise well. This leads to focus on single-domain generalisation (S-DG) in this paper, specifically on how to train a model using data from only one hospital (considered domain here) that generalises well to data from other hospitals. Popular S-GD methods in histopathology apply data augmentation and stain normalisation 3 The effectiveness of S-GD methods developed for natural images remains underexplored in histopathology [3] Here, we compare these methods to new, simple approach that we propose.
"Correponning author.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Research has shown that Convolutional Neural Networks (CNNs) tend to focus on texture over shape 4 [5. However, in histopathology, the texture and colour of cell nuclei vary much more across domains than the shape and organisation of cell nuclei. As result, focusing on shape features could improve a computational histopathology model's ability to generalise to unseen data because may rely less on domain-specifi features that vary across hospitals and more.
Nuclei in cancerous tissue exhibit distinct changes in shape, size, and overall organisation compared to nuclei in normal tissue [68]] Pathologists rely on these and other visual cues [9] for cancer diagnosis and grading, underscoring the biological importance and the consistency of nuclear morphology and organisation across domains. We hypothesise that focusing on nuclear morphology and organisation may be sufficient for cancer detection and that exploiting this during training could result in models with good generalisation.
We propose a method that encourages CNNs focus more on nuclear morphology and organisation by using additional loss terms that prioritise shape-based features. Specifically, our method leverages nuclear segmentation masks during training to steer the learning towards nuclei. Through extensive experimentation, we demonstrate that this method improves performance on out-of-domain data without requiring nuclear segmentation masks inference time, thus offering promising and attractive solution for addressing domain generalisation in histopathology. Our contributions include:
We propose novel training method that incentivises the model to focus nuclei.
We evaluate our method on three datasets comprising hundreds of WSIs in total from various hospitals and organs. Our results show accuracy improvements over all other approaches.
We evaluate the sensitivity of our method to image corruptions and adversarial attacks. Our results show performance improvements over the baseline.
We conduct extensive ablation studies to show that models trained with our method focus on nuclei.
2 Related work
The prediction of various properties such as malignancy, grading, and HER2 expression using segmented nuclei has been a well-studied topic for many years (60 Researchers have employed techniques such as watershed segmentation [17], thresholding, level sets [18], and snakes [1]] often followed by extracting explicit morphometric features from the segmentations. For example, early work by HHaseaawa et al. [20] focused on counting segmented regions, while Lee and Street [21 applied neural networks to the segmentation outputs. contrast to these approaches, our method does not rely on segmentation during inference. Instead, we adjust the training process to encourage the extraction of nuclear features.
Stain normalisation methods convert the colours of source image to match those of a target image. These methods were typically designed specifically for the most common type of histopathology images, which are images of tissue stained with haematoxylin and cosin (H&E). One of the earlier methods, Macenko normalisation [l] estimates stain vectors for source and target images and uses them to normalise the source image. Vahadane et al. [22 proposed a method that decouples stain "density maps" from "colour appearances" allowing the combination of the source image's ' density maps with the target image's is colour appearances, Reinhard et al. 223 pioneered colour transfer by adjusting the global statistics of images characteristics of the target to the source image. Random Stain Normalization and Augmentation (RandStainNA) (24) combines stain normalisation and augmentation. Unlike traditional approaches that normalise using a fixed template, RandStainNA generates random virtual templates in the LAB 233 colour space and uses them to normalise the images during training. The templates are drawn from Gaussian distributions whose means and variances a more comprehensive review, we refer the readers to [25). In summary, the stain normalisation methods primarily focus manipulating colour information to remove stain variability. On the other hand, our approach shifts the focus from colour manipulation to nuclear features.
different colour space, effectively transferring the colour
derived from the training data. For
Data augmentation is a common way to facilitate domain generalisation. Tellez et al. 2 evaluated several stain colour augmentation and stain normalisation methods and found that colour augmentation was crucial for good performance on external test sets in histopathology. Faryna et al. 226 extended Rugmenttent (27] by including certain histopathology-speee ones that produce crreaalistc-lookkig images. Tellez et al. 28 developed data augmentation method
augmentations and excluding the

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specific to HtE-st images and used it for domain generalisation in mitosis detection. Pohjonen al.] 29 developed StrongAugment where varying numbers of transformations are applied to an image to improve domain generalisation. Marini et al. 33 proposed Data-driven colour augmentation (DDCA), which evaluates an augmented image as acceptable or not for training based on its distance from other images in database. |aaryna et al. [31] evaluated different data augmentation strategies in histopathology, including manually selected augmentations, and found them all to be competitive.
Single-domain generalisation (S-DG) methods do not require data from multiple domains dur- ing training. Representation Self-Challenging (RSC) [32] works by discarding the features with relatively high gradients, making the model predict with the remaining features during training. Adversarial Domain Augmentation (ADA) [3] generates adversarial examples iteratively augment the source domain and creates an ensemble of models. Meta--earning-baee -bannng---eddd-- ADA (M-ADA) [3
uses Wasserstein Auto-Encoder (355 to generate new samples and uses adversarial training on top along with meta-learning scheme. Progressive domain expansion network (PDEN) [6) uses multiple autoencoders to generate new samples to expand the training set. Learning to Diversify (L2D) 37 introduces learnable style-complement module that generates augmented images. The style-complement module is trained to diversify the images as much as possible but still keep the semantic information intact.
Domain adaptation, unlike S-DG, requires having access to some samples from the target domain. In histopathology, domain adaptation methods commonly make use of GAN (38] and CycleGAN 39 StainGAN 40 uses CycleGAN to make images the source domain look like the target domain. Residual CycleGAN 41 modifies the CycleGAN objective to have the generator produce the residual between domains instead of recreating the input image. In (42). authors augment a generator in CycleGAN with a stain colour matrix as an auxiliary input to stabilise the training. NST_AD AD_HRNet (43 uses Neural Style Transfer 44 45] and GAN to preserve the content of the source image while combining it with the style of the target image. In some earlier works [46] 47), the input image is converted to greyscale and then coloured using generator network which is based on target image. While domain adaptation is not S-DG and thus bit tangential to the focus of this paper, it is worth noting that domain adaptation is impractical in many clinical settings and may result in worse generalisation than stain normalisation and colour augmentation [48).
3 Proposed Method
Our approach aims enhance S-DG by incentivising the model to focus shaped- based features of nuclei in histopathological images and thereby reduce overfitting to irrelevant features that may carry higher label noise.
The first step involves generating segmentation masks that highlight specific areas of interest in the image. This step is applied only during training, while test-time evaluation relies solely on H&E- stained images. As we hypothesised that nuclear morphology and organisation contain sufficient information for cancer detection, our segmentation masks are binary images with nuclear pixels as foreground and other pixels as background.
One possible approach to using the segmentation mask is to include it as a fourth channel in the input image. Alternatively, the mask can be used as the sole input to the model. However, both methods necessitate running the segmentation model during inference, which increases computational demands and slows down processing.
Our method circumvents the need for a nuclear segmentation network at inference time by incor- porating additional loss terms during training. For a given input image x and its corresponding segmentation mask x', our method involves the following steps:
1. Execute forward propagation through the neural network model on both the H&B-sttined image a and its nuclear mask x', saving the embeddings generated by the network as z and z' for x and x'. respectively.
2. Compute the Binary Cross-Entropy (BCE) loss for both and x'.
3. Compute the fi-distance between the embeddings = and z'.
4. Minimise the sum of the two CE losses and the -ddisttac..
3

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Probability:
50%
GAP and
classification
Probability:
50%
BCE Loss
ResNet-50 [without GAP and classification layers)
Euclidean distance
BCE Loss
Figure I: We pass the input image (or, with 0.5 probability, input image multiplied with its nuclear segmentation mask) and its nuclear segmentation mask through the network and minimise the Binary Cross-Entropy (BCE) loss for both the input image and its mask. Additionally, we minimise the f2-distance between the input image's embedding vector and the mask's embedding vector just before the Global Average Pooling (GAP) layer. The embedding vector is ResNet-50's penultimate layer's feature map, i.e., stage 4's last feature map.
Our approach is illustrated in Figure I We employ ResNet-50 449 from Torchvision 50 the base model. We next discuss some details of our approach.
ez-regularisation: To encourage the network to focus nuclei, we minimise the distance between the feature map of the original image and that of its nuclear segmentation mask. We use the flattened feature map from ResNet-50's penultimate layer, just before Global Average Pooling, to obtain the embeddings. The regularisation term consists of the f2-distance between the embeddings of the original image and its mask z', which is added to the BCE losses for both the image and the mask. Let ý be the model's prediction for x, ÿ' for x', and y be the ground truth. Then, the total loss is:
A|| z 11 BCE 1) BCE f/)
(I)
where BCE is the Binary Cross-Entrope loss function for labels in {0, 1}:
BCF(y.P) = - log(p) - y) log(1 [1 - -p))
(2)
Original image times mask: Since the embeddings of the original image and its binary segmentation mask may differ significantly, minimizing their fg-distance can be challenging for the model. To address this, with probability of 0.5, multiply the original image by its segmentation mask, i.e., the network receives æ & x' as input half the time instead of x. By multiplying with the segmentation mask, everything the original image except the nuclei is looks like. By simplifying the task, the network can more easily reduce the distance between the embeddings of the nuclei-only image and the mask and gradually improve alignment between the embeddings of the original image and the mask. We found this augmentation to help stabilise training.
Figure shows what the output
Experiments
4.1 Datasets
CAMELYONI7 51 dataset consists of 1000 H&E-stained Whole Slide Images (WSIs) of breast cancer metastases in lymph node sections from five medical centres in the Netherlands. It contains pixel-level annotations of tumours for 10 WSIs from each medical centre, giving us 50 WSIs to work with. WSIs from centres 0, 3 and 4 were scanned using the same scanner, while the other two centres used a different scanner each. All slides were scanned at 40x resolution. We treat each centre as a different domain.
BCSS 52 dataset consists of 151 Htt-stt WSIs of histologically primary breast cancer cases from The Cancer Genome Atlas (TCGA) with triple-negative status determined from

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clinical data files, All WSIs have resolution of 40x. The WSIs were annotated at the pixel level using crowdsoureing. Each pixel can have one of the many labels. We consider the label "tumor" to define pixels with tumour and all other labels except "outsideroo""" "exclude", or "undetermined" to define pixels without a tumour.
Ocelot [53] dataset consists of pixel-level annotations of tumour vs non-tumour pixels for 303 WSIs from TCGA. consists of WSIs of primary tumour from six different organs: Bladder, Endometrium, Head-and-neck, Kidney, Prostate, and Stomach. The annotations in the dataset are at low resolution, so we upscale the annotations to 40x. We exclude two WSIs that have only 20x resolution; all other WSIs have x0x resolution.
4.2 Dataset preparation
We use code from WILDS [54 55 to prepare the CAMELYON17 dataset with modifications. Tiles are sized (270 x 270) at 40x resolution. For each domain (medical centre), data is split by patient, ensuring all tiles from a patient are in single subset. Since the number of tiles varies drastically across patients, we shuffle patients so that the validation subset contains 20%-25% of all tiles per domain. Our processed version of CAMELYON17 is available at [56].
4.3 Experiment setup
We train models using the CAMELYON17 dataset, treating each medical centre as a distinct domain. We use the BCSS and Ocelot datasets external test datasets. To avoid multiple comparisons and overly optimistic performance estimates, we use the external test datasets only once during the entire project, solely to evaluate the final models (57].
For each combination of medical centre and method, we train ten models using the train subset of that centre. Thus, we train 50 models in total for each method, We use the loss on the validation dataset (of the training domain) to select the best model for each training. All models are trained for 50 epochs using the Adam optimiser with a learning rate 4e-5 and weight decay of 1e-4. We use exponential learning rate decay with a decay rate of 0.955. For our method, we set the parameter in equation (T to À 0 for the first five epochs, effectively training without f--distance loss in these epochs, and then use À = 1 for the rest of the training. We start saving models for selection of the one with lowest validation loss after ten epochs for our method allow the network to stabilise while from the first epoch for other methods. For all experiments, unless stated otherwise, we use Re 49 model pre-trained on ImageNet [58]. We use Ver-NNNe (59 trained on the CoNSeP (59 dataset to generate nuclear segmentation masks.
While domain generalisation encompasses a wide variety of methods, have selected several exemplary baselines for comparison: Macenko normalisation [. RSC [32]. L.D (37). RandStain (RandSNA in result tables) (24), and DDCA [30]. We also include baseline where initialise ResNet-50 with pre- pre-trained weights from Ho Ner-eeeet [59]. These methods represent different ap- proaches, including stain normalisation (Macenko, RandStainNA) and generating augmented images (L2D). By selecting these diverse techniques, we ensure a comprchensive evaluation of our method's performance across various S-DG strategies.
is important to note that our method can be integrated with many existing S-DG approaches, making it a flexible plug-in solution rather than direct competitor. We evaluate most methods with and without the photometric augmentations selected for ERM. After testing various augmen- tation strategies available in Torchvision (50, we identified the most effective combination to be: 1.5], contrast=(O.5. 1.5), saturation=(O.5. 1 1.5], hue=[-0.3, 0.3]) and BaussianBlur(kernn)) ) Results using these augmentations are marked '-Aug' in the results tables. In all experiments, including those without photometric augmentations, we apply the basic geometric augmentations: random horizontal and vertical flips. For all ViT-Tiny [60] experiments, we also add affine augmentations: random rotation (up to 90%) and translation (up to 45 pixels).
Babooiittrr(righhneess-0
We ran the experiments two clusters with GPUs with 64 GB (AMD MI250X) and 24 GB (Nvidia RTX 3090) GPU RAM each. Each job consumed about 21 to 31 GB of GPU RAM. The proposed method took 5 to 20 hours to train, depending on the train data size while ERM took 2.5 to 11 hours.
We report tile-level accuracy for tumour vs non-tumour tile classification for all datasets. Additionally, we measure robustness image noise by measuring the accuracy drop CAMELYON17 for image
5

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Table 1: Out-of-domain accuracy on CAMELYON17. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best in italics. Method "Ours-no-(z-A" is shorthand for "Ours-no-2z-Aug" and refers to our approach without fz- regularisation. Method "Ours-MO-Aug" refers to approach with masks only, that is, neither using fz-regularisation nor using mask- boak--iims--iputt augmentation of H&F images with 50% probability during training. A paired t-test for "L2D-Aug" versus "Ours-Aug" yields p-value of 10
Method
Centre-O
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
72.8 + 2.3
65.9 + 3.7
64.1 + 3.0
55.0 + 1.3
53.8 + 3.0
62.4 + 2.7
Macenko
79.3 + 2.1
62.4
73.3 5.0
65.8 2.3
85.9 + 4.2
73.3 + 3.0
Vereet
72.5 2.4
2.5
61.3
1.91
8..
61.9
RandSNA
7.1
4.9
2.5
2.8
2.6
63.6 3.2
RSC
3.2
6.15
6.89
2..
51.1
62.3 2.9
L2D
93.6 + 1.0
2..9
64.4
4.3
77.8 84.4 4.9
Ours
90.4 1.5
90.1 0.3
1.3
8.71
9
89.2 1.3
ERM-Aug
93.1 1.0
7.1
2.8
1.5
9.6
8.8
Macenko-Aug
86.3 = 1.9
1.5
86.2
2.8
1.2
2.3
HoVerNet-Aug
93.0
8.8
11.
1.6
2.2
1.7
RandSNA-Aug
92.7 1.1
83.1
91.0
3.0
87.4 91.1
DDCA-Aug
92.5
1..4
80.4
78.
90.2
2.5
RSC-Aug
93.1
78.2
3.4
7..
91.0
85.9 2.0
L2D-Aug
9
8.66
87.7
83.4
0.9
8.11
Ours-Aug
91.8 + 0.7
92.9 9.6
91.7 9..4+1111
0.5
91.8
ours-no-Lz-A
92.1 0.8
2.9
91.8 1L882.0 2.0
8.0
1.7
87.8 + 1.7
Ours-MO-Aug
91.8 + 2.2
2.1
85.0 + 2.2
79.7 4.2
77.9 + 2.8
84.5 + 2.7
corruptions introduced in [6]. This includes Gaussian-, shot-, impulse- and snow-noise, and two blur types, clastic transform and JPEG compression.
Results on CAMELYON17 (yymph node sections) We test models on their respective out-of- domain data. E.g., model trained on Centre-3 is tested on all the data from 1,ntre-0,1,2.44 Our method attains 10% higher accuracy than the next best method (L2D) when none used photometric augmentations and was also superior when photometric augmentations were used (Table IT.
Results on BCSS (primary breast cancer) The accuracy of the models trained on a centre in CAMELYONI7 drops substantially (12% to 14%) for all the methods when tested on BCSS (Table 2 compared to when tested on other centres in CAMELYONI7 (Table [T. This could be due to mismatch between pathologists' annotations on CAMELYON17 and BCSS but also due to the biological differences between these tissue types. In particular, epithelial cells in lymph nodes would almost certainly be tumour cells, while they could be benign cells ordinary breast tissue. These results show that the relative performance on CAMELYONI7 for different methods is indicative of relative performance on an external test set, the performance drop is similar for all methods.
Results Ocelot (primary non-breast cancer) We test our model on the Ocelot dataset to evaluate if our method helps to train models that generalise to other organs as well. Ocelot does not have any data from breast tissue nor does it include lymph node sections (which all the models have been trained on). We report the results of these experiments in Table While our method achieves the highest accuracy also in this casc, the difference between our method and L2D is not big as it is for CAMELYONI7 and BCSS. Taking a closer look into the performance for separate organs and 9 in the Supplement), we can see that our method performs worse than L2D Endometrium and Kidney, where accuracies are generally lower, and better in the four other organs. This indicates that models trained with our method generalise worse to organs where the transferability from breast tissue is generally low. This is at least the case for Kidney which has by far the lowest accuracies across all methods. Generalising to different cancer types is emerging experimental topic; see, for example, [62]
(Tables 4
5 6
In summary, method yields better accuracy than the baselines, including other S-DG approaches.

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Table 2: Out-of-domane accuracy on BCSS. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best in italics. A paired 1-test for "I.2DD-ug" versus "Ours-Aug yields a p-value of 10-5.
Method
Centre-0
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
58.0 = 2.9
6 2.8
52.0 + 1.0
50.0 0.1
52.1 3.9
56.3 + 2.1
Macenko
68.7 = 2.4
56.5 1
65.5 + 2.6
56.8 2.3
71.1 1 3.4
63.7 + 2.5
VerNee
55.7 + 2.0
2.2
53.8+00
19.8+11
47.5 2.9
54.4 + 2.0
RandSNA
60.5 + 2.8
6..4
51.0
5.6 5.71
55.8 1.9
RSC
63.3 + 3.7
65.7
1.0
5.11
0.5
56.0 +
L2D
79.9 1.2
6.72
2.3
4.52
68.4 2.2
Ours
74.2 = 3.6
2.2
1.9
2.88
2.3
72.3 = 2.6
ERM-Aug
80.1 + 1.6
2.
73.7
1.89
73.3 2.6
71.7 + 2.3
Macenko-Aug
75.8 + 2.9
2.46
2.6
57.
75.3 1.8
69.8 2.5
Ho VerNeetAAug
79.8 +: 1.4
6.61
2.2
6.1 2..0
69.9 4.7 2.7
RandSNA-Aug
2.7
73.2
3.2
64.5
7..1
7.0
DDCA-Aug
1.9
71.7
7.81
3.2
7.3
3.8
RSC-Aug
79.6
7..1
1.7
6.
72.1 7.31
L2D-Aug
81.9
1 0.3
0.6
74.2
0.6
67.5
77.2 2.2
75. +
Ours-Aug
82.3 0.8
81.9
+
75.7
7.0
74.8 +
78.8
Table 3: Out-of-domain accuracy on Ocelot. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best in italics. A paired t-test for "L2D-Aug" versus "Ours- Aug" yields p-value of 0.044.
Method
Centre-0
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
65.7 + 2.4
55.8 1.7
51.7 + 1.3
45.6 + 1.4
53.5 + 5.0
54.5 + 2.4
Macenko
64.3 + 1.9
0.9
63.8 + 1.4
54.8
2.0
65.3 + 3.7
60.5 + 2.0
Ho VerNet
62.0 + 1.2
2.2
52.1
+ 0.7
48.0 1.9
54.3 2.9
54.0 1.8
RandSNA
67.0 2.5
5..1
0.3
46.3 1.9
2..2
54.3 1.8
RSC
68.1 2.4
1.0
0.6
4
0.8
54.4 1.3
L2D
68.2 1.3
0.5
26.4
3.3
6..1
59.6
Ours
67.9 - 1.4
7.0
62.1 1.2
69.2
67.3 + 1.3
ERM-Aug
74.0 1.4
1.9
2.56
1.6
6.1
65.6 2.1
Macenko-Aug
08.8
6.1
1.5
3.2
7..5
16.01199
Ho He
70.7
1.92
2.3
2.42
6..
2.1
RandSNA-Aug
70.8
66.8
68.4
2.5
71.7
2.7
DDCA-Aug
73.1 2.4
64.9
2.79
2.
66.9
66.2
RSC-Aug
71.9 +: 2.2
61.7+224
6..2
58.4+14
+0.
66.3 2.5
L2D-Aug
74.7 + 0.6
6.5
0.7
74.4 62.5
1.2
69.1 1.1
Ours-Aug
70.8 - 0.5
72.0 1.3
1.3
7.74
4
1.0
5 Ablation Study and Discussion
Impact of data augmentation Tables 2 and 3 shows that data augmentation benefits all methods substantially, which is consistent with well-established knowledge.
Impact of fz-regularisation The result labelled Ours-no-E2-A Table shows that using data aug- mentation with nuclear masks alone is insufficient to achieve high accuracy. Without fg-regularisation (i.e., setting = Equation (I), our method only slightly outperforms most baselines that also uses data augmentation. The key factor for effective cross-domain generalisation is the ability to align the feature representation of input images with corresponding mask images, which lack colour and texture. Further evidence supporting this alignment effect is presented in the next paragraph.
Impact of taaatiime--ppuu--ggmenttttti The result Ours-MO-Aug in Table demonstrates an ablation with two changes: the absence of Er-reullation and the removal of the 50% probability
7

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Original
Mask
Original * Mask
Black Nuclei
(a)
(b)
(c)
(d)
White Nuclei
Inverted Mask
Inpainted Nuclei
(e)
Figure 2: Exemplary image ablations used in this study.
mask-timess mask-times-input augmentation of H&E images during training (Figure D. but still having two CE loss terms, of them over nuclear masks. We further decline of accuracies below most baselines with photometric augmentations.
Impact of learned features without nuclear-mask-like features Here, bring further evidence for the effect of lg Eg-reuularisation about pulling the features towards the representation nuclear masks. Note that nuclear masks are not used during inference in standard evaluations such as all those in Tables 1 [
and In Table 10 in the Supplement, we can see results for predictions in which the embeddings (Teatures before the GAP layer) of the H&E images are modified by subtracting the embeddings of the corresponding nuclear masks. By comparing to Table I we see a drop in performance for all methods. However, the drop is largest for our method, with an accuracy below random guessing. This shows that the features computed from Etainee images at test time are indeed more similar to features from nuclear masks for our method than for other methods.
Impact of removal of intranuclear texture and colour In Tables 11 and [4] in the Supplement, we consider performance on modified H&E images, in which intranuclear texture removed by masking it out with constant colour (see examples in Figure 21 2b) This is of interest due to the observation that intranuclear texture is often different in cancerous nuclei, which can informative to humans. We can see that if it is replaced by colour similar to the colour of nuclei, we for our method obtain a performance (Table [T] very similar to the performance with original H&E images (Table T On the other hand, changing the colour white seems to reduce the performance notably (Table 14p. This is possibly due to the creation of images with outlier statistics. A more likely explanation is that it is common for H&E stains to have small holes gaps of white background colour in the stroma, which usually are not discriminative information but rather shear stress artefacts from the tissue cutting process. Therefore, masking nuclei with white masks may effectively remove discriminative information about nuclei. This domain-specific observation may explain the asymmetry in behaviour when masking nuclei with black versus white.
Impact of removal of extranuclear information Table 25 in the Supplement shows results on data where all the non-nun non-nuclear background is set to white (see example FigureZbb. These images can be viewed as an inside-out inverted case of the images evaluated to give the results in Table [I] morphology and organisation
The common information both sets images performance for our proposed method remains high on these images (Table 23). being close to the best result on original H&E data (Table The experiments in Tables 23 and demonstrate strong generalisability of focusing on nuclear morphology and organisation out-of-domain settings.
nuclei. The

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Impact of dilution of nuclear shapes We expand nuclei masks by a classic morphological dilation and then blacken the dilated regions in the H&E images. The nuclear shape information in these images is thus progressively reduced compared to the images where only the nuclei are filled with black. Across all methods, observe drop in accuracy with increase in dilation (Tables 12 and 13 in the Supplement), highlighting the critical role of shape in this domain. The proposed method more robust to moderate shape dilution with mask size of 5 than the baseline methods. A similar but stronger trend appears in Tables [4 and and in the Supplement for whitened nuclei.
Impact of removing nuclei We dilate the nuclear mask image with a kernel size of 5 encapsulate remnants of the boundary of nuclei and then use the dilated mask to remove nuclei by inpainting The accuracy with the resulting images (see example gg drops to random guessing for our method (Tables 17 and T8 in the Supplement). Essentially no tiles are classified as tumour, giving a nearly zero recall and low precision (Tables 19
63
20 21 and 22 the Supplement). Also, this supports that models trained using our method focus on nuclear morphology and organisation, and shows that the models reasonably associate the absence of nuclei with no tumour.
Saliency maps via Integrated Gradients To further demonstrate that method steers models to focus on nuclei, we generate saliency (pixel attribution) maps using Integrated Gradients [64] and show some randomly selected examples in Figures 6 in the Supplement. The saliency maps also indicate that a model trained using our method focuses on nuclei.
Evaluation of L2D and RSC combined with the proposed method Tables 26 27 and 28 in the Supplement show the results of combining L2D and RSC with the proposed method. Combining the proposed method, which regularises, with L2D, which diversifies, yields mixed results, likely due to the opposing effects of these two interventions. Combining it with RSC results in a small gain over using our method alone. Overall, this demonstrates the effectiveness of the method proposed.
Evaluation segmentation mask data For the sake of completeness, and 23 in the Supplement that our method also performs well when tested on nuclear masks (as exemplified in Figure 2)) and their inversions (see example in Figure 2
show in Tables 24
Evaluation of robustness to image corruptions Figure 3 shows that the proposed method has notably higher robustness to image corruptions for most experiments in eight types of corruptions described in [61]. Samples of corrupted images are shown in Figure 5 in the Supplement.
Evaluation of robustness against adversarial attacks We evaluated the robustness of models against adversarial attacks [63] using the Projected Gradient Descent (PGD) attack [[6]] Figure 4a demonstrates that models trained using our method have significantly higher robustness than ERM and L2D, the latter being the second- best performing method in Tables 2 and 3 Additionally,

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Figure 4: (a) PGD attack on models. (b) mooel PGD attacks where adversarial images are generated using model from method but the accuracy for those images is tested models from other methods. Results are for the validation subset of each centre CAMELYON17.
conduct cross-model attacks by generating adversarial images using models trained with one method and evaluating them on models trained with other methods. The results indicate that our models exhibit minimal performance degradation when exposed to adversarial images generated by models from other methods (Figure 4b). In contrast, the accuracy of models trained with ERM and L2D drops substantially. This further demonstrates the superior robustness of models from method.
preliminary evaluation on transformer architecture We perform a comparison using fine- tuned ViT-Tiny (60) model. The results shown in Tables 29 30 and 31 in the Supplement. These results show that our approach obtains superior out-of-domain performance for the CAMELYONI dataset. The results are more mixed for the other datasets. In particular, it seems that models trained on one of the five centres (Center-4) in CAMELYON17 do not generalise well to other cancer types and are actually also performing sub-optimally in CAMELYON17. For models trained each of the performance with our approach is, on average, better
the other four centres in CAMELY than with other approaches, but the performance increase is lower than for ResNet-50. However, in the same tissue type (CAMELYON YON17 data), the performance gain is similar for both ViT-Tiny and ResNet-50. Our interpretation of all these results is that approach performance also for ViT-Tiny, in particular across centres and scanners for the same tissue type, but that it might also fail for minority of the training datasets. This experiment preliminary because we took the same hyperparameters as used for ResNet-50, including the same learning rate and X = 1, both of which might not be optimal. Also, note that ViT-Tiny has much fewer parameters than ResNet-50. Experiments with larger transformers might obtain bigger differences, as seen in 67]
improve out-of-domain
Limitations of this study As limitation, identify that have performed these experiments for only one classification task. For medical practitioners, it would he of interest to measure the impact for other tasks, such as tumour grading and survival prediction, when evaluated in an out-of-domain generalisation setup. However, this would require access to multi-centre datasets with relevant labels available. Secondly, because we preferred to run a larger set of ablation experiments to understand what actually has been learned when using our method. While we expect results be qualitatively similar for other CNNs, transformer networks might have different learning dynamics, and results for those with a larger capacity than the ViT-Tiny are of interest in future work. Finally, an extension to other cancer types, such prostate or colon cancer, would also be of interest.
ran the full set of experiments only on one base network, ResNet-50,
6 Conclusion
We have shown simple method to enforce the learning of shape features at training time, which uses unmodified input images at inference time. It shows very good out-of-domain performance and can be combined as plugin with other methods to enhance out-of-domain generalisation. Aside from out-of-domain accuracy, the proposed method gives improved robustness image alterations.
10

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Acknowledgments and Disclosure of Funding
The computations were performed on resources provided by Sigma2 the National Infrastructure for Pigh-Peeformmace Computing and Data Storage in -hrwayy-thrugh Project NN8 104K. Addi- tionally, we acknowledge Sigma2 for access to the LUMI supercomputer, owned by the EuroHPC Joint Undertaking. hosted by CSC (Finland) and the LUMI consortium through Sigma2, Norway, Project 465000262. This work was supported by the authors' institutions, the Research Council of Norway (project number 309439), the National Research Foundation, Singapore, and Infocomm Media Development Authority under its Trust Tech Funding Initiative. Any opinions, findings, and conclusions recommendations expressed in this work those of the authors and do not reflect the views of their institutions, the Research Council of Norway, the National Research Foundation, Singapore, and Infocomm Media Development Authority.
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Technical Appendix Supplement
Table Out-of-doman accuracy on Ocelot evaluated only on the organ BLADDER. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best italics.
Method
Centre-O
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
65.0 t 3.1
58.2 3.5
50.4 0.6
46.4 + 1.8
53.1 + 6.9
54.6
Macenko
71.8 + 1.4
58.7 + 2.0
66.2 +
60.4 + 3.0
55.6+5..
64.6 + 2.9
RSC
70.3 3.0
50.2 2.7
47.8 1.4
50.0
1.6
L2D
72.0
1.4
5..5
38.2
5.14
61.5
Ours
2.7
74.9 1.7
64.3 74.6 1.8
3.3
74.0
72.4
ERM-Aug
76.6 1.9
68.3
+ 2.8
63.9
1.9
63.7
6.0
Macenko-Aug
71.9
65.9 1.1
2.4
6.6
2.3 2.0
RSC-Aug
72.7
67.9 2.9
6.87
6.67
6.1
66.7 3.3
L2D-Aug
0.7
7.44
6.9
2.5
1.3 2.2
Ours-Aug
77.3
78.2 2.3
1.8
0.8
73.3 1.7
75.9 - 1.5
Table 5: Out-of-domain accuracy on Ocelot evaluated only on the organ ENDOMETRIUM, The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best in italics.
Method
Centre-O
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
68.4 2.9
58.7 + 2.2
52.3 + 2.1
47.5 + 0.7
53.9 5.1
56.2 2.6
Macenko
68.9 + 2.7
57.4 2 2.1
71.6
53.7 + 1.8
68.4
64.0 + 2.4
RSC
71.3 2.0
58.1 1.4
0.8
48.4 1.0
50.5
55.8 - 1.3
L2D
76.6 + 3.0
58.0 0.5
64.5
57.4 + 4.7
62.1
63.7 3.1
Ours
70.6 + 1.3
72.6 1.4
63.9 2.2
62.6
66.9 6
67.3
ERM-Aug
78.5 1.6
64.1 + 2.6
7.81
56.5 + 2.6
3.90
69.4
Macenko-Aug
73.2
64.1 2.3
58.5 4.1 2.0
7.5
69.8 2.7
RSC-Aug
3.8 2.7
75.8
1.1
74.7
6.2
L2D-Aug
78.5 0.6
70.6
72.0
65.1 3.5
7.79
73.2 + 1.5
Ours-Aug
1.8
69.5 3.0
68.8 2.4
69.9 1.3
70.6 - 2.1
Table Out-of-domain accuracy on Ocelot evaluated only on the organ HEAD AND NECK. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best in italies.
Method
Centre-0
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
61.9 4.0
53.7 + 1.6
49.5 1.8
44.1 + 1.5
52.6 2.9
52.4 2,4
Macenko
57.7 + 2.1
54.1 1.111. 1.3
51.8
56.0 2.9
6..2
56.2 2..222 2.1
RSC
63.0 4.8
0.5
2.1
5..6
52.7
L2D
63.1 2.112.7 2.7
1.2
4..7
1.74
6..0
2.7
Ours
66.2
74.1
6.0
1.4
66.7 69.0
ERM-Aug
72.0 8.8
6..4
6.1
58.9
63.2
2.
Macenko-Aug
1.8
1.9
6.0
3..6
6..7
6..4
RSC-Aug
2.1
64.3+24
61.9
61.2
6..7
64.2
1.2D-Aug
73.7
6.12+1.1 0.5
04.+1.1
1.9
68.6
67.9 1.5
Ours-Aug
71.8 1.2
74.9 + 1.8
68.8
74.8 - 1.0
7.94+0.9
72.1 + 1.3
16

---

Table 7: Out-of-domain accuracy on Ocelot evaluated only on the organ KIDNEY. The column name indicates the centre used to train models. The best accuracy for each column bold face and the second best in italics.
Method
Centre-O
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
61.5 + 4.2
50.2 + 0.4
50.3 + 0.5
42.5 + 2.7
54.7 + 6.2
51.8 + 2.8
Macenko
58.3 1.9
50.0 + 0.7
58.0 2.0
52.6 + 2.1
62.1 4.3
56.2 2.2
RSC
62.1 + 4.5
50.5 0.7
50.0 + 0.1
45.4 + 2.5
50.9 -0.91.4
51.8
L2D
54.4 1.4
0.6
3.4
51.6 + 2.3
5.6
52.7 2.7
Ours
59.5 + 2.0
56.5 + 3.3
53.9 + 1.5
2.3
57.5 2.0
ERM-Aug
66.4 2.1
54.7
3.0
52.6 + 2.2
6.5
2.4
Macenko-Aug
59.7 2.0
52.5 0.9
63.4 2
54.7
+ 2.9
1.8
2.0
RSC-Aug
6.4
51.2
65.4
56.0
+ 2.1
68.7
61.5 + 3.2
L2D-Aug
69.5
58.6
59.0
57.7
63.3 7..7
1.2
Ours-Aug
1.1
65.7 3.2
57.6
59.1=11
60.9 6.10
Table 8: Out-of-domain accuracy on Ocelot evaluated only on the organ PROSTATE. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best italics.
Method
Centre-O
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
68.4 1.9
58.7 + 1.7
3.0
46.7 + 1.0
52.1 2.5
56.7
Macenko
51.5 11.5
66.8 2.3
54.0 1.2
68.5
2.2
RSC
70.8
6.1
2.4
4.7 1.2
1.6
L2D
0.8
0.7
2.7
2.2
58.9
1.7
Ours
7.44
75.1
0.3
74.7 6..
73.8 1.1
ERM-Aug
76.0 + 1.1
2.6
6.13 1.42
2.7
67.8 07.8118
Macenko-Aug
72.9
0.9
1.0
2.59
68.2 1.2
RSC-Aug
7.7
2.3
1.67
62.0
7..3
6..
L2D-Aug
0.5
0.5
7.41
64.7
74.1
0.9
Ours-Aug
0.2
7.17
76.3
7.
75.6 0.5
0.7
Table 9: Out-of-domaan accuracy Ocelot evaluated only the organ STOMACH. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best in italics.
Method
Centre-0
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
71.7
+ 2.4
56.9 + 3.3
50.8 + 1.6
47.5 + 0.7
53.0 6.4
56.0 + 2.9
Macenko
61.6
3.5
54.2 + 1.9
62.9 3.2
51.4 + 3.1
65.5
59.1 3.1
RSC
72.1 + 1.8
52.1
0.7
50.1 1.1::00
48.6 0.9
4..6
5.15
L2D
74.1 1.4
1.2
1.9
47.944.5
64.4 6..0
Ours
74.4 1.4
77.2 0.7
73.4
70.9 1.6
73.9
74.0
ERM-Aug
6.00
7..6
53.8
7..2
6.
Macenko-Aug
70.9
6..8
1.94
2.
7.0
2.44
RSC-Aug
73.3
2.5
7.86
5..3+2.0
2.0
2.2
L2D-Aug
77.0 0.5
7.60
6..1
2.
76.4
7.18
Ours-Aug
3.5 0.7
7.09
7.86
0.9
16
17

---

Table 10: Out-of-domain accuracy on CAMELYONI7 where embeddings for segmentation masks subtracted from the embeddings the original image see if the accuracy drops. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best in italics.
Method
Centre-0
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
63.0 7.4
64.2 + 8.3
58.2 3.6
52.3 + 3.1
52.6 3.1
58.1 + 5.1
Macenko
63.3 + 13.2
58.4 + 11.5
54.5
59.8 - 2.8
52.1 + 5.5
57.6 + 7.4
RSC
54.6 3.5
52.7
6.60
5.0
54.1
55.4 +: 5.2
L2D
1.9
4.0
70.1
4..
7.9
70.0 + 5.6
Ours
36.6 + 4.6
51.4 6.5
38.
3.2
8.8
44.1 + 6.0
ERM-Aug
9.2
72.1
64.8 10.1
67.8 11.5
Macenko-Aug
11.8
68.7
6..1
6.57
10.5
6.21
RSC-Aug
6900
644484
8.9
56.0
11.4
8.51
L2D-Aug
2.7
2.1
8.5
6.9
61.7#
7.9
Ours-Aug
6.6
413
4.5
3.9
48.4229
4.84
Table 11: Out-of-domain accuracy on CAMELYON17 where nuclei are blackened out. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best italics.
Method
Centre-O
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
61.2 + 7.3
53.1 + 2.7
55.7 = 11.7
50.1 + 0.1
51.2 + 3.6
54.3 5.1
Macenko
0.9
12.61...6
5.3
5..1
3.3
5.0
RSC
2.4 6.5
5.9
49.1 0.8
3.84
L2D
4.6
8.44
8.5
62. 7.8
7.3
Ours
9.0
1..6 0.3
86.0 29.1
90.1
ERM-Aug
8.7
8.3
8.4
58.48.11
5.84
6.13
Macenko-Aug
75.6
6.2
12.4
59.8884
72.7
10.::10.3
RSC-Aug
10.7
13.6
86.8
57.3
5..7
6.75
L2D-Aug
91.9 0.3
0.8
7..0
8.67
79.5
8..7
Ours-Aug
92.3 0.5
01.7#144
92.1
1.2
93.4 0.3
8.2
91.2 - 1.3
Table 12: Out-of-domain accuracy on CAMELYONI7 where nuclei are blackened out after being expanded with filter size 5, i.e., the blackened out part covers nuclei and some region around nuclei. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best in italics.
Method
Centre-O
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
56.7 6.1
52.4 + 2.7
55.7
12.2 50.1 0.2
50.0 + 3.3
53.0 4.9
Macenko
50.3 1.1
54.8
50.0 +.1
53.7
50.2
51.8 4.6
RSC
57.2
51.1
50.8
5..
51.8 4.6
L2D
78.0 8.4
8.7
8..
9.0
63.0
9.5
Ours
89.9 0.4
80.3 + 3.5
90.9
90.7+1.0
2.0
87.6 + 1.7
ERM-Aug
72.3 + 11.2
51.6 4 2.0
78.6+10.4
50.9
1:4222 2.3
50.1 60.140.22
60.7 5.1
Macenko-Aug
68.7 12.3
79.7 + 11.5
57.8+
53.2
7.3
64.7+13.1
+
RSC-Aug
69.5
59.8 + 12.4
8..7+7.0
0.5
63.1+61 5.21
L2D-Aug
90.3+00
78.6
2.3
6.1
81.7
78.8
79.4
Ours-Aug
89.9 0.4
75.0 + 2.6
91.4
01.50...
8..
86.8
18

---

Table 13: Out-of-domain accuracy on CAMELYONI7 where nuclei are blackened out after being expanded with filter size 9, i.e., the blackened out part covers nuclei and some region around nuclei. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best in italics.
Method
Centre-0
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
54.3 4.9
51.9 + 2.5
55.7 12.2
50.1 + 0.2
51.1 3.2
52.6 + 4.6
Macenko
50.3 :: 0.9
53.4 + 5.8
50.1 + 0.1
54.5 5.577.1 7.7
50.1
51.7 + 2.9
RSC
54.0 4.6
10.4::2..
50.3 + 1.0
4
50.9 + 1.7
L2D
70.9 + 10.9
10.6
76.0 + 12.3
88.
68.9 10.4 9.7
Ours
76.6 3.3
67.0
66.9
83.0 0.8
7.2
ERM-Aug
66.6 11.3
0.3
71.7
50.2
0.2
57.7 4.7
Macenko-Aue
61.9 10.8
10.6
8.1
51.6447
59.7 9..
RSC-Aug
6
9.73
7.5
50.1
5.11
59.9 6.2
L2D-Aug
71.8 8.5
3.0
71.9
72.3 71.
Ours-Aug
1.3
62.1 4.7
6.9
8..1
80.4 4 5.3
74.5 8.5
Table 14: Out-of-domain accuracy on CAMELYON17 where nuclei are whitened out. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best italics.
Method
Centre-0
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
61.3 + 2.8
50.0
1.4
50.0 0.0
50.1 0.1
48.3 + 2.4
51.9 + 1.3
Macenko
50.2
50.0 ::00000 0.0
0..
50.0 5.9
50.1 0.3
RSC
1.8 3.8
50.0
50.3 0.3
1.44
5.5
L2D
7.11 9.7
8.1
72.9
53.
64.9 6.4
Ours
79.6 = 6.0
07.82.11
6.5
60.7 5.2
5.2
5.0
ERM-Aug
51.3 2.8
6.2
11.0
2..1
52.5 5.5
Macenko-Aug
49.6
4..7
55.7+
0.5
50.9 5..0
RSC-Aug
52.9 4.5
5..
2.87
5..0
6.1
51.9
L2DDAug
85.5
1.1
55.8
0.2
59.4
5..5
61.1 3.3
Ours-Aug
67.4 7.6
92.1
67.3
7.2
8.75
74 + 6.4
Table 15: Out-of-domain accuracy CAMELYON17 where nuclei are whitened out after being expanded with filter size 5, i.e., the whitened out part covers nuclei and some region around nuclei. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best in italics.
Method
Centre-O
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
61.3 3.0
50.7 + 1.8
50.0 + 0.0
50.1 1 0.3
49.6 1.3
52.3 + 1.3
Macenko
50.5 - 0.9
50.0
50.0
50.3 0.6
50.0
50.2 + 0.3
RSC
63.4
5..6
5..0
0.3
5.8
52.9 + 1.5
L2D
69.0 + 7.5
64.2
53.
7..4
5.4
62.0 + 6.9
Ours
63.9 9.3
74.6 + 2.9
65.5
05.6
5.84
64.2 + 5.2
ERM-Aug
50.9 2.6
49.9
9.3
50.1 0.2
1.3
54.3 2.7
Macenko-Aug
51.2 1.3
50.2
8.5
52.5+22.
5..4+14
53.2 2.8
RSC-Aug
52.6 + 4.0
5.84
6.28+6.2
5.01
52.7 54.6
L2D-Aug
80.4 + 3.2
51.0
5..
59.4
60.5 6.64
3.8
Ours-Aug
53.8 - 3.2
2.9
71.2
5.0
7.92
67.0
19

---

Table 16: Out-of-domain accuracy on CAMELYON17 where nuclei are whitened out after being expanded with filter size 9, i.e., the whitened out part covers nuclei and some region around nuclei. The column name indicates the centre used train models. The best accuracy for each column is in bold face and the second best in italics.
Method
Centre-0
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
61.8 = 3.5
50.5 1.6
50.0 + 0.0
50.1 + 0.2
49.9 + 1.2
52.5 + 1.3
Macenko
50.5 0.9
50.0
50.0 0.1
50.1 0.3
50.0
50.1 = 0.3
RSC
59.6 + 5.0
1.5
50.0+0.0
50.3 0.3
50.1
52.0 +
L2D
63.5 4.9
5..7
5.2
48.0 6.0
56.8
Ours
55.9 = 6.9
67.3
6.84
58.1
0.7
58.7
ERM-Aug
51.0 1.6
5.0
10.5
50.2
4.2
5.14
Macenko-Aug
51.3
5.1
6.4
1.8
47.7
5.1
RSC-Aug
5.3
6.2
0.0
5..4
2.6
L2D-Aug
68.2 +: 4.0
49.7
5.1
5.70
81.
$5.5
Ours-Aug
50.3 0.3
76.9 + 4.3
67.6 9.7
53.1 #
5.1
61.3 5.3 5.3
Table 17: Out-of-domain accuracy on where nuclei are inpainted after being expanded with filter size 5. The column name indicates the centre used to train models. The best accuracy for each column is bold face and the second best in italics.
Method
Centre-0
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
54.7
62.4 3.7
50.5 + 1.9
49.9 = 0.2
48.6 7.1
53.2 3.2
Macenko
54.3 14.377 7.6
3.4
50.0 10.0:00 0.0
49.9 0.5
49.6
51.7
RSC
5.. 2.2
5..9 5.1 0.1
53.5
L2D
6.91
61.7 0.4
59.8 5.89
2.9
Ours
0.0
2.1
0.6
0.0
4.9
497
ERM-Aug
51.8 3.1
5..1
5.9
58.5
46.1
4.0
Macenko-Aug
55.5
5.8
61.9
5.9
8.5
5.6
RSC-Aug
3.5
6.6
6.2
2..2
43.2
5.7
L2D-Aug
69.9
1.00
5..1
52.5 2.2
6.0
Ours-Aug
50.0
1.6
1.43
50.0
49.
0.6
Table 18: In domain accuracy on CAMELYON17 where nuclei are inpainted after being expanded with filter size 5. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best in italics.
Method
Centre-0
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
58.1 + 4.0
63.6 + 8.4
47.9 + 0.3
48.8 + 0.2
45.2 5.8
52.7 3.7
Macenko
53.4 9.3
53.7 0.2
48.2
48.3 0.5
52.5 4.3
51.2 + 2.8
RSC
58.2 4.8
59.5
0.1
0.1
49.9 6.4
52.8 - 4.8
L2D
66.1 8.5
7..9
4.11
5.2
11.411.
58.0 5.5
Ours
49.6 166+0.3 0.2
5.75
4.4
4.0
1.0
48.9 - 0.5
ERM-Aug
56.3 + 6.8
5.0
61.6
5.4
3.3
60.6 6.0
Macenko-Aug
66.2
1.
64.4
59.4
50.0
6.61
RSC-Aug
65.4
58.3
88.2 7.67
3..
65.8 6.8
L2D-Aug
72.8
37.6+22
08.23.1
8..9+13
6..5
6,80
Ours-Aug
49.9 + 0.0
2.73
48.0
48.1 0.2
4.10+
48.6 + 0.8
20

---

Table 19: OUT-OF-DOMAIN recall on CAMELYON17 where nuclei are inpainted after being expanded with filter size 5. The column name indicates the centre used train models. The best accuracy for each column is in bold face and the second best in italics.
Method
Centre-O
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
91.4 + 11.5 60.0 + 8.9
1.7 + 4.6
0.1 + 0.1
21.9 + 20.3
35.0 + 9.1
Macenko
11.1 + 21.4
12.4 10.3
0.0 0.0
0.9 2.0
20.7 + 13.6
9.0 9.5
RSC
$9.9
67.3
0.0
0.3
21.8 + 17.2
35.9 + 6.0
L2D
+ 19.8
2.8
1.6
32.6 12.9
19.9 + 22.5
11.1
Ours
0.0
7.0
0.2
0.0
0.3 0.2
0.6
ERM-Aug
10.1
23.8
19.6 + 17.5
2.78
4.8 3.8
16.6 10.2
Macenko-Aus
46.6
21.4
13.4
31.4 + 19.9
8..
44.0 + 21.1
29.3 + 15.9
RSC-Aug
23.4 + 16.5
30.4 1
14.5
20.6 + 12.0
22.3
3.1
20.0 10.3
L2D-Aug
74.5
61.7
2.6
43.6
36.2
45.7 5.2
Ours-Aug
0.1
2.2
2.7
0.0
0.0
0.8
Table 20: IN DOMAIN recall on CAMELYON17 where nuclei are inpainted after being expanded with filter size 5. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best in italics.
Method
Centre-O
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
97.1 4.3
35.0 + 18.5
0.0 0.0
1.0
50.6 + 15.3
36.8 = 7.7 7.7
Macenko
21.7
0.7
0.0
0.2
17.6 11.6
6.8
RSC
99.4
47.9
0.0
0.4
50.8 + 17.3
397
L2D
88.5 17.8
7.3
0.0
11.3
36.4 13.0 38.4
Ours
0.1 0.1
0.2
0.0
0.0
0.1
0.1 0.3
ERM-Aug
29.7 26.6
9.5 7.3
28.8
84.3 14.3
13.4
3.8
31.6 14.8
Macenko-Aur Aug 71.9 + 19.3
2.6
37.5
25.7
55.1
38.5 18.4
RSC-Aug
68.9 + 31.9
17.8
86.7 51.2+:2044
3.5
45.6 + 16.1
L2D-Aug
99.0
4..
6.5
9.0
48.7
6..4
Ours-Aug
0.0
0.4
0.1
0.1
0.3
0.2
Table 21: OUT-OF-DOMAIN precision CAMELYON17 where nuclei impainted after being expanded with filter size 5. The column name indicates the centre used to train models. The best accuracy for each column in bold face and the second best in italics.
Method
Centre-0
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
52.7 + 2.2
64.4 6.5
39.1 + 22.0 53.4 + 20.5 39.2 + 21.3
49.8 + 14.5
Macenko
88.2 + 8.1
83.0 + 7.6
52.8 + 45.6 42.6 + 19.3 48.3 3 16.9
63.0 + 19.5
RSC
55.7 2.0
55.7 2.4
15.7 11.9 70.6 + 17.8
50.3
99.6
L2D
69.2 - 9.2
78.8
60.7 13.1
5 5.5
15.3
08.5
Ours
23.2 10.2
20.2
10.2
53.2
11.2
23.6 11.0
ERM-Aug
61.0 13.0
80.5 13.9
85.0
8.1 76.6
30.8
66.8 + 11.6
Macenko-Aug
57.4 11.6
77.5
87.8 - 9.6 96.142.
50.3
73.8
RSC-Aug
47.3 = 15.2
7..4
72.3 19.8
92.1
1..947.6
60.4 10.7
L2D-Aug
68.3 = 2.0
59.8
62.1 8.1 84
55.3
66.2
Ours-Aug
69.8 = 10.1
3.67
38.3 - 22.6
38.6 16.7
2.
36.8 11.3
21

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Table 22: IN DOMAIN precision CAMELYONI7 where nuclei are inpainted after being expanded with filter size 5. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best in italics.
Method
Centre-0
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
54.8 + 2.5
71.2 + 13.8
0.0 + 0.0
99.4 + 0.5 47.7 7.4
54.6 + 4.8
Macenko
64.2 25.8
29.0 17.0
0.0 0.0
62.6 21.6 75.0
18.7
46.2 + 16.6
RSC
54.7 + 2.9
56.9 + 18.6
1.2 4.0
98.4 1.0 51.3
52.5 7.3
L2D
64.0 10.9
47.0222
1.8
90.1
48.1 15.3
58.0 7.3
Ours
11.1 + 5.8
14.2
1.6 2.6
42.9 + 47.5
0.8 0.8
14.1 15.6
ERM-Aug
58.8
12.9
79.8
12.3
97.6 1.6
83.0
5.6
21.3
71.5 + 10.7
Macenko-Aug
64.7 + 2.8
61.6
14.2
4..7
92.5
60.3 + 10.5
74.8 + 7.0
RSC-Aug
61.9 + 7.6
94.7 20.5
90.3 3.8
27.6
+ 18.1
68.9 + 11.1
L2D-Aug
65.1 0.6
1.2
97.31.1
69.9 2.7
71.0 2.6
Ours-Aug
25.9 = 40.5
1
20.9
13.8
18.6
15.0
2.7
17.1 16.8
Table 23: Out-of-domain accuracy on CAMELYON17 with nuclei on white background. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best italics.
Method
Centre-O
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
73.1 7.9
66.4 + 12.7
50.0 0.0
46.3 + 2.9
49.5 + 4.0
57.1 1.11:555
Macenko
44.9 : 1.9
63.5 : 10.4
49.8
45.4 1 2.0
50.1
50.7 3.0
RSC
55.0 1.0 7.4
61.4 10.0
2.9 0.0
0.3
4.2
L2D
53.6 - 10.1
84.1
4.4
4.9
61.6 7.5
Ours
90.1 1.0
92.9
1.9
0.3
8..7
91.1
ERM-Aug
08.6
6.5
6.9
5..0
6.6
7.21
Macenko-Aug
5.10
8..7
11.
512245.7
7.3
6.5
RSC-Aug
14.5
7.
4.67
5.00
6.64
58.1
L2D-Aug
84.1
8.6
4.9
6.65
9.5
2.3
Ours-Aug
90.2
0.9
91.0
0.7
89.8
91.0 - 1.2
Table 24: Out-of-domain accuracy CAMELYON17 evaluated binary nuclei segmentation
masks. The column name indicates the centre used to train models. The best accuracy for each column is in bold face and the second best italics.
Method
Centre-0
C
Centre-2
Centre-3
Centre-4
Average
ERM
48.7 + 4.1
50.0 + 0.0
50.0 + 0.0
50.0 + 0.0
50.8
49.9 + 1.6
Macenko
50.0 + 0.0
49.0 3.1
49.6 1.1
49.4 + 1.4
50.0
49.6 1.1
RSC
50.0 0.0
5.0
4.3
50.0
5.00
4.3
L2D
59.4 10.5
6..
5.07
5.00
5.72
4.51
Ours
90.8 1.0
92.9 0.3
9.64
9..
90.8
91.6 = 0.8
ERM-Aug
49.3 + 1.9
0.3
5.0
4.80
4.67
49.6
Macenko-Aug
1.1
1.0
1.8
4.1
5..0
1.0
RSC-Aug
49.8
5.00
53.1
50.0
5.0
5.2
L2D-Aug
1.0
58.4
5..0
50.8224
52.4442
52.3
Ours-Aug
9.56
02.4
92.
92
9..4+0.4
92.1
22

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Table 25: Out-of -ft-a-- accuracy on CAMELYON17 evaluated on inverted nuclei segmentation masks. The column name indicates the centre used train models. The best accuracy for each column is in bold face and the second best in italics.
Method
Centre-0
Centre-1
Centre-2
Centre-3
Centre-4
Average
ERM
47.7 2.5
50.8 + 2.5
50.0 + 0.0
47.3 + 1.9
51.1 + 3.6
49.4 = 2.1
Macenko
45.3 1.6
54.6 + 8.6
49.9 + 0.4
42.7 + 8.0
50.0 + 0.0
48.5 : 3.7
RSC
48.7
50.0 0.0
50.0 0.0
48.7 + 1.9
50.0
49.5 0.8
L2D
43.5 13.5+4.. 4.8
4.7
57.1+990
39.6 2.7
48.6
52.3 5.3
Ours
89.0 1.0
92.3 + 0.4
2.0
88.2 + 2.6
86.5 1.8
89.3
1.6
ERM-Aug
2.0
3.7
5.9
44.9 + 1.0
49.4
49.6 3.3
Macenko-Aup
3.8
76.0 13.4
9.1
46.1 0.9
5.1
5.54
RSC-Aug
47.6
11.8110.6
44.9 13.1
4.4
52.3
L2D-Aug
5.0
1.2
58.7 3.4
64.9
63.7 = 5.9
Ours-Aug
0.5
91.7
91.7
92.7
86.6 2.3
90.9 + 1.1
Table 26: Accuracy when combining L2D or RSC with the proposed method on CAMELYON17. The best accuracy for each column in bold face and the second best in italics. "+Ours" results are an average of five models for each combination of medical centre and method instead of an average of ten models. All results are using photometric augmentations described in 4.3 even though "-Aug" is omitted from the name in the table.
Method
Centre-0
Centre-1
Centre-2
Centre-3
Centre-4
Average
L2D
94.3 + 0.1
87.6 + 0.6
87.7 + 1.4
83.4 2.6
92.3 + 0.9
89.1 + 1.1
Lu2
92.2 0.6
92.8 0.1
92.5 + 0.5
84.8 + 1.5
88.9 1.4
90.3 + 0.8
RSC
93.1 + 0.8
78.2 + 2.0
77.9 3.4
1..0
85.9 + 2.0
RuC+Ours
92.1 + 0.8
93.6 0.4
02.5
91.3 0.7
91.9 0.4
92.3 0.7
Ours
91.8 0.7
92.2 + 1.6
92.9
90.4 1.1
9.5
91.8 = 0.9
Table 27: Accuracy when combining L2D or RSC with the proposed method on BCSS. The best accuracy for each column is in bold face and the second best in italics. "+Ours" results are an average of five models for each combination of medical centre and method instead of an average of ten models. All results are using photometric augmentations described in 4.3 even though "-Aug" is omitted from the name in the table.
Method
Centre-O
Centre-1
Centre-2
Centre-3
Centre-4
Average
L2D
81.9 0.3
74.7 + 0.6
74.2 + 0.6
67.5 + 2.9 2.9
77.2 + 2.2
75.1 + 1.3
L2D+OOus
81.8 + 2.0
82.2 0.5
73.7 1.3
2.67
75.5 1.7
RSC
79.6 + 1.5
2.9
71.6 1.7
7..1 1.6
72.1 7.6 2.0
RSC+Ours
82.0 + 1.2
1.7
76.7
78.9 1.9
Ours
82.3 + 0.8
81.9
75.7
79.6
7..8
78.8 + 1.6
Table 28: Accuracy when combining L.2D or RSC with the proposed method on Ocelot. The best accuracy for each column is in bold face and the second best in italics. "+Ours" results are an average of five models for each combination of medical centre and method instead of an average of models. All results using photometric augmentations described in 4.3 even though "-Aug" is omitted from the name in the table.
Method
Centre-O
Centre-1
Centre-2
Centre-3
Centre-4
Average
L2D
74.7 + 0.6
68.3 0.5
65.6 + 0.7
62.5 + 2.7
74.4 + 1.2
69.1 + 1.1
L2D+OOur
72.2 + 1.6
70.8
0.3
69.4 + 0.6
59.8 + 1.6
69.9 + 2.3
68.4 + 1.3
RSC
71.9 2.2
61.7 + 2.4
09.2
58.4 + 1.4
70.1
+ 3.6
66.3 + 2.5
RSC+Ours
71.1 0.7
72.1
1.77
7.74
70.9
1.2
70.8 1.2
Ours
70.8 0.5
72.0 1.3
1.3
70.4 0.7
70.6 1.0
23

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Table 29; Accuracy when using a ViT-Tiny [60].
comparing the baseline against the proposed method on CAMELYON17. The best accuracy for each column is bold face and the second best in italics. All results are using photometric augmentations described in 4.3 even though "-Aug" is omitted from
the name in the table. 'AwoC4' is 'Average without Centre-4 4" RRSNA' refers RandStainNA (24..
Method Centre-O
Centre-1
Centre-2
Centre-3
Centre-4
Average
AwoC4
ERM
94.6 + 0.5 78.0 = 4.0 90.0 1.6
84.9 + 2.9
87.7
86.9 2.3
2.3
RSNA
94.6 0.4
86.8 = 2.0 88.5 2.8
84.9 2.3
90.6 2.9 2.11
88.7 + 1.9
DDCA
95.0 + 0.5
3.7
91.2
8..7
8.0
5 2.
L2D
95.1 0.0
8.10
+18
87.3
91.8
89.4
88.8 + 0.4
Ours
9.2 92.5
0.5
0.7
91.8
86.7
93.2 9.9 0.6
Table 30: Accuracy when using a ViT-Tiny
comparing the baseline against the proposed method on BCSS. The best accuracy for each column is in bold face and the second best in italics. All results are using photometric augmentations described in even though "-Aug" is omitted from the name in the table. 'AwoC4' is 'Average without Centre-4', 'RSNA' refers to RandStainNA (24).
60
Method Centre-O
Centre-1
Centre-2
Centre-3
Centre-4
Average
AwoC4
ERM
79.2 3.3 70.7 + 4.6 68.6 + 2.4
66.9 + 2.2
69.5 + 5.1 3.5 71.4 + 3.1
RSNA
79.9 + 1.9 77.9
2.4
74.4 4.3
72.1 2.4
75.2 + 5.4
76.1 +
2.8
DDCA
332
69.9
06.34.
6.68
7.9
3.7
L2D
80.8
0.1
7.16
7.3
79.1
76.5
75.9 1.2
Ours
7..6
7
1.2
74.3
224
75.4 + 1.3
61.9
7..0
77.0 1.5
Table 31: Accuracy when using ViT-Tiny 160),
comparing the baseline against the proposed method on Ocelot. The best accuracy for each column is in bold face and the second best in italics. All results using photometric augmentations described in 4.3 even though "-Aug" is omitted from the
name the table. 'AwoC4' is 'Average without Centre-4 RSNA' refers to RandStainNA (4).
Method Centre-0
Centre-1
Centre-2
3
Centre-4
Average
AwoC4
ERM 71.7 + 1.9 61.0 3.5 67.1 3.1
62.6 + 3.5
67.3 = 4.3 65.9
65.6 + 3.0
RSNA 71.2 1.8
68.6
2.8 67.2
68.3 1.7
69.2 3.6
08.9
68.8 + 2.2
DDCA
73.0
6.0
67.2
62.6 6.2
65.1
65.7
L2D
7.1
6.1
1.0
71.8
0.3
69.8
69.3 + 0.5
Ours
0.8
68.8 + 0.8
70.0
0.6
70.4 + 1.0 64.5
2.3
6.16
69.6 + 0.8
24

---

JPEG Compression
Defocus Blur
Motion Blur
Gaussian Noise
Elastic Transform
Snow
Shot Noise
Impulse Noise
Figure Exemplary image corruptions from [1 applied to an input image used this study.
25

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Figure 6: Saliency maps for four randomly selected tiles using Integrated Gradients 664 for a model trained via ERM-Aug. The green-coloured map indicates the contribution towards the positive class (tumour), and the red one towards the negative class (non- mton-tumour).
26

---

Figure 7: Saliency maps for four randomly selected tiles using Integrated Gradients 64 for a model trained via Ours-Aug. The green-coloured map indicates the contribution towards the positive class (tumour), and the red one towards the negative class (non-ture mon-tuumour).
27

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Reviews:
[{"review_id": "d8wgyw1YQI", "reviewer": "Reviewer_UomK", "summary": "The paper \"Are nuclear masks all you need for improved out-of-domain generalization? A closer look at cancer classification in histopathology.\" approaches the task of center classification for out-of-domain histopathology imagery. Motivated by classical shape-based segmentation, the authors suggest utilizing segmentation maps as an input to force the classification network to focus more on the morphology of cells. This enables the classification network to achieve a more robust classification accuracy on out-of-domain histopathology images.", "questions": "Does the slight increase in classification performance justify the use of significantly more extensive supervision?\nHow does the attribution change when utilizing the presented approach?\nHow complimentary is the presented approach to other existing approaches? Can I combine L2D and your approach?", "limitations": "The authors have not adequately addressed the limitations of their approach. As mentioned in my main concern, the presented method requires significantly more supervision than other approaches. This is not discussed in the limitations sections.", "rating": 5, "confidence": 3, "soundness": 1, "presentation": 2, "contribution": 2, "strengths": "The presented approach is certainly interesting, using segmentation maps to shift the attribution/focus of the classification network to cells. While I'm not too familiar with the recent SOTA approach in this domain, this approach seems to be novel. Using the segmentation mask leads to an increased classification accuracy on OOD data.", "weaknesses": "The paper lacks clarity; I often find myself rereading sentences and paragraphs to follow the ideas of the authors. For instance, the abstract does not clearly state which specific tasks the authors are approaching. \n\nMany statements in the introduction motivating the presented approach are not supported by citations. An example include the last sentence of the second paragraph.\n\nAdditionally, the related work section mainly provides an enumeration of existing approaches and does not clearly explain how the presented approach fits/extends related work.\n\nMy main concern is the use of more extensive annotations, namely segmentation maps. The presented approach uses the predictions of HoVer-Net that rely on dense segmentation labels. Subsequentially, the authors require substantially more supervision than the approaches they compare against (e.g., L2D). Providing a discussion on this is required. Additionally, a more competitive baseline also using mask supervision would be required. For instance, training a network on segmentation and then fine-tuning it on the downstream task.\n\nFurther, the presented method does not consistently outperform L2D when data augmentation is utilized while using extensively more supervision.\n\nAs the main aim of using segmentation maps is to shift the attribution of the classification network, the authors should also discuss the connection to attribution methods or consider analyzing the attribution of their approach and other approaches. \n\nFinally, many tables presented both in the supplement and the main paper do not adhere to the style guidelines and use too much horizontal space.\n\nMinor comments:\n- Line 17: Labeling noise is not really changing the data distribution.\n- Line 29: What does general vision refer to? I think the domain of natural images is way more concise here.\n- Line 51: “directing the attention of CNNs” sounds somewhat misleading. I guess the attribution is relevant in this case.\n- Line 130: PyTorch does not provide a ResNet-50 implementation. The authors probably refer to TorchVision.\n- Line: 31 is -> are.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your comments and for recognizing our approach as interesting and novel.\n\nWe are committed to revise the paper to use more accessible language and make it easier to understand our ideas and statements. This will include specifying clearly in the abstract that we analyze cancer classification across different centers, whole-slide scanners, and cancer types. We will also make it clearer in the related work section that our approach is different from other S-DG methods in that it attempts to bias the model to focus on nuclei specifically, or more generally towards some domain-general features captured by an image map, without the need to have the segmentation or image map during inference. We will also mention the vague resemblance to contrastive learning, although our approach is with supervision and jointly uses a classification loss, which also implies that we do not need nor use negative examples as in contrastive learning. Additionally, we will include citations like (1) and (2) that survey domain generalization approaches for histopathology. Both surveys indicate that S-DG methods developed for natural images have not been widely adopted for histopathology. Histopathology has instead seen its own S-DG methods developed over time, most of which are about augmentation or normalization. We will amend the introduction accordingly and also address the minor concerns you raised, including adjusting the style of the tables to adhere to the guidelines.\n\n(1) Jahanifar, Mostafa, et al. \"Domain generalization in computational pathology: survey and guidelines.\" arXiv preprint arXiv:2310.19656 (2023).\n\n(2) Yoon, Jee Seok, et al. \"Domain generalization for medical image analysis: A survey.\" arXiv preprint arXiv:2310.08598 (2023).\n\n> \"My main concern is the use of more extensive annotations, namely segmentation maps. (...) Providing a discussion on this is required. Additionally, a more competitive baseline also using mask supervision would be required. For instance, training a network on segmentation and then fine-tuning it on the downstream task.\"\n\nWe agree that our approach requires more information during training because it utilizes segmentation masks, although these are not needed at inference time. To investigate whether it is this additional information during training that improves performance, we have, as you suggested, performed additional experiments with a baseline that also builds on the segmentations. Specifically, we fine-tuned the ResNet-50 model in HoVer-Net (the segmentation model used to create the segmentation masks) on our downstream task using the same setup as our approach. As seen in Tables 1 to 3 in the PDF attached to the \"global\" response, our approach compares favourably also in this case, demonstrating that our approach utilizes the segmentations and the original images in a more efficient manner in order to attain high out-of-domain accuracy. Please also note that the experiments commented on lines 234 through 243, with associated results in the two bottom rows of Table 1 in the paper, all use the segmentation masks as in our approach but without the $l_2$-regularization. These results also suggest that simply including the segmentations in training is not important to the performance; it needs to be coupled with the $l_2$-regularization that pulls the feature representation of original images towards the feature representation of the segmentation mask. However, we will expand the limitation paragraph to mention that if our approach is to be applied to other fields than histopathology, it might be more difficult to generate image maps that capture domain-invariant features, in which case our approach might not be applicable due to the requirement of such segmentation maps.\n\n> \"Further, the presented method does not consistently outperform L2D when data augmentation is utilized while using extensively more supervision.\"\n\nAlthough L2D occasionally performs slightly better than our approach when training on specific centres, our approach is clearly more accurate on average. The models trained using our approach are also more robust than models trained using L2D; please see additional results supporting this finding in the \"global\" response (in particular, Figure 1 in the PDF attached to that response).\n\n> \"As the main aim of using segmentation maps is to shift the attribution of the classification network, the authors should also discuss the connection to attribution methods or consider analyzing the attribution of their approach and other approaches.\"\n\nThank you for the suggestion. We have now generated some saliency maps using the Integrated gradients method, and they clearly highlight nuclei for our method while not for others. The generated attribution maps highlight the outline of some nuclei in the images for our method, while the attribution is dispersed in a region for ERM. We did not include examples in the rebuttal PDF because of insufficient space, but will include the images in our paper. Please also note that multiple of the ablation studies reported in our paper indicate that models trained using our method focus more on nuclei, in particular, those commented in lines 244 through 283 with results in Tables 13 to 26, but also the results in Tables 27 and 28 support this.\n\n> \"How complimentary is the presented approach to other existing approaches? Can I combine L2D and your approach?\"\n\nPlease see lines 284 through 289 and Tables 29 to 31. Combining with L2D does not yield better performance than our approach alone, likely due to the opposing effects of these two interventions. Combining with RSC yielded a small gain over our approach alone. Overall, this demonstrates the effectiveness of the method proposed in this paper. It might also suggest that even better performance could be obtained by combining our method with a method that improves out-of-domain generalization more substantially than RSC while not having opposing effects as our method.", "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": "", "extra_scores": {}}, {"review_id": "IfJ00F02pm", "reviewer": "Reviewer_fXPp", "summary": "The paper proposes a method focusing on nuclei masks, along with an augmentation method, to train a feature extractor to increase\nthe out-of-domain generalization by directing the model toward learning nuclear features.\nThe method has been evaluated against 4 different approaches on three different cancer datasets comprised of different centers. It has been empirically shown that the nuclei-based approach is superior to other methods, and also the augmentation method can improve the performance of different approaches when used as a plugin.", "questions": "The work has been built upon the idea that the network should focus on nuclei to generalize better. Yet, I would suggest the authors add a qualitative experiment visualizing the saliency map of resnt50 (or attention map of vit) to support indeed that the focus on nuclei is the key to the improvement. This can be compared with some sort of human annotation and also against other methods.", "limitations": "N/A", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "The methodology proposed is simple and straightforward and appears to be superior to other methods using a resnet50 backbone. There are different ablation studies supporting the authors' claims for the components of their design. Also, generally, the idea is biologically interesting to lead the model to focus on nuclei.", "weaknesses": "major:\nThe methods used to benchmark against are not the recent methods (with the newest method (L2D) being from 2021). This needs to be addressed by comparing it against some new pathology-specific methods like (1), (2), or (3).\n\nThe authors only used resnet50 for most of their experiments and used vit_tiny for limited experiments against ERM. In order to show the generality of the method, vit_tiny needs to be added to the full set of experiments, along with other methods for benchmarking.\n\n(1) Marini, Niccolò, et al. \"Data-driven color augmentation for H&E stained images in computational pathology.\" Journal of Pathology Informatics 14 (2023): 100183.\n\n(2) Shen, Y., Luo, Y., Shen, D., Ke, J. (2022). RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_21\n\n(3) Bouteldja, Nassim, et al. \"Stain-independent deep learning–based analysis of digital kidney histopathology.\" The American Journal of Pathology 193.1 (2023): 73-83.\n\nminor:\nthe contributions of the work in the introduction can be written more clearly: it is a bit vague to understand the key points through the first read :)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for recognizing the simplicity yet superiority of our approach as well as its biological appeal. We also appreciate your suggestions for additional analyses, which add to our previous experiments to show the strengths of our proposed approach.\n\n> The methods used to benchmark against are not the recent methods (with the newest method (L2D) being from 2021). This needs to be addressed by comparing it against some new pathology-specific methods like (1), (2), or (3).\n\nWe were able to complete analyses for the approaches described in your reference (1) and (2). Please find the results in Tables 1, 2, and 3 in the PDF attached to the \"global\" response. These results demonstrate that our approach compares favorably also to these recent pathology-specific methods, thus adding support for the superiority of our approach.\n\n> The authors only used resnet50 for most of their experiments and used vit_tiny for limited experiments against ERM. In order to show the generality of the method, vit_tiny needs to be added to the full set of experiments, along with other methods for benchmarking.\n\nWe have substantially expanded on these experiments. Although results are not yet generated for the baseline methods that performed poorly using ResNet-50 (specifically no augmentation, Macenko normalization, and RSC), we managed to expand the experiments to include the other methods and even include the new pathology-specific methods you suggested in reference (1) and (2) (although for DDCA, the method in your reference (1), the number of models trained per center was between 5 and 10 instead of always 10 because of time constraints; we will amend this during the discussion phase but are very confident that this will not change the interpretation of the results). Please find the results in Tables 4 to 6 in the PDF attached to the \"global\" response. These results show that our approach obtains superior out-of-domain performance when classifying the same cancer type in different centers and scanners for the CAMELYON17 dataset. When classifying tumors in other cancer types, the results are more mixed. In particular, it seems that models trained on one of the five centers (Center-4) in CAMELYON17 do not generalize well to other cancer types and are actually also performing sub-optimally in the four other centers in CAMELYON17. For models trained on each of the four other centers in CAMELYON17, the performance is, on average, better than other approaches also in new cancer types, but the performance increase is lower than for ResNet-50. However, in the same cancer type (CAMELYON17 data), the performance gain is similar for both ViT tiny and ResNet-50. Our interpretation of all these results is that our approach can improve out-of-domain performance also for ViT tiny, in particular across centers and scanners for the same cancer type, but that it might also fail for a minority of the training datasets. We have not had time to investigate the reason for this occasional failure further.\n\n> minor: the contributions of the work in the introduction can be written more clearly: it is a bit vague to understand the key points through the first read :)\n\nThank you for the feedback. We are committed to revise the introduction and use a more accessible language that will make it easier to understand the key points and contributions of our work.\n\n> The work has been built upon the idea that the network should focus on nuclei to generalize better. Yet, I would suggest the authors add a qualitative experiment visualizing the saliency map of resnt50 (or attention map of vit) to support indeed that the focus on nuclei is the key to the improvement. This can be compared with some sort of human annotation and also against other methods.\n\nThank you for the suggestion. We have now generated some saliency maps of ResNet-50 models using the Integrated gradients method, and they clearly highlight nuclei for our method while not for others. The generated attribution maps highlight the outline of some nuclei in the images for our method, while the attribution is dispersed in a region for ERM. We did not include examples in the rebuttal PDF because of insufficient space but will include the images in our paper. We will also compare these images to segmentations of nuclei in order to confirm qualitatively that the models trained using our approach focus more on nuclei. Please also note that multiple of the ablation studies reported in our paper indicate that models trained using our method focus more on nuclei, in particular those commented in line 244 through 283 with results in Tables 13 to 26, but also the results in Tables 27 and 28 support this.", "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": "", "extra_scores": {}}, {"review_id": "IU2NY9kdgA", "reviewer": "Reviewer_sg4y", "summary": "This paper tackles the issue of out-of-domain (OOD) generalization in histopathology, which is complicated by domain shifts due to different scanners, staining procedures, and inter-patient variability. With a focus on single-domain generalization, this paper proposes to prioritize shape features over texture, as shape remains consistent across domains. This is achieved by using images and segmentation masks with data augmentation and regularization, emphasizing nuclear morphology critical for cancer detection. Experiments confirm this approach improves OOD generalization, validating an old hypothesis with deep learning and demonstrating the method's flexibility as an enhancement to other techniques.", "questions": "- Could you carefully revise the manuscript to make its content self-organized and easy to follow?\n\n- Compared to the suggested alternative solutions, what is the advantage of adopting the proposed approach? In-depth investigation and analysis are required in this respect.", "limitations": "The authors have clearly identified the limitations of their work and suggested sound solutions. No potential negative societal impact.", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Vulnerability to cross-domain variances and shifts is essentially a critical challenge in the context of AI-driven digital pathology. Diving deeper into this topic could greatly enhance the reliability and usability of current learning-based approaches.\n\n- The experimental evaluations and ablation studies are quite comprehensive.", "weaknesses": "- The paper is overall poorly structured with convoluted paragraph organizations and intricate sentences. It is hard for readers to get the core idea of this paper.\n\n- The description of proposed methodology is quite informal, with ambiguous elucidation and unrigorous mathematical formulations. For example, what do you mean by stating “run a forward pass”?\n\n- The idea to leverage the shape of nuclei as domain-invariant markers is not new [Ref-A]. The morphology and spatial distribution of nucleus clusters are also common biomarkers for cancer diagnosis and grading [Ref-B]. It is unclear what the contribution of this work is. \n\n- Why not directly employing a segmentation to extract the morphometrics for all nuclei and then input the secured measurements to a simple classifier (e.g., SVM) to get the diagnosis prediction? If the color and texture are argued to be not robust against domain shifts, why not simply discard the original pathology slides?\n\n[Ref-A]:\nSharma, Yash, Sana Syed, and Donald E. Brown. \"Mani: Maximizing mutual information for nuclei cross-domain unsupervised segmentation.\" in MICCAI, 2022.\n\n[Ref-B]:\nBansal, Cherry, et al. \"Grading systems in the cytological diagnosis of breast cancer: a review.\" Journal of cancer research and therapeutics 10.4 (2014): 839-845.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for assessing our paper and recognizing the importance of domain shifts in AI-driven digital pathology and the comprehensiveness of our experimental evaluations and ablation studies.\n\nWe are committed to revise the paper so that it is better organized and easier to follow with a more accessible language, clarification or omission of elucidations that might be ambiguous, and the use of more rigorous mathematical formulations. Regarding the latter, we consider it beneficial to define more formally which embeddings $z$ and $z'$ are (it is the embedding just before the Global Average Pooling in ResNet-50 of the input image $x$ and its corresponding segmentation mask $x'$, respectively) and what predictions $\\hat{y}$ and $\\hat{y}'$ are (it is the final output of the model for $x$ and $x'$, respectively). By stating \"run a forward pass\", we meant to execute a forward propagation through the neural network model, referencing the forward method used in standard code and textbooks (e.g., http://d2l.ai/chapter_linear-regression/oo-design.html#models). We will revise the formulations in the paper accordingly. If there are any other mathematical formulations that might be perceived as unrigorous, we kindly ask the reviewer to specify them concretely via an official comment.\n\nAs for the more technical comments, we acknowledge that utilizing the shape and organization of nuclei is not new in machine learning in general and deep learning in particular. However, we did not make such a claim in the paper but rather referenced multiple earlier contributions, please see line 57 through 63 in the paper. As noted there, an essential difference between the approach we propose and previous approaches is that our approach enables the training of neural network models that robustly analyze the original images directly, while many other approaches uses the segmentation masks also at inference.\n\nAlso the paper mentioned in [Ref-A] does not propose an approach similar to ours. It rather deals with segmentation of nuclei and propose an architecture including a backbone encoder-decoder network, a segmentation head, a projection head, and a network combining the outputs from the heads and estimating the mutual information. Our paper deals with tumor classification and simply adds a loss and augments the input during training. As such, these approaches are not comparable in terms of task and complexity. It is interesting to note that [Ref-A] uses HoVer-Net as the backbone encoder-decoder network. Based also on the comments from reviewer UomK, we have now performed experiments with fine-tuning HoVer-Net for tumor classification. As seen in the PDF attached to the \"global\" response (see Tables 1 to 3 in that PDF), our approach compares favorably also in this case.\n\nRegarding [Ref-B], we fully agree that the importance of nuclear morphology is well recognized in the medical literature. Indeed, the knowledge about this and also the importance of organization of nuclei in the tissue is part of the motivation for using nuclear segmentation masks in our proposed approach. Line 45 through 51 in our paper addresses this with multiple references, including reference 9 that defines perhaps the most famous grading system in breast cancer histopathology (which is also cited within [Ref-B]). If you recommend to cite also [Ref-B] to cover insights from breast cancer cytology in addition, we are happy to do so (although please note that our paper is about histopathology).\n\nWe hope that these explanations have made the contribution of our work clear and will in the revision of the paper make sure that this is expressed clearly. If you think otherwise or have additional concerns, we kindly ask you to specify them concretely.\n\nBecause our study aims to improve an aspect of neural networks, we do not consider it to be relevant to compare the performance of our approach with that of classical feature extraction followed by e.g. SVM. We consider it to be shown that neural networks can perform better than classical approaches for the task studied in our paper (tumor classification) and many other tasks in histopathology and beyond. As a concrete example for tumor classification, the results of the CAMELYON16 challenge show that the submitted algorithm using several nucleus-based features and 6 other submitted algorithms extracting features and applying a supervised classification method perform substantially worse than the 25 submitted algorithms using deep learning (see Table 2 in https://jamanetwork.com/journals/jama/fullarticle/2665774). We have therefore chosen to not evaluate the performance of using morphometrics for all nuclei and a simple classifier (e.g., SVM), as this is neither expected to give as good results nor would it provide neural network models that robustly analyze the original images directly.", "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": "", "extra_scores": {}}]

Author rebuttal:
We would like to thank the reviewers for recognizing the approach we propose as interesting, important, and novel. We also appreciate their input on the language, organization, and clarity of statements in our paper, which we will use to revise the paper thoroughly to make it easier to follow. The reviewers also provided useful comments that guided us to perform additional ablation studies that also demonstrate the superiority of our approach, for which we are most grateful. The most important results from the additional analyses are provided in the PDF attached to this comment. Please find our answers to each reviewer below with references to this PDF. After revising the paper to include these additional analyses and to improve the readability, we hope the paper is considered suitable for NeurIPS.

Tables 1 to 6 in the attached PDF present the results of the new experiments requested by the reviewers. Additionally, Figure 1 in the attached PDF provides further support for the increased robustness of models trained using our approach, thus complementing the results in Tables 4 to 6 in our paper. This figure shows the results of testing the robustness of ERM, L2D, and our method against adversarial attacks using the PGD attack. More specifically, Figure 1(a) shows the robustness of the different methods against the attack in the usual way, while Figure 1(b) shows the results of cross-model-attacks. For cross-model-attacks, we generate adversarial images using models from method A and then test the accuracy of models from methods B and C on those images, i.e., a method generates adversarial images for other methods. These results clearly show that adversarial images generated via models trained using our method are able to successfully attack the other models, and that adversarial images generated via models trained using the other methods have only a very small impact on the accuracy of models trained using our method.

Meta-review: The paper addresses the challenge of out-of-domain generalization in computational histopathology by focusing on nuclei in images, proposing a novel training method that mixes original images with segmentation masks. The approach enhances the model's robustness to variations in staining procedures and imaging equipment, common across different hospitals. Reviewers appreciated the method's simplicity and broad applicability. However, there were concerns regarding the paper's overall presentation and clarity, particularly about the detailed explanation of results and the rationale behind certain methodological choices. The authors' rebuttal, which included additional comparisons and clarifications, addressed many of these concerns, leading reviewers to raise their scores marginally. While the paper still has some issues related to writing and clarity, the consensus leans toward acceptance.
Decision: Accept (poster)