Machine-Learning-Based Process and Analysis of Medical Images

Dear Colleagues,

In recent years, deep learning has achieved impressive success in leading to increased use of deep learning algorithms in the different fields of medical image analysis tasks. However, there are several challenges with the current deep learning models, such as deep learning algorithms being data-hungry and requiring large amounts of labeled data for achieving high performance in supervised learning settings. The collection of a large dataset requires a lot of time, resources including qualified medical experts, infrastructure, interdisciplinary collaboration, and regulatory approvals. In addition to obtaining datasets, a team of experienced doctors and computer scientists are required to provide high-quality annotations, which is extremely labor-intensive and burdensome. Despite data collection and annotations, it is not feasible to deploy large deep learning models to edge devices for various medical applications within a resource-constrained situation. The current deep learning models are not robust, and their performance can drop when there is a change in conditions (such as testing with different cohort populations, and scanners), which leads to challenges in deploying deep learning models into real-world clinical applications.  The trustworthiness and societal impact of such models have not been explored much. Despite the minimal amount of research carried out to address the limitations of the availability of limited datasets, label efficiency, and lightweight algorithms, these fields have not been fully explored. Therefore, in this Special Issue, we encourage submissions on potential research problems raised by limited datasets, label efficiency, hardware efficiency, and trustworthy and reproducible (training time and testing) deep learning that can prepare for more biomedical applications in future. This Special Issue will be devoted to unveiling the most recent progress in obtaining analytical and numerical solutions to nonlinear differential equations through various methods and to stimulating collaborative research activities.

Dr. Debesh Jha
Guest Editor

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• deep learning (architecture, generative models, real-time algorithms, lightweight network design, etc.)
• medical image segmentation/classification with limited training datasets
• trustworthy machine learning (privacy, fairness, transparency, safety, ethics, AI safety, etc.)
• computer-aided diagnosis
• image segmentation
• weakly/semi/unsupervised/self-supervised learning methods
• resource-efficient learning
• out-of-distribution detection
• early cancer detection and diagnosis
• single-shot/one-shot/few-shot learning methods
• imaging informatics
• domain adaptation
• biomedical applications (endoscopy, colonoscopy, Alzheimer's disease, laparoscopy, head and neck, organs at risk, prostate, lung cancer, liver, breast, etc.)
• rare disease diagnosis with limited training datasets
• surgical data science