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From the 1 of 11 linked papers with an AI index.

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11 papers

cs.CV2026

Improving Medical Image Generative Models with Fréchet Distance Loss

Andrew Marshall, Xuanang Xu, Xiaoran Zhang +3

The paper introduces a Fréchet Distance loss to fine‑tune diffusion generative models so they better reproduce the irregular shapes of tumors in medical images, leading to higher-q…

cs.CV2026

MRI2Rep: Autoregressive Structured Report Generation for 3D Liver MRI

Xinran Li, Junlin Yang, Annabella Shewarega +4

Manual reporting of 3D MRI studies is time-consuming, yet end-to-end structured report generation for 3D liver MRI remains underexplored due to volumetric complexity and scarce pai…

cs.LG2026

FM-fMRI: Event Conditioned Flow Matching for Rest-to-Task fMRI Time-Series Synthesis

Peiyu Duan, Jiyao Wang, Nicha C. Dvornek +4

Task-based fMRI provides a direct readout of task-evoked neural dynamics, but it is expensive and difficult to acquire at scale, motivating rest-to-task synthesis from widely avail…

cs.LG2026

Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning

Jiyao Wang, Peiyu Duan, Nicha C. Dvornek +4

Functional magnetic resonance imaging (fMRI) is a powerful tool for investigating human brain function. However, the high cost of data acquisition and the inherent subjectivity of…

cs.CV2026

BioFact-MoE: Biologically Factorized Mixture of Experts for Vision-Language Prognostic Modeling in Hepatocellular Carcinoma

Junlin Yang, Tian Yu, Nicha C. Dvornek +6

Hepatocellular carcinoma (HCC) is biologically heterogeneous, shaped by the interplay between hepatic functional reserve and tumor-related oncologic factors; thus, similar survival…

cs.CV2025

Spatially-Aware Evaluation of Segmentation Uncertainty

Tal Zeevi, Eléonore V. Lieffrig, Lawrence H. Staib +1

Uncertainty maps highlight unreliable regions in segmentation predictions. However, most uncertainty evaluation metrics treat voxels independently, ignoring spatial context and ana…