Meta Learning With Medical Imaging and Health Informatics Applications

1st Edition - September 24, 2022
Editors: Hien Van Nguyen, Ronald Summers, Rama Chellappa
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 8 5 1 - 2
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 8 5 2 - 9

Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task,… Read more

Meta Learning With Medical Imaging and Health Informatics Applications

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Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task, Meta-Learning constructs computational mechanisms to systematically and efficiently adapt to new tasks. The meta-learning paradigm has great potential to address deep neural networks’ fundamental challenges such as intensive data requirement, computationally expensive training, and limited capacity for transfer among tasks.

This book provides a concise summary of Meta-Learning theories and their diverse applications in medical imaging and health informatics. It covers the unifying theory of meta-learning and its popular variants such as model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize. The book brings together thought leaders from both machine learning and health informatics fields to discuss the current state of Meta-Learning, its relevance to medical imaging and health informatics, and future directions.

1. Meta-Learning Theory

1.1. A Gentle Introduction to Meta-Learning (Hien Van Nguyen)

1.2. Prototypical Networks for Few-Shot Learning (Jake Snell)

1.3. Model-Agnostic Meta-Learning (Chelsea Finn)

1.4. Memory-Augmented Meta-Learning (Adam Santoro)

1.5. Optimization As Models (Hugo Larochelle)

1.6. MetaReg: Towards Domain Generalization using Meta-Regularization (Rama Chellappa)

2. Meta-Learning for Medical Image Detection and Segmentation

2.1. Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy (Yu Tian)

2.2. Automatic Detection of Rare Pathologies in Fundus Photographs Using Few-Shot Learning (Gwenolé Quellec)

2.3. Learn to Segment Organs with A Few Bounding Boxes (Abhijeet Parida)

2.4. Shape-Aware Meta-Learning for Generalizing Prostate MRI Segmentation to Unseen Domains (Quande Liu)

2.5. Self-Supervision with Superpixels: Training Few-Shot Medical Image Segmentation Without Annotation (Cheng Ouyang)

2.6. Semi-Supervised Few-Shot Learning for Medical Image Segmentation (Abdur Feyjie)

2.7. ‘Squeeze & Excite’ Guided Few-Shot Segmentation of Volumetric Images (Abhijit Guha Roy)

2.8. A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Image Segmentation (Erica Rutter)

2.9. Learning to Segment Skin Lesions from Noisy Annotations (Zahra Mirikharaji)

2.10. Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks (Wangbin Ding)

3. Meta-Learning for Medical Image Diagnosis

3.1. Lung Cancer Screening Using Adaptive Memory-Augmented Recurrent Networks (Aryan Mobiny)

3.2. Few-shot chest x-ray diagnosis using discriminative ensemble learning (Angshuman Paul)

3.3. Training Medical Image Analysis Systems Like Radiologists (Gabriel Maicas)

3.4. Additive Angular Margin for Few-Shot Learning to Classify Clinical Endoscopy Images (Sharib Ali)

3.5. Few-Shot Decision Tree for Diagnosis of Ultrasound Breast Tumor Using Bi-Rads Features (Qinghua Huang)

3.6. Classification of Femur Fracture in Pelvic X-Ray Images Using Meta-Learned Deep Neural Network (Changhwan Lee)

3.7. Overcoming Data Limitation in Medical Visual Question Answering (Binh Duong Nguyen)

3.8. Task Adaptive Metric Space for Medium-Shot Medical Image Classification (Xiang Jiang)

3.9. Unsupervised Task Design to Meta-Train Medical Image Classifiers (Gabriel Maicas)

3.10. Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learning (Jia-Fong Yeh)

3.11. Deep Learning from Small Amount of Retinopathy Data with Noisy Labels: A Meta-Learning Approach (Görkem Algan)

3.12. Learning from The Guidance: Knowledge Embedded Meta-Learning for Medical Visual Question Answering (Wenbo Zhen)

4. Meta-Learning for Other Biomedical Applications

4.1. Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification (Pengyu Yuan)

4.2. A New Benchmark for Evaluation of Cross-Domain Few-Shot Learning (Yunhui Guo) (Meta-Learning Across Modality)

4.3. Few-Shot Learning for Dermatological Disease Diagnosis (Viraj Prabhu)

4.4. Metaphys: Unsupervised Few-Shot Adaptation for Non-Contact Physiological Measurement (Xin Liu)

4.5. Few-Shot Microscopy Image Cell Segmentation (Youssef Dawoud)

4.6. Few-Shot Learning in Histopathological Images: Reducing The Need of Labeled Data on Biological Datasets (Alfonso Medela)

4.7. Difficulty-Aware Meta-Learning for Rare Skin Disease Diagnosis (Xiaomeng Li)

4.8. Few-Shot Meta-Denoising of Medical Images (Leslie Casas)

4.9. Few-Shot Pill Recognition (Suiyi Ling)

5. Meta-Learning for Health Informatics

5.1. Metapred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records (Xi Zhang)

5.2. Pearl: Prototype Learning Via Rule Learning on ICU Patient Data (Tianfan Fu)

5.3. Deep Mixed Effect Model Using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare (Ingyo Chung)

5.4. Personalized Multitask Learning for Predicting Tomorrow’s Mood, Stress, and Health (Sara Taylor)

5.5. Multi-Task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases (Luchen Liu)

5.6. Effectiveness of Rotation Forest in Meta-Learning Based Gene Expression Classification (Gregor Stiglic)

5.7. A Meta-Learning Framework Using Representation Learning to Predict Drug-Drug Interaction (Deepika)

5.8. MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-Signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning (Nannapas Banluesombatkul)

5.9. A Deep Meta-Learning Framework for Heart Disease Prediction (Iman Salem)

5.10. Meta-Learning with Selective Data Augmentation for Medical Entity Recognition (Asma Ben Abacha)

5.11. Investigating Active Learning and Meta-Learning for Iterative Peptide Design (Rainier Barrett)

5.12. Tadanet: Task-Adaptive Network for Graph-Enriched Meta-Learning on Intensive Care Data (Qiuling Suo)

Hien Van Nguyen

Dr. Hien Van Nguyen is an Assistant Professor of the Department of Electrical and Computer Engineering Department at the University of Houston. His research interests are at the intersection between machine learning, computer vision, and biomedical image analysis. He has published 45 peer-reviewed papers and received 12 U.S. patents. His research has received awards from the National Science Foundation and the National Institutes of Health. He has served as area chairs of MICCAI (2019, 2021) and organized a series of popular MICCAI tutorials including deep learning for medical imaging (2015), deep reinforcement learning for medical imaging (2018), Bayesian deep learning (2019).

Affiliations and expertise

Assistant Professor, Department of Electrical and Computer Engineering Department, University of Houston, USA

Ronald Summers

Dr. Summers received a BA in physics and the M.D. and Ph.D. degrees in medicine/anatomy and cell biology from the University of Pennsylvania. He completed a medical internship at the Presbyterian-University of Pennsylvania Hospital, Philadelphia, PA, a radiology residency at the University of Michigan, Ann Arbor, MI, and an MRI fellowship at Duke University. In 2000, he received the Presidential Early Career Award for Scientists and Engineers, presented by Dr. Neal Lane, President Clinton's science advisor. In 2012, he received the NIH Director's Award, presented by NIH Director Dr. Francis S. Collins. He is an editorial board member of the journals Radiology and Academic Radiology. He was a co-chair of the Computer-aided Diagnosis program of the annual SPIE Medical Imaging conference in 2010 and 2011. He has co-authored over 300 journal, review and conference proceedings articles, and is a co-inventor on 12 patents.

Affiliations and expertise

University of Michigan, Ann Arbor, MI, USA

Rama Chellappa

Prof. Rama Chellappa received the B.E. (Hons.) degree from the University of Madras, India, in 1975 and the M.E. (Distinction) degree from Indian Institute of Science, Bangalore, in 1977. He received M.S.E.E. and Ph.D. Degrees in Electrical Engineering from Purdue University, West Lafayette, IN, in 1978 and 1981 respectively. Since 1991, he has been a Professor of Electrical Engineering and an affiliate Professor of Computer Science at University of Maryland, College Park. He is also affiliated with the Center for Automation Research (Director) and the Institute for Advanced Computer Studies (Permanent Member). In 2005, he was named a Minta Martin Professor of Engineering. Prior to joining the University of Maryland, he was an Assistant (1981-1986) and Associate Professor (1986-1991) and Director of the Signal and Image Processing Institute (1988-1990) at University of Southern California, Los Angeles. Over the last 29 years, he has published numerous book chapters, peer-reviewed journal and conference papers. He has co-authored and edited books on MRFs, face and gait recognition and collected works on image processing and analysis. His current research interests are face and gait analysis, markerless motion capture, 3D modeling from video, image and video-based recognition and exploitation and hyper spectral processing.

Affiliations and expertise

University of Maryland, College Park, MD, USA