# Train, Learn, Expand, Repeat

Source: [Train, Learn, Expand, Repeat](<https://www.sota2.com/research/paper/68fa675a1e209131cea15c42>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:12:13.696Z

Authors: Abhijeet Parida, Aadhithya Sankar, Rami Eisawy, Tom Finck, Benedikt Wiestler, Franz Pfister, Julia Moosbauer

Published: 2020-03-18T20:55:38.000Z

## Abstract

High-quality labeled data is essential to successfully train supervised machine learning models. Although a large amount of unlabeled data is present in the medical domain, labeling poses a major challenge: medical professionals who can expertly label the data are a scarce and expensive resource. Making matters worse, voxel-wise delineation of data (e.g. for segmentation tasks) is tedious and suffers from high inter-rater variance, thus dramatically limiting available training data. We propose a recursive training strategy to perform the task of semantic segmentation given only very few training samples with pixel-level annotations. We expand on this small training set having cheaper image-level annotations using a recursive training strategy. We apply this technique on the segmentation of intracranial hemorrhage (ICH) in CT (computed tomography) scans of the brain, where typically few annotated data is available.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2003.08469>)

[PDF](<https://arxiv.org/pdf/2003.08469.pdf>)