# MixModule: Mixed CNN Kernel Module for Medical Image Segmentation

Source: [MixModule: Mixed CNN Kernel Module for Medical Image Segmentation](<https://www.sota2.com/research/paper/68fa67191e209131cea142a7>)

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

Data updated: 2025-10-25T22:06:19.934Z

Authors: Henry H. Yu, Xue Feng, Hao Sun, Ziwen Wang

Published: 2019-10-19T09:06:21.000Z

## Abstract

Convolutional neural networks (CNNs) have been successfully applied to medical image classification, segmentation, and related tasks. Among the many CNNs architectures, U-Net and its improved versions based are widely used and achieve state-of-the-art performance these years. These improved architectures focus on structural improvements and the size of the convolution kernel is generally fixed. In this paper, we propose a module that combines the benefits of multiple kernel sizes and we apply the proposed module to U-Net and its variants. We test our module on three segmentation benchmark datasets and experimental results show significant improvement.

## 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/1910.08728>)

[PDF](<https://arxiv.org/pdf/1910.08728.pdf>)