{"repo":"AmirhosseinHonardoust/Tumor-Doppelganger-Studio","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Tumor-Doppelganger-Studio","clone":"git clone https://github.com/AmirhosseinHonardoust/Tumor-Doppelganger-Studio.git","description":"Similarity-first interpretability studio for breast tumor samples: pick a case, find its closest “twins” (benign/malignant look-alikes), visualize neighborhood structure, compare feature fingerprints, and run minimal-change counterfactual edits toward a target class. Educational demo only, not for diagnosis.","language":"Python","stars":16,"topics":["breast-cancer","case-based-reasoning","classification","counterfactuals","data-visualization","educational-demo","explainable-ai","exploratory-data-analysis","feature-attribution","healthcare-analytics"],"license":"MIT","category":"analytics","readme_excerpt":"Tumor Doppelgänger Studio Similarity-first interpretability: find a case’s closest look-alikes (“twins”) and explain why. Educational demo only, Not a medical device. --- Medical disclaimer (read first) This project is an educational visualization built on a public dataset. It is NOT intended for diagnosis, treatment, screening, triage, or clinical decision-making. Never use this app to make medical decisions. If need medical support, consult qualified healthcare professionals. --- Why this project is different Most ML demos do this: “Predict malignant vs benign.” This project does something more interpretable : “Show me the closest look-alike cases (twins) and explain the similarity structure around a case.” Instead of pretending the model “knows,” the app reveals neighborhood evidence : - Who does this case resemble? - How consistent is the neighborhood? - Which features make the case look like its twins? - Is the case sitting near a boundary between groups? - What minimal feature shifts would move it closer to another neighborhood? (educational geometry, not medical advice) --- What the app does (high-level) For a selected case (row): 1. Standardizes features to make distances meaningful across mixed units. 2. Builds a k-nearest neighbor (kNN) neighborhood (the “twins”). 3. Shows neighborhood composition ( how many benign vs malignant twins ). 4. Explains similarity by highlighting the largest feature differences (“drivers”). 5. Provides multiple interpretability views: - ","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Tumor-Doppelganger-Studio/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}