# Relevance analysis of MRI sequences for automatic liver tumor segmentation

Source: [Relevance analysis of MRI sequences for automatic liver tumor segmentation](<https://www.sota2.com/research/paper/68fa7f461e209131cea99bcf>)

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

Data updated: 2025-10-26T06:47:46.360Z

Authors: Grzegorz Chlebus, Nasreddin Abolmaali, Andrea Schenk, Hans Meine

Published: 2019-07-26T19:47:47.000Z

## Abstract

Explainability of decisions made by deep neural networks is of high value as it allows for validation and improvement of models. This work proposes an approach to explain semantic segmentation networks by means of layer-wise relevance propagation. As an exemplary application, we investigate which MRI sequences are most relevant for liver tumor segmentation.

## 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/1907.11773>)

[PDF](<https://arxiv.org/pdf/1907.11773.pdf>)