Computer Aided Diagnosis

Dear Colleagues,

This Special Issue of the journal Applied Sciences, entitled “Computer Aided Diagnosis”, aims to present recent advances in the generation and utilization of features and machine learning techniques for biomedical image classification and retrieval. The recent advances of machine learning techniques, mostly based on deep learning, have significantly influenced the design and performance of computer aided diagnosis systems. Nowadays, deep features are often preferred to hand-crafted ones, but, at the same time, their complexity, and the poor interpretability of the extracted data, have not favoured its wide use in real applications. This Special Issue places particular attention on contributions dealing with practical applications, in which hand-crafted features still play a key role and achieve state-of-the-art performances, and where deep features are used in conjunction with specific methods for improving their interpretability.

All interested authors are invited to submit their newest results on biomedical image processing and analysis for possible publication in this Special Issue. All papers need to present original, previously unpublished work, and will be subject to the normal standards and peer-review processes of this journal. Potential topics include, but are not limited to:

Supervised segmentation;
Weakly-supervised segmentation;
Self-supervised segmentation;
Supervised detection;
Weakly-supervised detection;
Self-supervised detection;
Deep features for biomedical image classification;
Handcrafted features for biomedical image classification;
Medical image indexing and retrieval;
Medical image classification;
Computer-aided detection/diagnosis applications;
Machine learning and artificial intelligence in CAD.