A Weakly Supervised Deep Learning Framework for Whole Slide Classification to Facilitate Digital Pathology in Animal Study-Reference-Cited by-同舟云学术

A Weakly Supervised Deep Learning Framework for Whole Slide Classification to Facilitate Digital Pathology in Animal Study

Published:2023-08-04 Issue:8 Volume:36 Page:1321-1331
ISSN:0893-228X
Container-title:Chemical Research in Toxicology
language:en
Short-container-title:Chem. Res. Toxicol.

Author:

Bussola Nicole¹,Xu Joshua²,Wu Leihong²^ORCID,Gorini Lorenzo³,Zhang Yifan²,Furlanello Cesare³,Tong Weida²^ORCID

Affiliation:

1. Center for Integrative Biology, University of Trento, Trento 38123, Italy

2. Division of Bioinformatics and Biostatistics National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079, United States

3. HK3 Lab, Rovereto 38068, Italy

Publisher

American Chemical Society (ACS)

Subject

Toxicology,General Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.3c00058

Reference37 articles.

1. Animal models are essential to biological research: issues and perspectives

2. Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

3. The Pathologists' Conundrum

4. Examination of Low ERBB2 Protein Expression in Breast Cancer Tissue

5. National Toxicology Program Position Statement on Informed (“Nonblinded”) Analysis in Toxicologic Pathology Evaluation

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