Deep Learning and Explainable Artificial Intelligence for Medical Image Analysis
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 374
Special Issue Editors
Interests: medical imaging; explainable artificial intelligence; deep learning; image-guided surgery
Interests: intelligent decision support systems for medicine; biomedical image processing and understanding; radiomics and radiogenomics for precision medicine; bioinformatics; digital pathology and pathomics
Interests: computer vision (feature extraction and pattern analysis); scene and event understanding (by people and/or vehicles and/or objects); human–computer interaction (pose estimation and gesture recognition by hands and/or body); sketch-based interaction (handwriting and freehand drawing); human–behaviour recognition (actions, emotions, feelings, affects, and moods by hands, body, facial expressions, and voice); biometric analysis (person re-identification by body visual features and/or gait and/or posture/pose); artificial intelligence (machine/deep learning); medical image analysis (MRI, ultrasound, X-rays, PET, and CT); multimodal fusion models; brain–computer interfaces (interaction and security systems); signal processing; visual cryptography (by RGB images); smart environments and natural interaction (with and without virtual/augmented reality); robotics (monitoring and surveillance systems with PTZ cameras, UAVs, AUVs, rovers, and humanoids)
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Special Issue Information
Dear Colleagues,
Deep Learning has revolutionized the medical imaging field in the last decade. The possibility to automatically extract hierarchical feature representation from raw data has facilitated the development of algorithms for Medical Image Analysis, in different Computer Vision tasks such as classification, localization, segmentation, object detection, instance segmentation, and many more. On the other hand, despite the success of Deep Learning models, the hidden decision-making mechanism is hindering their deployment in clinical practice, where interpretability holds paramount significance for specialists. Explainable Artificial Intelligence (XAI) offers the possibility to unveil the black-box nature of Deep Learning models to build trust between the Deep Learning models and the clinicians. Furthermore, the quantitative and qualitative measures to evaluate the XAI methods are indispensable to unleash the full potential of Deep Learning in healthcare.
Hence, we welcome novel research articles, as well as comprehensive reviews and survey articles, spanning the applications of Deep Learning and XAI in Medical Image Analysis.
Topics of interest include, but are not limited to:
- Deep Learning for Medical Image Analysis
- Explainable Artificial Intelligence
- Intelligent Imaging Systems
- Clinical Decision Support Systems
- Computer-Aided Diagnosis Systems
- Autonomous Healthcare Systems
- Evaluation of XAI in Medical Imaging
- Expert Systems based on Convolutional Neural Networks
- Vision Transformers for Medical Image Understanding
Dr. Sardar Mehboob Hussain
Dr. Nicola Altini
Dr. Danilo Avola
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning
- medical image analysis
- explainable artificial intelligence
- clinical decision support
- computer-aided diagnosis
- convolutional neural networks
- vision transformers
