Public articles linked to the same research event.
arXiv 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.
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.