About this Research Topic
Computer-assisted diagnosis and prognosis prediction (especially with medical images) consist of a series of long-standing tasks, including classification, regression, segmentation, tracking tasks, etc. Deep learning is one of the most important breakthroughs in the field of artificial intelligence over the last decade. It has achieved great success because of enormously increasing data and computation resources. Not only there has been a constantly growing flow of related research papers, but also substantial progress has been achieved in real-world applications including axillary lymph node (ALN) metastasis status prediction, radiotherapy planning, histological image understanding, retina image recognition, etc.
The aim of this Research Topic is to cover the promising and novel deep learning algorithms in automatic diagnosis and prognosis prediction. Potential topics include, but are not limited to：
1. Reviews that summarize the current state of the art, legacy data as well as future prospects for research.
2. Methodological articles that present cutting-edge algorithms in the study of automatic diagnosis and prognosis prediction using medical images or/and other sources of information. This could include advances in data mining, image processing, learning framework, and application of SOTA deep learning algorithms, but also new theoretical frameworks for understanding the possibility of automatic diagnosis and prognosis prediction via medical images. Potential techniques include, but are not limited to:
（1） Advanced Transfer Learning techniques that transfer knowledge from other tasks or modals.
（2） Multi-modal Learning techniques that enable multi-modal diagnosis or prognosis prediction.
（3） Novel multi-task learning framework that enables joint diagnosis and prognosis prediction in a single model.
（4） Advanced Unsupervised/Semi-Supervised/Weak-Supervised Learning techniques which boost performance with limited annotations.
3. Benchmark articles that provide datasets containing various information especially medical images and corresponding diagnostic and/or prognostic annotations, and define the prediction task, evaluation metrics, and provide baseline algorithms.
4. Other related aspects
The aim of this Research Topic is to cover the promising and novel deep learning algorithms in automatic diagnosis and prognosis prediction. Potential topics include, but are not limited to：
1. Reviews that summarize the current state of the art, legacy data as well as future prospects for research.
2. Methodological articles that present cutting-edge algorithms in the study of automatic diagnosis and prognosis prediction using medical images or/and other sources of information. This could include advances in data mining, image processing, learning framework, and application of SOTA deep learning algorithms, but also new theoretical frameworks for understanding the possibility of automatic diagnosis and prognosis prediction via medical images. Potential techniques include, but are not limited to:
（1） Advanced Transfer Learning techniques that transfer knowledge from other tasks or modals.
（2） Multi-modal Learning techniques that enable multi-modal diagnosis or prognosis prediction.
（3） Novel multi-task learning framework that enables joint diagnosis and prognosis prediction in a single model.
（4） Advanced Unsupervised/Semi-Supervised/Weak-Supervised Learning techniques which boost performance with limited annotations.
3. Benchmark articles that provide datasets containing various information especially medical images and corresponding diagnostic and/or prognostic annotations, and define the prediction task, evaluation metrics, and provide baseline algorithms.
4. Other related aspects
Keywords: Computer-aided Diagnosis，Medical Images, Prognosis Prediction, Deep Learning, Machine Learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
