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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,… Read more
SUSTAINABLE DEVELOPMENT
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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)
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