{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-scale-fully-convolutional-neural","title":"Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecture","arxiv_id":"1909.10726","date":"2019-09-24","proceeding":null,"authors":["Rüdiger Schmitz","Frederic Madesta","Maximilian Nielsen","Jenny Krause","René Werner","Thomas Rösch"],"abstract":"Histopathologic diagnosis relies on simultaneous integration of information from a broad range of scales, ranging from nuclear aberrations ($\\approx \\mathcal{O}(0.1{\\mu m})$) through cellular structures ($\\approx \\mathcal{O}(10{\\mu m})$) to the global tissue architecture ($\\gtrapprox \\mathcal{O}(1{mm})$). To explicitly mimic how human pathologists combine multi-scale information, we introduce a family of multi-encoder FCNs with deep fusion. We present a simple block for merging model paths with differing spatial scales in a spatial relationship-preserving fashion, which can readily be included in standard encoder-decoder networks. Additionally, a context classification gate block is proposed as an alternative for the incorporation of global context. Our experiments were performed on three publicly available whole-slide images of recent challenges (PAIP 2019, BACH 2020, CAMELYON 2016). The multi-scale architectures consistently outperformed the baseline single-scale U-Nets by a large margin. They benefit from local as well as global context and particularly a combination of both. If feature maps from different scales are fused, doing so in a manner preserving spatial relationships was found to be beneficial. Deep guidance by a context classification loss appeared to improve model training at low computational costs. All multi-scale models had a reduced GPU memory footprint compared to ensembles of individual U-Nets trained on different image scales. Additional path fusions were shown to be possible at low computational cost, opening up possibilities for further, systematic and task-specific architecture optimization. The findings demonstrate the potential of the presented family of human-inspired, end-to-end trainable, multi-scale multi-encoder FCNs to improve deep histopathologic diagnosis by extensive integration of largely different spatial scales.","url_abs":"https://arxiv.org/abs/1909.10726v3","url_pdf":"https://arxiv.org/pdf/1909.10726v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-scale-fully-convolutional-neural","repo_url":"https://github.com/IPMI-ICNS-UKE/multiscale","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"whole-slide-images","task_name":"whole slide images"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1909.10726","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}