# All Attention U-NET for Semantic Segmentation of Intracranial Hemorrhages In Head CT Images

Source: [All Attention U-NET for Semantic Segmentation of Intracranial Hemorrhages In Head CT Images](<https://www.sota2.com/research/paper/68fa71881e209131cea51d27>)

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

Data updated: 2025-10-26T02:08:33.784Z

Authors: Chia Shuo Chang, Tian Sheuan Chang, Jiun Lin Yan, Li Ko

Published: 2023-12-16T15:23:22.000Z

## Abstract

Intracranial hemorrhages in head CT scans serve as a first line tool to help specialists diagnose different types. However, their types have diverse shapes in the same type but similar confusing shape, size and location between types. To solve this problem, this paper proposes an all attention U-Net. It uses channel attentions in the U-Net encoder side to enhance class specific feature extraction, and space and channel attentions in the U-Net decoder side for more accurate shape extraction and type classification. The simulation results show up to a 31.8\\% improvement compared to baseline, ResNet50 + U-Net, and better performance than in cases with limited attention.

## 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/2312.10483>)

[PDF](<https://arxiv.org/pdf/2312.10483.pdf>)