Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an
underestimated global problem. In biomedical image analysis, chosen performance metrics …

Interactive segmentation is a crucial research area in medical image analysis aiming to
boost the efficiency of costly annotations by incorporating human feedback. This feedback …

Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update | Radiology:
Artificial Intelligence RSNA "skipMainNavigation" closeDrawerMenuopenDrawerMenu Home …

Measuring similarity of neural networks to understand and improve their behavior has
become an issue of great importance and research interest. In this survey, we provide a …

In the field of biomedical image analysis, the quest for architectures capable of effectively
capturing long-range dependencies is paramount, especially when dealing with 3D image …

Analyzing immune cell interactions in the bone marrow is vital for understanding
hematopoiesis and bone homeostasis. Three-dimensional analysis of the complete, intact …

Purpose: To develop an open-source and easy-to-use segmentation model that can
automatically and robustly segment most major anatomical structures in MR images …

Brain lesion segmentation provides a valuable tool for clinical diagnosis and research, and
convolutional neural networks (CNNs) have achieved unprecedented success in the …

In silico labeling is the computational cross-modality image translation where the output
modality is a subcellular marker that is not specifically encoded in the input image, for …

This paper does not describe a novel method. Instead, it studies an essential foundation for
reliable benchmarking and ultimately real-world application of AI-based image analysis …