Deep Learning for Pathology Detection and Diagnosis in Medical Imaging

Dear Colleagues,

Severe pathologies, such as the diffuse liver diseases or tumors, can lead to the significant degradation of the human health and sometimes to lethal stages. The most reliable methods for the diagnosis of these affections, such as the classical biopsy or surgery, are invasive and dangerous. Advanced computerized methods are urgently needed to reduce invasiveness and enhance the information derived from medical images as much as possible by unveiling their subtle aspects, conducting to a virtual biopsy. Computer Vision and Machine Learning can be successfully employed to achieve this target. Thus, advanced image analysis combined with conventional machine learning, as well as the deep learning techniques, can lead to a highly accurate automatic diagnosis process. The corresponding features, together with the classification, segmentation, fusion of multiple image modalities, and 3D reconstruction techniques, can be involved in the achievement of appropriate 2D and 3D models for the considered affections, which are helpful in computer-aided diagnosis and surgery.

The current special issue expects scientific articles with original contributions in these directions, as well as surveys in the described domain. The papers should cover domain topics that include but are not limited to:

• Deep learning and conventional methods for severe pathology detection and diagnosis in medical imaging (e.g. US, CEUS, elastography images, CT, MRI, radiographies, endoscopy, microscopic images, etc.)
• Pathologies of interest (malignant/benign tumors, diffuse liver diseases, skin pathology, brain diseases, heart diseases, etc.)
• Early detection of the severe pathologies based on medical images through deep learning methods
• Deep learning methods for recognition within medical images
• Segmentation through deep learning techniques within medical images
• Comparisons and combinations between the conventional and the deep learning methods for recognition and segmentation within medical images
• Fusion of multiple medical image modalities, for improved pathology detection and diagnosis
• 2D and 3D models of severe pathologies, for improved pathology detection and diagnosis
• 3D reconstruction based on medical images, for improved pathology detection and diagnosis

Prof. Dr. Sergiu Nedevschi
Dr. Delia-Alexandrina Mitrea
Guest Editors

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