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From the 2 of 35 linked papers with an AI index.

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20242026
most citedThe TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography

12 citations · 16 across the 12 of their papers we have counts for

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18 papers · 1 filter

cs.CV2026

Unifying Active Learning and Semi-Supervised Learning for Medical Image Segmentation

Bahram Jafrasteh, Cheng Wan, Heejong Kim +2

In practical settings, medical image segmentation models are often developed with limited annotated data rather than fully labeled datasets. Training frequently begins in ultra-low…

cs.CV2026

Beyond scalar losses: calibrating segmentation models via gradient vector field surgery

Laurin Lux, Alexander H. Berger, Moritz Knolle +2

The paper introduces a gradient‑based modification to region‑based loss functions that scales the gradient magnitude with prediction error, improving calibration of medical image s…

cs.CV202612 cited

The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography

Kaiyuan Yang, Fabio Musio, Yihui Ma +112

The paper introduces the TopCoW Challenge, a benchmark for automatically segmenting the Circle of Willis in CT and MR angiography using deep learning, and provides a new annotated…

cs.CV2026

BackSplit: The Importance of Sub-dividing the Background in Biomedical Lesion Segmentation

Rachit Saluja, Asli Cihangir, Ruining Deng +3

Segmenting small lesions in medical images remains notoriously difficult. Most prior work tackles this challenge by either designing better architectures, loss functions, or data a…

cs.CV2026

A Multimodal 3D Foundation Model for Light Sheet Fluorescence Microscopy Enables Few-Shot Segmentation, Classification, and Deblurring

Adina Scheinfeld, Haotan Zhang, Shang Mu +5

Light sheet fluorescence microscopy (LSM) enables high-resolution, three-dimensional (3D) imaging of biological specimens, providing rich volumetric data for studying cellular orga…

cs.CV2025

Synthetic Vasculature and Pathology Enhance Vision-Language Model Reasoning

Chenjun Li, Cheng Wan, Laurin Lux +4

Vision-Language Models (VLMs) offer a promising path toward interpretable medical diagnosis by allowing users to ask about clinical explanations alongside predictions and across di…