State-of-the-art models for medical image segmentation achieve excellent accuracy but require substantial computational resources, limiting deployment in resource-constrained clinical settings. We present SegMate, an efficient 2.5D framework that achieves state-of-the-art accuracy, while considerably reducing computational requirements. Our efficient design is the result of meticulously integrating asymmetric architectures, attention mechanisms, multi-scale feature fusion, slice-based positional conditioning, and multi-task optimization. We demonstrate the efficiency-accuracy trade-off of our framework across three modern backbones (EfficientNetV2-M, MambaOut-Tiny, FastViT-T12). We perform experiments on three datasets: TotalSegmentator, SegTHOR and AMOS22. Compared with the vanilla models, SegMate reduces computation (GFLOPs) by up to 2.5x and memory footprint (VRAM) by up to 2.1x, while generally registering performance gains of around 1%. On TotalSegmentator, we achieve a Dice score of 93.51% with only 295MB peak GPU memory. Zero-shot cross-dataset evaluations on SegTHOR and AMOS22 demonstrate strong generalization, with Dice scores of up to 86.85% and 89.35%, respectively. We release our open-source code at https://github.com/andreibunea99/SegMate.
LUCF-Net: Lightweight U-shaped Cascade Fusion Network for Medical Image Segmentation
Hangzhou Dianzi University（杭州电子科技大学）；International Joint Research Laboratory for Autonomous Robotic Systems（自主机器人系统国际联合研究实验室）；University of Macau（澳门大学）；University of Miami（迈阿密大学）
SpaFactor: Lightweight Spatial Context-Aware Gene Program Modeling for Histology-to-Transcriptomics Inference
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Fuzhou University（福州大学）；Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University（清华大学附属北京清华长庚医院，清华大学临床医学院）；Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院 北京协和医学院）
Lightweight Method for Interactive 3D Medical Image Segmentation with Multi-Round Result Fusion
Aerospace Center Hospital（航天中心医院）；Yizhun Medical AI Co., Ltd（医准智能医疗科技有限公司）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Southern University of Science and Technology（南方科技大学）；Beijing Institute of Technology（北京理工大学）
DMC-Net: Lightweight Dynamic Multi-Scale and Multi-Resolution Convolution Network for Pancreas Segmentation in CT Images
Mallinckrodt Institute of Radiology（马林克罗特放射学研究所）；Institute for Informatics, Data Science and Biostatistics（信息学、数据科学与生物统计学研究所）；Washington University School of Medicine（华盛顿大学医学院）
TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration
Department of Imaging, Radboudumc, Nijmegen, the Netherlands（影像部，Radboudumc，尼姆egen，荷兰）；Institute for Medical Informatics, University of Lübeck, Lübeck, Germany（医学信息研究所，吕贝克大学，吕贝克，德国）；Department of Nuclear Medicine, Hospital 108, Hanoi, Vietnam（核医学部，108医院，河内，越南）；Department of Diagnostic Imaging and Interventional Radiology, Thai Nguyen National Hospital, Thai Nguyen, Vietnam（诊断影像和介入放射科，Thai Nguyen国家医院，Thai Nguyen，越南）；Interventional Radiology Center, Tam Anh Hospital, Hanoi, Vietnam（介入放射科中心，Tam Anh医院，河内，越南）；FET, Vietnam National University, University of Engineering and Technology, Hanoi, Vietnam（FET，越南国家大学，工程与技术大学，河内，越南）
PromptForSegCXR: Prompt-Driven Multi-Organ and Multi-Disease Segmentation in Chest X-rays using a Multi-stage Fusion Mechanism
1 ,5Department of Computer Science \& Engineering,\ University of Engineering \& Technology, Rajshahi-6204, Bangladesh；2 Department of Electrical；Electronic Engineering,\ University of Engineering；Bachelor of Surgery,\ Suhrawardy Medical College , Sher-E-Bangla Nagar, Dhaka-1207；6 Department of Biomedical Engineering,\ University of Engineering
Skin cancer is one of the deadly types of cancer and is common in the world. Recently, there has been a huge jump in the rate of people getting skin cancer. For this reason, the number of studies on skin cancer classification with deep learning are increasing day by day. For the growth of work in this area, the International Skin Imaging Collaboration (ISIC) organization was established and they created an open dataset archive. In this study, images were taken from ISIC 2017 Challenge. The skin cancer images taken were preprocessed and data augmented. Later, these images were trained with tran…
Beijing University of Posts and Telecommunications（北京邮电大学）；Guilin University of Electronic Technology（桂林电子科技大学）
Abdominal organ segmentation has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis. However, manually annotating organs from CT scans is time-consuming and labor-intensive. Semi-supervised learning has shown the potential to alleviate this challenge by learning from a large set of unlabeled images and limited labeled samples. In this work, we follow the self-training strategy and employ a high-performance hybrid architecture (PHTrans) consisting of CNN and Swin Transformer for the teacher model to generate precise pseudo labels for unl…
Multi-planar 2D-U-Net Segmentation of 3D-CT Abdominal Organs augmented by Spatial Occurrence Maps
Glasgow Caledonian University School of Science & Engineering（格拉斯哥卡里多尼亚大学科学与工程学院）；Jade University of Applied Sciences Department of Engineering & Medical Technology（雅德应用科学大学工程与医疗技术系）
MedFG-VQA: Low-Frequency Memory and Graph Attention for Lightweight Medical VQA
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；State Key Laboratory of Intelligent Manufacturing of Advanced Construction Machinery（高端工程机械智能制造国家重点实验室）；Department of Electrical and Computer Engineering, Sungkyunkwan University（成均馆大学电气与计算机工程系）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）
Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography
City University of Hong Kong（香港城市大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of California, San Francisco（加州大学旧金山分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Chinese University of Hong Kong（香港中文大学）；NVIDIA（英伟达）
