Meta Learning With Medical Imaging and Health Informatics Applications

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)