Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology | Paper Citation Record

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This paper cites A method for normalizing histology slides for quantitative analysis.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology A method for normalizing histology slides for quantitative analysis

Reference 1

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This paper cites Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

Reference 2

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This paper cites Domain Generalization in Computational Pathology: Survey and Guidelines.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Domain Generalization in Computational Pathology: Survey and Guidelines

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This paper cites Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness

Reference 4

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This paper cites Deep convolutional networks do not classify based on global object shape.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Deep convolutional networks do not classify based on global object shape

Reference 5

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This paper cites Pathological prognostic factors in breast cancer.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Pathological prognostic factors in breast cancer

Reference 6

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This paper cites Nuclear structure in cancer cells.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Nuclear structure in cancer cells

Reference 7

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This paper cites Nuclear morphology and the biology of cancer cells.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Nuclear morphology and the biology of cancer cells

Reference 8

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This paper cites Importance of nuclear morphology in breast cancer prognosis.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Importance of nuclear morphology in breast cancer prognosis

Reference 9

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This paper cites Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology

Reference 10

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This paper cites Automatic breast cancer grading of histopathological images.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Automatic breast cancer grading of histopathological images

Reference 11

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This paper cites Boucheron, B.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Boucheron, B

Reference 12

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This paper cites Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer

Reference 13

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This paper cites Automatic breast cancer diagnosis based on k-means clustering and adaptive thresholding hybrid segmentation.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Automatic breast cancer diagnosis based on k-means clustering and adaptive thresholding hybrid segmentation

Reference 14

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This paper cites Hewitt, Nicholas Petrick, Kyle J.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Hewitt, Nicholas Petrick, Kyle J

Reference 15

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This paper cites Automated segmentation and measurement for cancer classification of her2/neu status in breast carcinomas.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Automated segmentation and measurement for cancer classification of her2/neu status in breast carcinomas

Reference 16

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This paper cites Use of watersheds in contour detection.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Use of watersheds in contour detection

Reference 17

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This paper cites Osher and James A.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Osher and James A

Reference 18

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This paper cites Snakes: Active contour models.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Snakes: Active contour models

Reference 19

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This paper cites Cullen M.D., and Seong Ki Mun.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Cullen M.D., and Seong Ki Mun

Reference 20

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Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Unresolved cited work

Reference 21

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This paper cites Structure- preserving color normalization and sparse stain separation for histological images.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Structure- preserving color normalization and sparse stain separation for histological images

Reference 22

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This paper cites Color transfer between images.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Color transfer between images

Reference 23

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Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Randstainna: Learning stain-agnostic features from histology slides by bridging stain augmentation and normalization

Reference 24

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This paper cites Stain normaliza- tion methods for histopathology image analysis: A comprehensive review and experimental comparison.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Stain normaliza- tion methods for histopathology image analysis: A comprehensive review and experimental comparison

Reference 25

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This paper cites Tailoring automated data augmenta- tion to h&e-stained histopathology.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Tailoring automated data augmenta- tion to h&e-stained histopathology

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This paper cites Randaugment: Practical automated data augmentation with a reduced search space.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Randaugment: Practical automated data augmentation with a reduced search space

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Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology H and e stain augmentation improves generalization of convolutional networks for histopathological mitosis detection

Reference 28

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Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Augment like there's no tomorrow: Consistently performing neural networks for medical imaging

Reference 29

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Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Data-driven color augmentation for h&e stained images in computational pathology

Reference 30

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This paper cites Automatic data augmentation to improve generalization of deep learning in h&e stained histopathology.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Automatic data augmentation to improve generalization of deep learning in h&e stained histopathology

Reference 31

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Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Self-challenging improves cross- domain generalization

Reference 32

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Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Generalizing to unseen domains via adversarial data augmentation

Reference 33

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This paper cites Learning to learn single domain generalization.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Learning to learn single domain generalization

Reference 34

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Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Wasserstein auto- encoders

Reference 35

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Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Progressive domain expansion network for single domain generalization

Reference 36

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This paper cites Learning to diversify for single domain generalization.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Learning to diversify for single domain generalization

Reference 37

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Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Generative adversarial nets

Reference 38

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Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Unpaired image-to-image translation using cycle-consistent adversarial networks

Reference 39

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This paper cites Staingan: Stain style transfer for digital histological images.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Staingan: Stain style transfer for digital histological images

Reference 40

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This paper cites Residual cyclegan for robust domain transformation of histopathological tissue slides.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Residual cyclegan for robust domain transformation of histopathological tissue slides

Reference 41

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Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Enhanced cycle-consistent generative adversarial network for color normalization of h&e stained images

Reference 42

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Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Histopathological stain transfer using style transfer network with adversarial loss

Reference 43

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Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Image style transfer using convolutional neural networks

Reference 44

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Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Perceptual losses for real-time style transfer and super-resolution

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This paper cites Neural Stain Normalization and Unsupervised Classification of Cell Nuclei in Histopathological Breast Cancer Images.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Neural Stain Normalization and Unsupervised Classification of Cell Nuclei in Histopathological Breast Cancer Images

Reference 46

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This paper cites Neural Stain-Style Transfer Learning using GAN for Histopathological Images.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Neural Stain-Style Transfer Learning using GAN for Histopathological Images

Reference 47

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This paper cites Measuring domain shift for deep learning in histopathology.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Measuring domain shift for deep learning in histopathology

Reference 48

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This paper cites Deep residual learning for image recognition.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Deep residual learning for image recognition

Reference 49

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This paper cites Torchvision: Pytorch’s computer vision library.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Torchvision: Pytorch’s computer vision library

Reference 50

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This paper cites 1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology 1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset

Reference 51

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This paper cites Structured crowdsourcing enables convolutional segmentation of histology images.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Structured crowdsourcing enables convolutional segmentation of histology images

Reference 52

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This paper cites Ocelot: Overlapped cell on tissue dataset for histopathology.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Ocelot: Overlapped cell on tissue dataset for histopathology

Reference 53

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This paper cites Earnshaw, Imran S.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Earnshaw, Imran S

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:47:20.329979Z

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This paper cites Extending the wilds benchmark for unsupervised adaptation.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Extending the wilds benchmark for unsupervised adaptation

Reference 55

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This paper cites Replication Data for: Are nuclear masks all you need for improved out-of- domain generalization? A closer look at cancer classification in histopathology, 2024.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Replication Data for: Are nuclear masks all you need for improved out-of- domain generalization? A closer look at cancer classification in histopathology, 2024

Reference 56

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This paper cites Designing deep learning studies in cancer diagnostics.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Designing deep learning studies in cancer diagnostics

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:47:20.286598Z

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This paper cites Imagenet: A large- scale hierarchical image database.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Imagenet: A large- scale hierarchical image database

Reference 58

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This paper cites HoVer-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology HoVer-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:47:20.243067Z

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology An image is worth 16x16 words: Transformers for image recognition at scale

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T20:47:19.328536Z

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Observation 8b2503cf-9ee8-423d-b096-2b4015b9fa13 · outbound

This paper cites Benchmarking neural network robustness to common corruptions and perturbations.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Benchmarking neural network robustness to common corruptions and perturbations

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:47:20.203867Z

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Observation c92b6d56-abad-4d9b-a980-2f8bb2890c2f · outbound

This paper cites Kunz, Matthew C.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Kunz, Matthew C

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:47:20.184577Z

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This paper cites An image inpainting technique based on the fast marching method.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology An image inpainting technique based on the fast marching method

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:47:20.158995Z

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Observation afc28569-bea7-46a7-bf1f-ae1de588fe42 · outbound

This paper cites The many shapley values for model explanation.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology The many shapley values for model explanation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:47:20.130040Z

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Observation 921f0614-7757-4031-894b-ddc743d8edb5 · outbound

This paper cites Intriguing properties of neural networks.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Intriguing properties of neural networks

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T20:47:19.363406Z

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Observation d37946ba-3edc-4dd6-b490-d4bb928d14c4 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Towards deep learning models resistant to adversarial attacks

Reference 66

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This paper cites Scaling vision transform- ers.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology Scaling vision transform- ers

Reference 67

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Observation 03969d14-bd6e-4414-8fa7-fc7eb2cbbcf3 · outbound

This paper cites The best accuracy for each column is in bold face and the second best in italics.

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology The best accuracy for each column is in bold face and the second best in italics

Reference 68

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T20:47:20.088786Z

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