Machine Learning in Medical Imaging

This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic.

The 68 papers presented in this volume were carefully reviewed and selected from 101 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.

• artificial intelligence
• automatic segmentations
• bioinformatics
• cellular image analysis
• computer vision
• computer-aided diagnosis
• deep learning
• image analysis
• image processing
• image quality
• image reconstruction
• image segmentation
• machine learning
• medical images
• molecular imaging
• network protocols
• neural networks
• pattern recognition
• segmentation methods
• signal processing

• University of North Carolina at Chapel Hill, Chapel Hill, USA

  Mingxia Liu, Chunfeng Lian

• Rensselaer Polytechnic Institute, Troy, USA

  Pingkun Yan

• United Imaging Intelligence, Shanghai, China

  Xiaohuan Cao

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