Lesion annotation network (LesaNet)

最新推荐文章于 2024-06-10 09:11:21 发布

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最新推荐文章于 2024-06-10 09:11:21 发布 · 831 阅读

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LesaNet是一个多标签分类CNN，用于预测CT图像中病变的身体部位、类型和属性。利用放射学报告和标签本体学习，提高预测准确性。适用于病变检测、检索和基于内容的图像检索。

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本文再一次初步翻译这篇论文，可以对医学的病灶区域进行检测和检索的一篇最新的paper

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Hypodense（低密度） <——>Hyperdense（高密度）
论文：https://arxiv.org/abs/1904.04661
代码：https://github.com/phoebe0803/CADLab/tree/master/LesaNet

This project contains the code and labels of the CVPR 2019 oral paper: “Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning from Radiology Reports and Label Ontology” (arXiv).
Developed by Ke Yan (yankethu@gmail.com, yanke23.com), Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, National Institutes of Health Clinical Center.
LesaNet [1] predicts the body part, type, and attributes of a variety of lesions in CT images. It is a multi-label classification CNN. It learns to annotate lesion images by leveraging radiology reports and the relations between labels (ontology).
You can use LesaNet to:

Given a lesion image patch, predict the lesion’s body part, type, and attributes;

Given a label (e.g., kidney) fin