For equitable deployment of AI tools in hospitals and healthcare facilities, we need Deep Segmentation Networks that offer high performance and can be trained on cost-effective GPUs with limited memory and large batch sizes. In this work, we propose Wave-GMS, a lightweight and efficient multi-scale generative model for medical image segmentation. Wave-GMS has a substantially smaller number of trainable parameters, does not require loading memory-intensive pretrained vision foundation models, and supports training with large batch sizes on GPUs with limited memory. We conducted extensive experiments on four publicly available datasets (BUS, BUSI, Kvasir-Instrument, and HAM10000), demonstrating that Wave-GMS achieves state-of-the-art segmentation performance with superior cross-domain generalizability, while requiring only ~2.6M trainable parameters. Code is available at https://github.com/ATPLab-LUMS/Wave-GMS.
U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
School of Biomedical Engineering, Division of Life Sciences；Medicine, University of Science；Technology of China (USTC), Hefei Anhui, 230026, China Center for Medical Imaging, Robotics, Analytic Computing \& Learning (MIRACLE), Suzhou Institute for Advance Research, USTC Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology, Suzhou State Key Laboratory of Precision
LV-UNet: A Lightweight and Vanilla Model for Medical Image Segmentation
College of Control Science and Engineering（控制科学与工程学院）；Zhejiang University（浙江大学）；College of Computer Science and Technology（计算机科学与技术学院）；Zhejiang University of Technology（浙江工业大学）；The First Clinical Medical College（第一临床医学院）；Guangdong Medical University（广东医科大学）
LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation
School of Computer Science, Peking University（北京大学计算机学院）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Key Laboratory of High Confidence Software Technologies, Ministry of Education（教育部高可信软件技术重点实验室）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）
MLRU++: Multiscale Lightweight Residual UNETR++ with Attention for Efficient 3D Medical Image Segmentation
AI Research Lab, Department of Computer Science（人工智能研究实验室、计算机科学系）；Biomedical & Translational Sciences, Sanford School of Medicine（生物医学与转化科学、桑福德医学院）；University Of South Dakota（南达科他大学）
Less is More: AMBER-AFNO -- a New Benchmark for Lightweight 3D Medical Image Segmentation
Department of Physics E. Pancini, University of Naples Federico II（那不勒斯费德里科二世大学物理系）；Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II（那不勒斯费德里科二世大学电气工程与信息技术系）
De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation
School of Computer Science, University of Nottingham Ningbo China（诺丁汉大学宁波校区计算机科学学院）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）；School of School of Engineering & Physical Sciences, University of Lincoln（林肯大学工程与物理科学学院）；Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港科学与创新研究院）；School of Computer Science, University of Nottingham（诺丁汉大学计算机科学学院）；The Hong Kong Polytechnic University（香港理工大学）
Physics-Inspired Generative Models in Medical Imaging: A Review
KTH Royal Institute of Technology（瑞典皇家理工学院）；Karolinska University Hospital（卡罗林斯卡大学医院）；University of Regensburg（雷根斯堡大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）；Rensselaer Polytechnic Institute（伦斯勒理工学院）
Duke University（杜克大学）；Vanderbilt University（范德堡大学）
Lesion detection in digital breast tomosynthesis (DBT) is an important and a challenging problem characterized by a low prevalence of images containing tumors. Due to the label scarcity problem, large deep learning models and computationally intensive algorithms are likely to fail when applied to this task. In this paper, we present a practical yet lightweight backbone to improve the accuracy of tumor detection. Specifically, we propose a novel modification of visual transformer (ViT) on image feature patches to connect the feature patches of a tumor with healthy backgrounds of breast images a…
Harnessing Lightweight Transformer with Contextual Synergic Enhancement for Efficient 3D Medical Image Segmentation
Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR（电子工程系，香港中文大学（深圳）Hong Kong SAR）；Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong SAR（人工智能与机器人中心（CAIR），香港科学与创新研究院，中国科学院，Hong Kong SAR）
DAUNet: A Lightweight UNet Variant with Deformable Convolutions and Parameter-Free Attention for Medical Image Segmentation
Department of Electrical Engineering (ISY), Information Coding (ICG), Linköping University（电气工程系（ISY）、信息编码系（ICG）、利厄普大学）；SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals（SDAIA-KFUPM人工智能联合研究中心、国王法赫德石油大学）；Department of Computer Engineering, College of Computing and Mathematics, King Fahd University of Petroleum & Minerals（计算机工程系、计算与数学学院、国王法赫德石油大学）
BiSe-Unet: A Lightweight Dual-path U-Net with Attention-refined Context for Real-time Medical Image Segmentation
Electrical and Computer Engineering, University of Central Florida（佛罗里达中央大学电气与计算机工程系）；Department of Medicine, University of Central Florida College of Medicine（佛罗里达中央大学医学院医学系）
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）
LGMSNet: Thinning a medical image segmentation model via dual-level multiscale fusion
School of Biomedical Engineering, Division of Life Sciences；Medicine, University of Science；Center for Medical Imaging, Robotics, Analytic Computing \& Learning (MIRACLE), Suzhou Institute for Advance Research, USTC, Suzhou, 215123, China；Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China；Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology, Suzhou, 215123, China；State Key Laboratory of Precision \& Intelligent Chemistry, USTC, Hefei, China
Simple is what you need for efficient and accurate medical image segmentation
Department of Ultrasound, Shenzhen Maternity and Child Healthcare Hospital, Women and Children's Medical Center, Southern Medical University, Shenzhen, Guangdong Province, China（超声科，深圳母婴保健医院，妇女儿童医疗中心，南方医科大学，深圳，广东省，中国）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing University of Posts and Telecommunications（北京邮电大学）；Shandong University of Science and Technology（山东科技大学）
Efficient medical image segmentation aims to provide accurate pixel-wise predictions for medical images with a lightweight implementation framework. However, lightweight frameworks generally fail to achieve superior performance and suffer from poor generalizable ability on cross-domain tasks. In this paper, we explore the generalizable knowledge distillation for the efficient segmentation of cross-domain medical images. Considering the domain gaps between different medical datasets, we propose the Model-Specific Alignment Networks (MSAN) to obtain the domain-invariant representations. Meanwhil…
Mobile U-ViT: Revisiting large kernel and U-shaped ViT for efficient medical image segmentation
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, USTC（苏州先进研究院）；Shanghai Jiao Tong University（上海交通大学）；Harbin Institute of Technology（哈尔滨工业大学）；State Grid Hunan ElectricPower Corporation Limited Research Institute（国网湖南电力有限公司研究院）
Tsinghua University（清华大学）；University of Southern California（南加州大学）；Hebei University of Engineering（河北工程大学）
Image segmentation is an important task in the medical image field and many convolutional neural networks (CNNs) based methods have been proposed, among which U-Net and its variants show promising performance. In this paper, we propose GP-module and GPU-Net based on U-Net, which can learn more diverse features by introducing Ghost module and atrous spatial pyramid pooling (ASPP). Our method achieves better performance with more than 4 times fewer parameters and 2 times fewer FLOPs, which provides a new potential direction for future research. Our plug-and-play module can also be applied to exi…
Department of Statistics and Applied Probability National University of Singapore（新加坡国立大学统计与应用概率系）
In recent years, convolutional neural networks (CNNs) have revolutionized medical image analysis. One of the most well-known CNN architectures in semantic segmentation is the U-net, which has achieved much success in several medical image segmentation applications. Also more recently, with the rise of autoML ad advancements in neural architecture search (NAS), methods like NAS-Unet have been proposed for NAS in medical image segmentation. In this paper, with inspiration from LadderNet, U-Net, autoML and NAS, we propose an ensemble deep neural network with an underlying U-Net framework consisti…
A Text-Image Fusion Method with Data Augmentation Capabilities for Referring Medical Image Segmentation
College of Information Science and Engineering（信息科学与工程学院）；Ritsumeikan University（立命馆大学）；Tiwaki Co., Ltd.（Tiwaki公司）；Dalian University of Technology（大连理工大学）；School of Software（软件学院）；Zhejiang University（浙江大学）
Federated learning (FL) allows multiple medical institutions to collaboratively learn a global model without centralizing client data. It is difficult, if possible at all, for such a global model to commonly achieve optimal performance for each individual client, due to the heterogeneity of medical images from various scanners and patient demographics. This problem becomes even more significant when deploying the global model to unseen clients outside the FL with unseen distributions not presented during federated training. To optimize the prediction accuracy of each individual client for medi…
TopoMamba: Topology-Aware Scanning and Fusion for Segmenting Heterogeneous Medical Visual Media
Dalian University of Technology（大连理工大学）；Guangdong University of Technology（广东工业大学）；Huizhou University（惠州市学院）；University of Macau（澳门大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Hong Kong Polytechnic University（香港理工大学）
Semantic image segmentation plays a pivotal role in many vision applications including autonomous driving and medical image analysis. Most of the former approaches move towards enhancing the performance in terms of accuracy with a little awareness of computational efficiency. In this paper, we introduce LiteSeg, a lightweight architecture for semantic image segmentation. In this work, we explore a new deeper version of Atrous Spatial Pyramid Pooling module (ASPP) and apply short and long residual connections, and depthwise separable convolution, resulting in a faster and efficient model. LiteS…
LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation
College of Electronic Information, Guangxi Minzu University（广西民族大学电子信息学院）；The First Affiliated Hospital of Sun Yat-Sen University（中山大学附属第一医院）；School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能、光学与电子学院（iOPEN））
MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation
Yale University（耶鲁大学）；University of Florida（佛罗里达大学）；Wichita State University（威奇托州立大学）；Purdue University（普渡大学）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Stevens Institute of Technology（史蒂文斯理工学院）；Zhejiang University（浙江大学）
MedVeriSeg: Teaching LISA-Like Medical Segmentation Models to Verify Query Validity Without Extra Training
School of Aeronautics and Astronautics, Zhejiang University（浙江大学航空宇航学院）；Southern Medical University（南方医科大学）；School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University（浙江科技学院计算机科学与技术学院（人工智能学院））；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）
