PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization

[2503.24135] PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2503.24135 (cs) [Submitted on 31 Mar 2025 ( v1 ), last revised 14 Apr 2026 (this version, v4)] Title: PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization Authors: Alexis Guichemerre , Soufiane Belharbi , Mohammadhadi Shateri , Luke McCaffrey , Eric Granger View a PDF of the paper titled PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization, by Alexis Guichemerre and 4 other authors View PDF Abstract: Weakly supervised object localization (WSOL) methods allow training models to classify images and localize ROIs. WSOL only requires low-cost image-class annotations yet provides a visually interpretable classifier. Standard WSOL methods rely on class activation mapping (CAM) methods to produce spatial localization maps according to a single- or two-step strategy. While both strategies have made significant progress, they still face several limitations with histology images. Single-step methods can easily result in under- or over-activation due to the limited visual ROI saliency in histology images and scarce localization cues. They also face the well-known issue of asynchronous convergence between classification and localization tasks. The two-step approach is sub-optimal because it is constrained to a frozen classifier, limiting the capacity for localization. Moreover, these methods also struggle when applied to out-of-distribution (OOD) datasets. In this paper, a multi-task approach for WSOL is introduced for simultaneous training of both tasks to address the asynchronous convergence problem. In particular, localization is performed in the pixel-feature space of an image encoder that is shared with classification. This allows learning discriminant features and accurate delineation of foreground/background regions to support ROI localization and image classification. We propose PixelCAM, a cost-effective foreground/background pixel-wise classifier in the pixel-feature space that allows for spatial object localization. Using partial-cross entropy, PixelCAM is trained using pixel pseudo-labels collected from a pretrained WSOL model. Both image and pixel-wise classifiers are trained simultaneously using standard gradient descent. In addition, our pixel classifier can easily be integrated into CNN- and transformer-based architectures without any modifications. Comments: 43 pages, 24 figures, Medical Imaging with Deep Learning (MIDL 2025) Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2503.24135 [cs.CV] (or arXiv:2503.24135v4 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2503.24135 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Alexis Guichemerre [ view email ] [v1] Mon, 31 Mar 2025 14:18:01 UTC (40,048 KB) [v2] Tue, 3 Jun 2025 03:34:02 UTC (35,879 KB) [v3] Wed, 8 Apr 2026 21:15:01 UTC (35,890 KB) [v4] Tue, 14 Apr 2026 07:18:08 UTC (35,891 KB) Full-text links: Access Paper: View a PDF of the paper titled PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization, by Alexis Guichemerre and 4 other authors View PDF TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2025-03 Change to browse by: cs 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

• Computationally Efficient Pathology Segmentation using Knowledge Distillation from Foundation Models#926809
• Unsupervised domain adaptation in biomedical images segmentation with guided diffusion generative prior#257638
• Aberration-free volumetric high-speed imaging of in vivo retina#978618
• A YOLO-based learning lesion classifier of pre-exposure scan in digital breast tomosynthesis#411953
• Histology-informed spatial domain identification through multi-view graph convolutional networks#248074