CARD: Calibration via Agreement in Reverse Diffusion for Out-of-Domain MRI Segmentation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2606. 19300v1 Announce Type: cross Abstract: Glioma segmentation in multiparametric MRI is a critical component of treatment planning.
The study evaluates the reliability of deep‑ensemble uncertainty for brain tumour segmentation on the BraTS‑GoAT dataset. A 5‑fold cross‑validated nnU‑Net baseline and a 3‑seed deep ensemble were compared for calibration and error detection; the ensemble showed modest gains in calibration on in‑distribution data but the single model’s confidence remained flat while accuracy degraded under synthetic corruptions. Disagreement among ensemble members rose sharply with corruption severity, proving to be a more sensitive indicator of acquisition shift than single‑model confidence.
arXiv:2607. 16317v1 Announce Type: cross Abstract: Deep networks now subtype brain tumors on MRI about as well as specialist readers, yet accuracy is not what keeps them out of the clinic.
arXiv:2609.21412v1 Announce Type: new Abstract: Medical image segmenters often get worse when sites, scanner vendors, or protocols change. Continual test-time adaptation (CTTA) addresses this problem...
The paper introduces Missing Modality-Aware Local Temperature Scaling (MMA‑LTS), a post‑hoc voxel‑wise confidence calibration technique designed for multimodal brain tumor segmentation. MMA‑LTS adapts a temperature field based on which MRI modalities are present and a voxel‑wise difficulty score, addressing calibration errors that arise when specific modalities are missing. Experiments on BraTS 2020 and FeTS 2024 demonstrate that MMA‑LTS improves calibration while maintaining segmentation accuracy across various missing‑modality scenarios.
arXiv:2608. 08173v1 Announce Type: cross Abstract: Longitudinal MRI enables sensitive measurement of structural brain change for studying aging and neurodegenerative disease.