Computer Vision and Machine Learning in Medical Applications, 2nd Edition

Dear Colleagues,

Recently, the use of computer vision and machine learning has spread to almost all fields. In medical applications, an immense amount of data are being generated by distributed sensors, cameras, and multi-modal digital health platforms that support audio, video, image, and text. The availability of data from medical devices and digital record systems has greatly increased the potential for automated diagnosis. The past several years have witnessed an explosion of interest in and a rapid development of computer-aided medical investigations using MRI, CT, and X-ray images and medical data. Having reached a deeper understanding of these methods, researchers are proposing elegant ways to better integrate computer vision with machine learning in complex problems and advancing the learning algorithms themselves.

This Special Issue focuses on computer vision and machine learning techniques for medical applications, including but not limited to the following:

• Intelligent medical and health systems;
• Novel theories and methods of using deep learning for medical imaging;
• Drug discovery with deep learning;
• Pandemic (such as COVID-19) management with machine learning;
• Health and medical behavior analytics with deep learning;
• Un/semi/weakly/fully supervised medical data (text/images);
• Generating diagnostic reports from medical images;
• Using machine learning for medical imbalanced datasets;
• The summarization of clinical information;
• Multimodal medical image analysis;
• Data mining for medical information;
• Organ and lesion segmentation/detection;
• Using machine learning for image classification with MRI/CT/PET;
• Medical image enhancement/denoising;
• Learning robust medical image representation with noisy annotation;
• Predicting clinical outcomes using medical data;
• Anomaly detection in medical images or data;
• Active learning and life-long learning in medical computer vision;
• User/patient psychometric modeling from video, image, audio, and text.

Dr. Chunhung Richard Lin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• computer vision
• machine learning
• medical applications

• Computer Vision and Machine Learning in Medical Applications in Bioengineering (10 articles)