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Erschienen in:
A Data Augmentation Based ViT for Fine-Grained Visual Classification Kapitel lesen Erstes Kapitel lesen

2023 | OriginalPaper | Buchkapitel

Medical Image Segmentation and Saliency Detection Through a Novel Color Contextual Extractor

verfasst von : Xiaogen Zhou, Zhiqiang Li, Tong Tong

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2023

Verlag: Springer Nature Switzerland

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Abstract

Image segmentation is a critical step in computer-aided system diagnosis. However, many existing segmentation methods are designed for single-task driven segmentation, ignoring the potential benefits of incorporating multi-task methods, such as salient object detection (SOD) and image segmentation. In this paper, we propose a novel dual-task framework for the detection and segmentation of white blood cells and skin lesions. Our method comprises three main components: hair removal preprocessing for skin lesion images, a novel color contextual extractor (CCE) module for the SOD task, and an improved adaptive threshold (AT) paradigm for the image segmentation task. We evaluate the effectiveness of our proposed method on three medical image datasets, demonstrating superior performance compared to representative approaches.

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Vorheriges Kapitel MC-MLP: A Multiple Coordinate Frames MLP-Like Architecture for Vision
Nächstes Kapitel MedNet: A Dual-Copy Mechanism for Medical Report Generation from Images
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Metadaten
Titel
Medical Image Segmentation and Saliency Detection Through a Novel Color Contextual Extractor
verfasst von
Xiaogen Zhou
Zhiqiang Li
Tong Tong
Copyright-Jahr
2023
Buch
Artificial Neural Networks and Machine Learning – ICANN 2023

Print ISBN: 978-3-031-44209-4

Electronic ISBN: 978-3-031-44210-0

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-3-031-44210-0_37

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