Thielecke, LarsRoehnert, Maximilian-Alexander2026-03-182026-03-1820252026-03-18https://opara.zih.tu-dresden.de/handle/123456789/2141https://doi.org/10.25532/OPARA-1134This repository provides a minimal, end‑to‑end example pipeline demonstrating how a trained ResNet‑34 model can recognize MRD‑associated patterns in UMAP embeddings derived from flow‑cytometry data of AML patients. ResNet‑based classifiers are widely used for image‑recognition tasks , making them well‑suited for distinguishing subtle MRD‑related patterns in UMAP‑transformed cytometry data. The pipeline consists of a small collection of Python and R scripts organized as a lightweight workflow, following the standard idea of pipelines as sequences of data‑processing and prediction steps . Included in the repository are: - a single example patient dataset (raw data, pre‑processed data, and generated image) - an R script for preprocessing the raw flow‑cytometry data - a Python script that converts the tabular preprocessed data into 2D images - the representative UMAP embedding needed for generating standardized 2D representations of patient-specific data - a Python script that loads the ResNet‑34 architecture (including the custom classifier head), initializes the trained weights , and runs the prediction procedure to distinguish MRD‑positive from MRD‑negative image patterns This example is intentionally minimal: it is not a production‑ready pipeline but an educational demonstration of how the core steps—data preparation, image generation, and model prediction—link together in a transparent, reproducible workflow.Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/2::22::205::205-142::22::205::205-07