Flemme: A Flexible and Modular Learning Platform for Medical Images

[2408.09369] Flemme: A Flexible and Modular Learning Platform for Medical Images Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Image and Video Processing arXiv:2408.09369 (eess) [Submitted on 18 Aug 2024 ( v1 ), last revised 9 Apr 2026 (this version, v3)] Title: Flemme: A Flexible and Modular Learning Platform for Medical Images Authors: Guoqing Zhang , Jingyun Yang , Yang Li View a PDF of the paper titled Flemme: A Flexible and Modular Learning Platform for Medical Images, by Guoqing Zhang and Jingyun Yang and Yang Li View PDF HTML (experimental) Abstract: As the rapid development of computer vision and the emergence of powerful network backbones and architectures, the application of deep learning in medical imaging has become increasingly significant. Unlike natural images, medical images lack huge volumes of data but feature more modalities, making it difficult to train a general model that has satisfactory performance across various datasets. In practice, practitioners often suffer from manually creating and testing models combining independent backbones and architectures, which is a laborious and time-consuming process. We propose Flemme, a FLExible and Modular learning platform for MEdical images. Our platform separates encoders from the model architectures so that different models can be constructed via various combinations of supported encoders and architectures. We construct encoders using building blocks based on convolution, transformer, and state-space model (SSM) to process both 2D and 3D image patches. A base architecture is implemented following an encoder-decoder style, with several derived architectures for image segmentation, reconstruction, and generation tasks. In addition, we propose a general hierarchical architecture incorporating a pyramid loss to optimize and fuse vertical features. Experiments demonstrate that this simple design leads to an average improvement of 5.60% in Dice score and 7.81% in mean interaction of units (mIoU) for segmentation models, as well as an enhancement of 5.57% in peak signal-to-noise ratio (PSNR) and 8.22% in structural similarity (SSIM) for reconstruction models. We further utilize Flemme as an analytical tool to assess the effectiveness and efficiency of various encoders across different tasks. Code is available at this https URL . Comments: 8 pages, 6 figures Subjects: Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2408.09369 [eess.IV] (or arXiv:2408.09369v3 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2408.09369 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Guoqing Zhang [ view email ] [v1] Sun, 18 Aug 2024 05:47:33 UTC (36,654 KB) [v2] Tue, 7 Jan 2025 01:23:54 UTC (36,654 KB) [v3] Thu, 9 Apr 2026 10:01:38 UTC (36,656 KB) Full-text links: Access Paper: View a PDF of the paper titled Flemme: A Flexible and Modular Learning Platform for Medical Images, by Guoqing Zhang and Jingyun Yang and Yang Li View PDF HTML (experimental) TeX Source view license Current browse context: eess.IV < prev | next > new | recent | 2024-08 Change to browse by: cs cs.CV eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• DINO-Med: A Unified Patch-Based Adaptation Framework for Multi-Modal Medical Image Analysis Applied to Liver Fibrosis Staging#998913
• UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation#1141254
• FreqDINO++: A Frequency-Guided Multi-Task Routing Vision Foundation Model for Universal Ultrasound Analysis#1034104
• Hierarchical Barycentric Multimodal Representation Learning for Medical Image Analysis#157804
• Hierarchical Barycentric Multimodal Representation Learning for Medical Image Analysis#154940