https://www.scottpoulton.com/categories/research/ Recent content in Research on somewhere/nowhere Hugo -- gohugo.io en-us Tue, 07 Apr 2026 00:00:00 +0000 https://www.scottpoulton.com/posts/histohelper_case_study/ Tue, 07 Apr 2026 00:00:00 +0000 https://www.scottpoulton.com/posts/histohelper_case_study/ HistoHelper: WSI Inference & 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. https://www.scottpoulton.com/posts/project-log-005-formalising-architecture/ Wed, 01 Apr 2026 00:00:00 +0000 https://www.scottpoulton.com/posts/project-log-005-formalising-architecture/ Formalising the Architecture: ResNet and Predictive Morphology As HistoHelper shifts from a conceptual MVP into a structured research tool, it is necessary to formally ground the architecture in established literature. The primary objective remains unchanged: to determine whether a Convolutional Neural Network (CNN) can identify morphological signatures within primary melanoma Whole Slide Images (WSIs) that predict specific organ tropism (e.g., pulmonary versus cerebral metastasis). The Rationale for Residual Networks (ResNet) Training deep CNNs from scratch on gigapixel histological data is computationally prohibitive and highly prone to vanishing gradients. https://www.scottpoulton.com/posts/project-log-001-defining/ Wed, 25 Mar 2026 00:00:00 +0000 https://www.scottpoulton.com/posts/project-log-001-defining/ Defining HistoHelper AI The Genesis Every good tool starts with a problem that is too annoying to ignore. In clinical pathology, the problem is volume and visual fatigue. Pathologists are tasked with scanning gigapixel SVS (Whole Slide Images) to find microscopic anomalies. It is finding a needle in a digital haystack, and visual fatigue is a real threat to diagnostic accuracy. The Problem Beyond basic anomaly detection, there is a massive, unanswered question in oncology known as Organ Tropism.