From the 2 of 24 linked papers with an AI index.
12 citations · 12 across the 3 of their papers we have counts for
24 papers
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…
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…
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…
VERITAS: A Multi-Agent Co-Scientist for Verifiable Image-Derived Hypothesis Testing
Lucas Stoffl, Benedikt Wiestler, Johannes C. Paetzold
Scientific research based on multimodal clinical data (including medical imaging) requires coordinating clinical, radiological, programming, and biostatistical expertise, a fragmen…
TG-OT: Topology-guided CCTA-IVUS registration via optimal transport matching
R. L. M. van Herten, José P. Henriques, R. Nils Planken +5
Registering coronary CT angiography (CCTA) and intravascular ultrasound (IVUS) enables comprehensive coronary analysis that neither modality can provide alone, yet their fusion rem…
MAdam: Metric-Aware Multi-Objective Adam
Fengbei Liu, Rachit Saluja, Sunwoo Kwak +5
Multi-objective optimization (MOO) underlies many machine learning problems, yet MOO solvers across the loss-balancing, gradient-balancing, and Pareto-based families almost univers…