ResNet to the ResCue: An automated approach for the detection of measurable residual disease in patients with acute myeloid leukemia.

Abstract

This 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.