Improving Automatic Segmentation of lymphoma with Additional Medical Knowledge Priors-Reference-Cited by-同舟云学术

Improving Automatic Segmentation of lymphoma with Additional Medical Knowledge Priors

Published:2023-04-18 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
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Author:

Liu Liangchen¹,Hou Benjamin¹,Nag Manas Kumar¹,Liu Jianfei¹,Hasani Navid¹,Shen Thomas C.¹,Zhu Yingying²,Saboury Babak¹,Xiao Jing³,Summers Ronald M.¹

Affiliation:

1. National Institutes of Health,Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center,Bethesda,MD,USA

2. The University of Texas at Arlinton,Department of Computer Science and Engineering,Arlington,TX,USA

3. Ping An Technology,Shenzhen,China

Funder

Health

Research and Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10230311/10230322/10230634.pdf?arnumber=10230634

Reference19 articles.

1. A large annotated medical image dataset for the development and evaluation of segmentation algorithms;simpson,2019

2. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

3. Primary extra nodal Non-Hodgkin's lymphoma of urinary bladder presenting as a bladder tumor: A case report

4. Lymphoma Involving the Heart: A Case Report

5. Improved multi-modal patch based lymphoma segmentation with negative sample augmentation and label guidance on pet/ct scans;et al;International Conference on Medical Image Computing and Computer Assisted Intervention Workshop on Multi-scale Multimodal Medical Imaging,2022

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