Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …

High quality and efficient medical service is one of the major factors defining living
standards. Developed countries strive to make their healthcare systems as efficient and cost …

An estimated 3 billion people lack access to dermatological care globally. Artificial
intelligence (AI) may aid in triaging skin diseases and identifying malignancies. However …

A plethora of work has shown that AI systems can systematically and unfairly be biased
against certain populations in multiple scenarios. The field of medical imaging, where AI …

Concept Bottleneck Models (CBMs) map the inputs onto a set of interpretable concepts
(``the bottleneck'') and use the concepts to make predictions. A concept bottleneck enhances …

Although advances in deep learning systems for image-based medical diagnosis
demonstrate their potential to augment clinical decision-making, the effectiveness of …

Building trustworthy and transparent image-based medical artificial intelligence (AI) systems
requires the ability to interrogate data and models at all stages of the development pipeline …

Abstract Large Vision-Language Models (LVLMs) have demonstrated remarkable
capabilities in various multimodal tasks. However their potential in the medical domain …

Undetected infection and delayed isolation of infected individuals are key factors driving the
monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of …

Fairness is a critical concern in deep learning especially in healthcare where these models
influence diagnoses and treatment decisions. Although fairness has been investigated in the …