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    <title>Pathology on somewhere/nowhere</title>
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      <title>HistoHelper</title>
      <link>https://www.scottpoulton.com/posts/histohelper_case_study/</link>
      <pubDate>Tue, 07 Apr 2026 00:00:00 +0000</pubDate>
      
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      <description>HistoHelper: WSI Inference &amp;amp; Predictive Morphology 1. Tl;dr HistoHelper is an active research prototype exploring predictive morphology in oncology. The objective is to evaluate whether a Convolutional Neural Network can identify morphological signatures within primary skin lesions to predict specific organ tropism (e.g., pulmonary vs. cerebral metastasis).
Frontend Interface: FastAPI, Uvicorn, Docker, Google Cloud Run Machine Learning Pipeline: PyTorch (MPS Hardware Accelerated), OpenCV, OpenSlide Model Architecture: ResNet18 (Feature Extraction) transitioning to Multiple Instance Learning.</description>
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      <title>Project Log 2</title>
      <link>https://www.scottpoulton.com/posts/project-log-002-histohelper/</link>
      <pubDate>Wed, 25 Mar 2026 00:00:00 +0000</pubDate>
      
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      <description>The Needle in the Gigapixel Haystack I am officially starting work on HistoHelper.
If you have read my About page, you know why I am in Medical Laboratory Science. I am here to understand the mechanics of disease, specifically melanoma, from the ground up. But understanding the theory is only half the battle. The other half is building tools that actually do something useful with that theory.
The Problem with SVS Files In digital pathology, tissue biopsies are scanned into Whole Slide Images (WSIs), usually saved as .</description>
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