Memory-aware curriculum federated learning for breast cancer classification

Abstract

For early breast cancer detection, regular screening with mammography imaging is recommended. Routinary examinations result in datasets with a predominant amount of negative samples. A potential solution to such class-imbalance is joining forces across multiple institutions. Developing a collaborative computer-aided diagnosis system is challenging in different ways. Patient privacy and regulations need to be carefully respected. Data across institutions may be acquired from different devices or imaging protocols, leading to heterogeneous non-IID data. Also, for learning-based methods, new optimization strategies working on distributed data are required. Recently, federated learning has emerged as an effective tool for collaborative learning. In this setting, local models perform computation on their private data to update the global model. The order and the frequency of local updates influence the final global model. Hence, the order in which samples are locally presented to the optimizers plays an important role. In this work, we define a memory-aware curriculum learning method for the federated setting. Our curriculum controls the order of the training samples paying special attention to those that are forgotten after the deployment of the global model. Our approach is combined with unsupervised domain adaptation to deal with domain shift while preserving data privacy. We evaluate our method with three clinical datasets from different vendors. Our results verify the effectiveness of federated adversarial learning for the multi-site breast cancer classification. Moreover, we show that our proposed memory-aware curriculum method is beneficial to further improve classification performance. Our code is publicly available at: https://github.com/ameliajimenez/curriculum-federated-learning.

arXiv commentComputer Methods and Programs in Biomedicine

References in corpus (19)

• Communication-Efficient Learning of Deep Networks from Decentralized Data
• mixup: Beyond Empirical Risk Minimization
• The Future of Digital Health with Federated Learning
• Learning Transferable Features with Deep Adaptation Networks
• Unsupervised Data Augmentation for Consistency Training
• Temporal Ensembling for Semi-Supervised Learning
• Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
• End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design
• Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography
• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
• FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
• Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification
• Federated Adversarial Domain Adaptation
• Automatic CAD-RADS Scoring Using Deep Learning
• Algorithms and Theory for Multiple-Source Adaptation
• Private Federated Learning with Domain Adaptation
• Medical-based Deep Curriculum Learning for Improved Fracture Classification
• Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertainty
• FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation

Cited by in corpus (3)

• Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
• Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection
• Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction