Hugging Face Trending Papers
Jul 1

MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentation

Medical image segmentation relies on the ability of encoder-decoder architectures to translate rich feature representations into accurate pixel-level predictions under challenging conditions such as low contrast, structural ambiguity, and scale variability. While recent advances in large-scale pretraining and transformer-based encoders have substantially improved feature extraction, segmentation accuracy remains constrained by decoder design, particularly in terms of cross-scale alignment, contextual integration, and boundary preservation.

arXiv Computer Vision
Sep 22

MTMed3D: A Multi-Task Transformer-Based Model for 3D Medical Imaging

MTMed3D is a multi-task Transformer-based model that jointly performs 3D detection, segmentation, and classification in medical imaging. It uses a shared Transformer encoder to produce multi-scale features, with separate CNN decoders for each task. Evaluated on BraTS 2018 and 2019, it achieves strong results, especially in detection, while reducing computational cost and inference time compared to single-task models.

By Fan Li, Arun Iyengar, Lanyu Xu