Uncertainty-based Model Acceleration for Cancer Classification in Whole-Slide Images-Reference-Cited by-同舟云学术

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Uncertainty-based Model Acceleration for Cancer Classification in Whole-Slide Images

Published:2022-12-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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Author:

Gao Zeyu¹,Mao Anyu¹,Wu Jialun¹,Li Yang¹,Wang Chunbao²,Ding Caixia³,Gong Tieliang¹,Li Chen¹

Affiliation:

1. Xi’an Jiaotong University,School of Computer Science and Technology,Xi’an,China

2. the First Affiliated Hospital of Xi’an Jiaotong University,Department of Pathology,Xi’an,China

3. Shaanxi Provincial Cancer Hospital,Department of Pathology,Xi’an,China

Funder

National Natural Science Foundation of China

Research and Development

Chinese Academy of Engineering

Ministry of Education

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9994793/9994847/09995601.pdf?arnumber=9995601

Reference18 articles.

1. Next generation diagnostic pathology: use of digital pathology and artificial intelligence tools to augment a pathological diagnosis

2. Artificial intelligence and computational pathology

3. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

4. Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images

5. Data-efficient and weakly supervised computational pathology on whole-slide images

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