# MedSAM

The work proposes Class-Aware Asymmetric Weighted Conformal Prediction (CA-WCP), which combines latent-space density-ratio weighting with directional quantiles that separately calibrate lower and upper volume bounds and scales each bound by a class-specific asymmetry factor derived from validation-set false-positive and false-negative rates; the authors prove it retains the weighted-exchangeability marginal coverage guarantee per class and show on BraTS 2020 and a covariate-shifted synthetic multi-organ CT benchmark that 95% Clopper–Pearson intervals for observed coverage contain the nominal 90% level for every class while interval width shrinks 8–14% versus symmetric weighted conformal prediction.

The work proposes Class-Aware Asymmetric Weighted Conformal Prediction (CA-WCP), which combines latent-space density-ratio weighting with directional quantiles that separately calibrate lower and upper volume bounds and scales each bound by a class-specific asymmetry factor derived from validation-set false-positive and false-negative rates; the authors prove it retains the weighted-exchangeability marginal coverage guarantee per class and show on BraTS 2020 and a covariate-shifted synthetic multi-organ CT benchmark that 95% Clopper–Pearson intervals for observed coverage contain the nominal 90% level for every class while interval width shrinks 8–14% versus symmetric weighted conformal prediction.

Training a unified multi-task model on ThyroidXL (11,635 images, 4,093 patients) and testing externally on DDTI (660 images), this work characterizes negative transfer between segmentation and TI-RADS malignancy classification under cross-center domain shift for a CNN (ResNet34) and a medical ViT (MedSAM), and proposes lightweight decoder-side adapters, MKGA and its residual variant ResMKGA, which refine multi-scale skip features with complementary receptive fields and apply semantic context-conditioned gating to suppress artifact-prone content; the adapters improve out-of-domain segmentation stability (ResNet34+MKGA external Dice 0.659, ResMKGA 0.671, versus 0.590 for the unfrozen baseline) and, in the CNN setting, significantly raise TI-RADS diagnostic accuracy (0.406 to 0.

Training a unified multi-task model on ThyroidXL (11,635 images, 4,093 patients) and testing externally on DDTI (660 images), this work characterizes negative transfer between segmentation and TI-RADS malignancy classification under cross-center domain shift for a CNN (ResNet34) and a medical ViT (MedSAM), and proposes lightweight decoder-side adapters, MKGA and its residual variant ResMKGA, which refine multi-scale skip features with complementary receptive fields and apply semantic context-conditioned gating to suppress artifact-prone content; the adapters improve out-of-domain segmentation stability (ResNet34+MKGA external Dice 0.659, ResMKGA 0.671, versus 0.590 for the unfrozen baseline) and, in the CNN setting, significantly raise TI-RADS diagnostic accuracy (0.406 to 0.

The work introduces the first framework that explicitly disentangles prompt dependence into prompt ambiguity (inter-user variability) and local sensitivity (interaction imprecision): a mixture density network models the image-conditioned distribution of plausible prompts and quantifies ambiguity by the trace of its covariance, while a stability margin captures the minimal perturbation under measurement noise needed to induce a mask change; evaluated on two female pelvic T2-weighted MRI datasets, UT-EndoMRI (81 patients with endometriosis, uterus segmentation) and MOGaMBO (94 patients with locally advanced cervical cancer, bladder segmentation), with MobileSAM and MedSAM, the two metrics correlate significantly and negatively with Dice, correlate little with each other, and conditional samp

The work introduces the first framework that explicitly disentangles prompt dependence into prompt ambiguity (inter-user variability) and local sensitivity (interaction imprecision): a mixture density network models the image-conditioned distribution of plausible prompts and quantifies ambiguity by the trace of its covariance, while a stability margin captures the minimal perturbation under measurement noise needed to induce a mask change; evaluated on two female pelvic T2-weighted MRI datasets, UT-EndoMRI (81 patients with endometriosis, uterus segmentation) and MOGaMBO (94 patients with locally advanced cervical cancer, bladder segmentation), with MobileSAM and MedSAM, the two metrics correlate significantly and negatively with Dice, correlate little with each other, and conditional samp

This preprint benchmarks eight open-source thigh muscle MRI segmentation tools on the MyoSegmenTUM, AIPS and Sheffield base datasets plus derived pathological and augmented out-of-distribution sets, combining Dice, Jaccard, Hausdorff, boundary IoU and inter-slice Dice ratio with qualitative usability review, and finds that domain-specific U-Net models, especially MuscleMap, generally outperformed foundation-model and newer general-purpose approaches, that adding SAM variants usually degraded rather than improved segmentation quality, and that most tools showed reduced accuracy on pathological cases.

This preprint benchmarks eight open-source thigh muscle MRI segmentation tools on the MyoSegmenTUM, AIPS and Sheffield base datasets plus derived pathological and augmented out-of-distribution sets, combining Dice, Jaccard, Hausdorff, boundary IoU and inter-slice Dice ratio with qualitative usability review, and finds that domain-specific U-Net models, especially MuscleMap, generally outperformed foundation-model and newer general-purpose approaches, that adding SAM variants usually degraded rather than improved segmentation quality, and that most tools showed reduced accuracy on pathological cases.