Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis | Paper Citation Record

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This paper cites Pan-cancer analysis of the extent and con- sequences of intratumor heterogeneity.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Pan-cancer analysis of the extent and con- sequences of intratumor heterogeneity

Reference 1

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This paper cites Integra- tive spatial analysis of cell morphologies and transcriptional states with muse.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Integra- tive spatial analysis of cell morphologies and transcriptional states with muse

Reference 2

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This paper cites A graph-based multi- scale approach with knowledge distillation for wsi classifica- tion.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis A graph-based multi- scale approach with knowledge distillation for wsi classifica- tion

Reference 3

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This paper cites The causes and consequences of genetic heterogeneity in cancer evolution.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis The causes and consequences of genetic heterogeneity in cancer evolution

Reference 4

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This paper cites Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Reference 5

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This paper cites Histopathology whole slide image anal- ysis with heterogeneous graph representation learning.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Histopathology whole slide image anal- ysis with heterogeneous graph representation learning

Reference 6

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This paper cites Grad-cam++: General- ized gradient-based visual explanations for deep convolu- tional networks.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Grad-cam++: General- ized gradient-based visual explanations for deep convolu- tional networks

Reference 7

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This paper cites Whole slide images are 2d point clouds: Context-aware survival prediction using patch-based graph convolutional networks.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Whole slide images are 2d point clouds: Context-aware survival prediction using patch-based graph convolutional networks

Reference 8

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This paper cites Towards a general-purpose foundation model for com- putational pathology.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Towards a general-purpose foundation model for com- putational pathology

Reference 9

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This paper cites TimeMIL: Advancing multivariate time series classification via a time-aware multiple instance learning.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis TimeMIL: Advancing multivariate time series classification via a time-aware multiple instance learning

Reference 10

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This paper cites Rankmix: Data augmentation for weakly supervised learning of classifying whole slide images with diverse sizes and imbalanced cat- egories.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Rankmix: Data augmentation for weakly supervised learning of classifying whole slide images with diverse sizes and imbalanced cat- egories

Reference 11

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This paper cites Multiple instance learn- ing with center embeddings for histopathology classifica- tion.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Multiple instance learn- ing with center embeddings for histopathology classifica- tion

Reference 12

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This paper cites Conditional Positional Encodings for Vision Transformers.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Conditional Positional Encodings for Vision Transformers

Reference 13

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This paper cites Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Reference 14

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This paper cites Elements of information theory.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Elements of information theory

Reference 15

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This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Sinkhorn distances: Lightspeed computation of optimal transport

Reference 16

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Observation f6ba3f26-70a8-4a76-b77b-5819ec3c553f · outbound

This paper cites Statistical comparisons of classifiers over multiple data sets.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Statistical comparisons of classifiers over multiple data sets

Reference 17

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This paper cites Inherently interpretable time series classification via multiple instance learning.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Inherently interpretable time series classification via multiple instance learning

Reference 18

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This paper cites Deep residual learning for image recognition.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Deep residual learning for image recognition

Reference 19

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This paper cites Deep residual learning for image recognition.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Deep residual learning for image recognition

Reference 20

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This paper cites Probability of error, equiv- ocation, and the chernoff bound.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Probability of error, equiv- ocation, and the chernoff bound

Reference 21

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Observation 0f40d97b-a613-4c6e-ae17-442f8cc57021 · outbound

This paper cites Patch-based convolutional neural network for whole slide tissue image classification.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Patch-based convolutional neural network for whole slide tissue image classification

Reference 22

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This paper cites Hˆ 2-mil: ex- ploring hierarchical representation with heterogeneous mul- tiple instance learning for whole slide image analysis.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Hˆ 2-mil: ex- ploring hierarchical representation with heterogeneous mul- tiple instance learning for whole slide image analysis

Reference 23

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This paper cites Attention-based deep multiple instance learning.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Attention-based deep multiple instance learning

Reference 24

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This paper cites Model- ing dense multimodal interactions between biological path- ways and histology for survival prediction.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Model- ing dense multimodal interactions between biological path- ways and histology for survival prediction

Reference 25

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This paper cites Dual-stream multiple instance learning network for whole slide image classifica- tion with self-supervised contrastive learning.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Dual-stream multiple instance learning network for whole slide image classifica- tion with self-supervised contrastive learning

Reference 26

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This paper cites Learning to match via inverse optimal transport.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Learning to match via inverse optimal transport

Reference 27

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This paper cites Interventional multi-instance learning with deconfounded instance-level prediction.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Interventional multi-instance learning with deconfounded instance-level prediction

Reference 28

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Observation 2b9421ff-2a48-4d3f-a829-b2be7ddd3a66 · outbound

This paper cites Interventional bag multi-instance learning on whole-slide pathological images.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Interventional bag multi-instance learning on whole-slide pathological images

Reference 29

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Swin transformer: Hierarchical vision transformer using shifted windows

Reference 30

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This paper cites Decoupled Weight Decay Regularization.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Decoupled Weight Decay Regularization

Reference 31

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Observation da8e5ebb-1d34-496f-a459-f83a695fa4d9 · outbound

This paper cites Data-efficient and weakly supervised computational pathology on whole- slide images.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Data-efficient and weakly supervised computational pathology on whole- slide images

Reference 32

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This paper cites Tumor heterogeneity: causes and consequences.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Tumor heterogeneity: causes and consequences

Reference 33

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This paper cites Unsupervised learning of visual representations by solving jigsaw puzzles.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Unsupervised learning of visual representations by solving jigsaw puzzles

Reference 34

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This paper cites A threshold selection method from gray-level histograms.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis A threshold selection method from gray-level histograms

Reference 35

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This paper cites Prevalence of neural collapse during the terminal phase of deep learning training.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Prevalence of neural collapse during the terminal phase of deep learning training

Reference 36

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This paper cites Pytorch: An im- perative style, high-performance deep learning library.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Pytorch: An im- perative style, high-performance deep learning library

Reference 37

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This paper cites Computational optimal transport: With applications to data science.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Computational optimal transport: With applications to data science

Reference 38

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This paper cites SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification

Reference 39

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This paper cites Boosting whole slide image classification from the perspectives of dis- tribution, correlation and magnification.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Boosting whole slide image classification from the perspectives of dis- tribution, correlation and magnification

Reference 40

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This paper cites Reproducibility in multiple instance learning: a case for algorithmic unit tests.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Reproducibility in multiple instance learning: a case for algorithmic unit tests

Reference 41

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This paper cites Reproducibility in multiple instance learning: a case for algorithmic unit tests.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Reproducibility in multiple instance learning: a case for algorithmic unit tests

Reference 42

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This paper cites The diagnostic path, a useful visualisation tool in virtual microscopy.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis The diagnostic path, a useful visualisation tool in virtual microscopy

Reference 44

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This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 45

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This paper cites Transmil: Transformer based correlated multiple instance learning for whole slide image classification.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Transmil: Transformer based correlated multiple instance learning for whole slide image classification

Reference 46

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Observation 4ad0d567-520c-4e95-9f43-5d942a5d02a2 · outbound

This paper cites Mor- phological prototyping for unsupervised slide representation learning in computational pathology.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Mor- phological prototyping for unsupervised slide representation learning in computational pathology

Reference 47

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Observation 66a05f42-b186-4b00-826a-1d0e505aaa4d · outbound

This paper cites Multimodal Prototyping for cancer survival prediction.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Multimodal Prototyping for cancer survival prediction

Reference 48

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This paper cites Inverse op- timal transport.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Inverse op- timal transport

Reference 49

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Observation 4b36f2ec-36a9-4b6a-b241-0995078b4366 · outbound

This paper cites Multiple instance learning framework with masked hard instance mining for whole slide image classification.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Multiple instance learning framework with masked hard instance mining for whole slide image classification

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:32:17.710097Z

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Observation 8ba3d633-8491-4fa6-adc0-117feaa2e105 · outbound

This paper cites Attention is all you need.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Attention is all you need

Reference 51

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This paper cites Revisiting multiple instance neural networks.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Revisiting multiple instance neural networks

Reference 52

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This paper cites Weakly supervised deep learning for whole slide lung cancer image analysis.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Weakly supervised deep learning for whole slide lung cancer image analysis

Reference 53

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This paper cites Transpath: Transformer-based self-supervised learning for histopatho- logical image classification.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Transpath: Transformer-based self-supervised learning for histopatho- logical image classification

Reference 54

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This paper cites Exploring low-rank property in multiple instance learning for whole slide image classifica- tion.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Exploring low-rank property in multiple instance learning for whole slide image classifica- tion

Reference 55

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This paper cites Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification

Reference 56

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This paper cites Remix: A general and efficient framework for multiple instance learning based whole slide image classification.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Remix: A general and efficient framework for multiple instance learning based whole slide image classification

Reference 57

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This paper cites Bias in cross- entropy-based training of deep survival networks.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Bias in cross- entropy-based training of deep survival networks

Reference 58

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This paper cites Deep sets.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Deep sets

Reference 59

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This paper cites Dtfd- mil: Double-tier feature distillation multiple instance learn- ing for histopathology whole slide image classification.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Dtfd- mil: Double-tier feature distillation multiple instance learn- ing for histopathology whole slide image classification

Reference 60

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This paper cites Lookahead optimizer: k steps forward, 1 step back.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Lookahead optimizer: k steps forward, 1 step back

Reference 61

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This paper cites Attention-challenging multi- ple instance learning for whole slide image classification.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Attention-challenging multi- ple instance learning for whole slide image classification

Reference 62

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This paper cites Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:32:16.255062Z

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This paper cites A graph-transformer for whole slide image classifica- tion.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis A graph-transformer for whole slide image classifica- tion

Reference 64

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Observation fe7db1be-f070-4c3e-aac9-2f6af5e7614b · outbound

This paper cites Learning deep features for discrimina- tive localization.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Learning deep features for discrimina- tive localization

Reference 65

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Observation 65145382-f29b-42b6-b8e6-e58f342d1b27 · outbound

This paper cites Are all losses created equal: A neural collapse perspective.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Are all losses created equal: A neural collapse perspective

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:32:15.985667Z

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Observation 6e7aa48a-429d-4a7e-bc5a-e473caa1984b · outbound

This paper cites Dgr-mil: Exploring diverse global representation in multiple instance learning for whole slide image classification.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis Dgr-mil: Exploring diverse global representation in multiple instance learning for whole slide image classification

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:32:15.802455Z

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Observation d3b693a5-9ebf-4a3d-b3f3-11117b8fbc21 · outbound

This paper cites How ef- fective can dropout be in multiple instance learning ? In Forty-second International Conference on Machine Learn- ing, 2025.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis How ef- fective can dropout be in multiple instance learning ? In Forty-second International Conference on Machine Learn- ing, 2025

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:32:15.630744Z

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Observation 256583e3-fb80-4cf2-abc5-ef60c8984d30 · outbound

This paper cites A geometric analysis of neu- ral collapse with unconstrained features.

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis A geometric analysis of neu- ral collapse with unconstrained features

Reference 69

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