Regularization Strategy for Multi-organ Nucleus Segmentation with Localizable Features

被引：0

作者：

Traisuwan, Attasuntorn ^{[1
]}

Limsiroratana, Somchai ^{[1
]}

Phukpattaranont, Pornchai ^{[2
]}

Tandayya, Pichaya ^{[1
]}

机构：

[1] Prince Songkla Univ, Fac Engn, Dept Comp Engn, Hat Yai, Songkhla, Thailand

[2] Prince Songkla Univ, Fac Engn, Dept Elect Engn, Hat Yai, Songkhla, Thailand

来源：

2022 19TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (JCSSE 2022) | 2022年

关键词：

Nucleus segmentation; Deep Learning; Regularization Strategy; CutMix; Color Normalization;

D O I：

10.1109/JCSSE54890.2022.9836241

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Applying color normalization on H&E images is a famous protocol in digital pathology. Recently, the CutMix technique has a strong ability to generalize the classification models. In this paper, we propose the modified CutMix for segmentation tasks. We apply it to the MoNuSeg dataset. The U-Net with a MobileNet backbone is used for training and inferencing. Moreover, we compare it with the traditional color normalization. The results show that our modified CutMix outperformed color normalization with the +0.179 AJI score. It achieved the IoU score faster and got a higher AP for every IoU threshold.

引用

页数：6

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