Optimal Transport and Contrastive-Based Clustering for Annotation-Free Tissue Analysis in Histopathology Images-Reference-Cited by-同舟云学术

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Optimal Transport and Contrastive-Based Clustering for Annotation-Free Tissue Analysis in Histopathology Images

Published:2023-12-15 Issue: Volume:abs/1702.05747 Page:301-307
ISSN:
Container-title:2023 International Conference on Machine Learning and Applications (ICMLA)
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Author:

Aburidi Mohammed¹,Marcia Roummel¹

Affiliation:

1. University of California Merced,Department of Applied Mathematics,Merced,USA

Funder

NSF

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10459339/10459727/10459798.pdf?arnumber=10459798

Reference39 articles.

1. A Survey on Deep Learning in Medical Image Analysis;Geert,2017

2. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study

3. Artificial intelligence and digital pathology: Challenges and opportunities

4. An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies

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1. Topological Adversarial Attacks on Graph Neural Networks Via Projected Meta Learning;2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS);2024-05-23

2. Wasserstein Distance-Based Graph Kernel for Enhancing Drug Safety and Efficacy Prediction *;2024 IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC);2024-02-05

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