arXiv Computer Vision
DeferredSeg:A Multi-Expert Deferral Framework for Medical Image Segmentation
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People-Centred Medical Image Analysis via Fairness-Aware Human-AI Cooperation
arXiv:2604. 26991v2 Announce Type: replace-cross Abstract: Machine learning models for medical image analysis often exhibit subgroup-dependent performance, which impacts how decisions should be allocated between automated systems and human experts under limited resources.
arXiv Machine Learning
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Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentation
arXiv:2608. 14766v1 Announce Type: cross Abstract: Uncertainty estimation is critical for the safe clinical deployment of deep learning in medical image segmentation, with aleatoric uncertainty theoretically designed to capture irreducible data ambiguity.
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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification
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Trustworthy Medical Segmentation: Uncertainty-Aware U-Net Evaluation Under Clinical Image Degradation
arXiv:2607. 22727v1 Announce Type: cross Abstract: Medical image segmentation models often report high benchmark accuracy under ideal imaging conditions, yet their failures under clinical degradation can be quiet: sensor noise, patient motion, low- resolution acquisition, and contrast variability may all alter model behavior without producing an obvious warning.
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OPERA: Offline Policy-guided Expert Routing and Adaptation for Universal Biomedical Image Analysis
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From Reasoning to Pixels: Grounded Medical Multimodal LLMs for VQA and Segmentation
The paper introduces MedREAL, a unified framework that aligns linguistic reasoning with spatial grounding for medical visual question answering and segmentation. MedREAL employs Seg Anchored Reasoning Pooling (SARP) to extract semantic evidence from segmentation tokens and a Reasoning-to-Visual (R2V) fusion mechanism to integrate these features into a segmentation pipeline. Using the newly created MedRAVS-13K dataset, MedREAL achieves superior performance, reporting 68.49% gIoU and 70.47% cIoU, and generates evidence masks that consistently match textual diagnoses.