Title

Weakly Supervised Representation Learning for Histopathology Image Analysis

Alternative Title

조직병리학 이미지 분석을 위한 약지도표현학습

DGIST Authors

Philip Chikontwe ; Sang Hyun Park ; Sunghoon Im

Advisor

박상현

Co-Advisor(s)

Sunghoon Im

Issued Date

2023

Awarded Date

2023-02-01

Citation

Philip Chikontwe. (2023). Weakly Supervised Representation Learning for Histopathology Image Analysis. doi: 10.22677/THESIS.200000652733

Type

Thesis

Description

Computer aided diagnosis, Deep learning, Histopathology, Multiple instance learning, Weak supervision

Table Of Contents

I. Introduction 1
1 Background and Motivations 1
1.1 Weakly Supervised Learning 3
1.2 Multiple Instance Learning 4
1.3 Histopathology Image Analysis 6
2 Contributions and Outline 7
3 Publications 8
3.1 Excluded Research 9

II. End-to-End Multiple Instance Learning with Center Embeddings 11
1 Introduction 11
2 Related Works 12
2.1 Multiple Instance Learning for Histopathology 13
2.2 Unsupervised Methods with Multiple Instance Learning 13
3 Methodology 13
3.1 Top-k Instance Selection 14
3.2 Instance Level Learning 14
3.3 Pyramidal Bag-level Learning 15
3.4 Soft-Assignment based Inference 15
4 Evaluation 16
4.1 Dataset and Settings 16
4.2 Quantitative Results 17
4.3 Qualitative Results 18
5 Conclusion 19

III. Dual Multiple Instance Learning with Self-Supervision 20
1 Introduction 20
2 Related Works 22
2.1 Deep Learning for COVID-19 Diagnosis 22
2.2 COVID-19 Diagnosis under Weak Supervision 22
2.3 Self-Supervised Learning 23
3 Methodology 23
3.1 Dual Attention based Learning 25
3.2 Contrastive Multiple Instance Learning 26
4 Evaluation 27
4.1 Datasets and Settings 27
4.2 Quantitative Results 29
4.3 Qualitative Results 30
5 Discussion 31
6 Conclusion 32

IV. Weakly Supervised Segmentation in Gigapixel Images 33
1 Introduction 33
2 Related Works 35
2.1 Weakly Supervised Segmentation in Computer Vision 35
2.2 Patch-based Weakly Supervised Segmentation for Histopathology 36
2.3 Weakly Supervised Learning with Self-Training 36
3 Methodology 37
3.1 Contrastive Neural Compression 38
3.2 Self-Supervised Neural CAM Refinement 39
3.3 τ -MaskOut: Spatial Feature Masking as Regularization 40
3.4 Pixel Correlation Module (PCM) 41
3.5 Conditional Entropy Minimization between CAMs 42
4 Evaluation 43
4.1 Datasets 43
4.2 Implementation Settings 44
4.3 Quantitative Results 46
4.4 Ablations on Learning Objectives 49
4.5 Qualitative Results 50
4.6 Ablations on Relative Tumor Sizes 51
5 Conclusion 52

V. Re-calibrating Feature Distributions in Histopathology 53
1 Introduction 53
2 Methodology 55
2.1 Preliminaries: A Simple Baseline 56
2.2 Feature Re-calibration & Max-Instance Selection 57
2.3 Positional Encoding Module (PEM) 57
2.4 MIL Pooling by Multi-head Self-Attention (PMSA) 58
3 Evaluation 59
3.1 Datasets and Settings 59
3.2 Main Results 59
3.3 Impact of Learning Objectives 61
4 Conclusion 61

VI. Concluding Remarks 62
1 Conclusion 62
2 Future Work 63

VII. Acknowledgement 64
References 65

URI

http://hdl.handle.net/20.500.11750/45676
http://dgist.dcollection.net/common/orgView/200000652733

DOI

10.22677/THESIS.200000652733

Degree

Doctor

Department

Department of Robotics and Mechatronics Engineering

Publisher

DGIST

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