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Resolution-based distillation for efficient histology image classification

DiPalma, Joseph, Suriawinata, Arief A., Tafe, Laura J., Torresani, Lorenzo, Hassanpour, Saeed·2021·Artificial Intelligence in Medicine
DOI 10.1016/j.artmed.2021.102136PMC PMC8449014
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  • These studies addressed a five-category classification task-distinguishing lepidic, acinar, papillary, micropapillary, and solid growth patterns-and showed that AI can support accurate subtype identification and deeper characterization of tumor morphology.

    “These studies addressed a complex five-category classification task—distinguishing lepidic, acinar, papillary, micropapillary, and solid growth patterns—and showed that AI can support both accurate subtype identification and a deeper characterization of tumor morphology.”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [56]

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  • Several investigations show that deep learning techniques can assist in detailed histologic classification.

    “Several investigations underline how deep learning techniques can assist in detailed histologic classification [55,56].”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [56]

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Cited by

Sorted by citations

Authors
DiPalma, J., Suriawinata, A. A., Tafe, L. J., Torresani, L., & Hassanpour, S.
Venue
Artificial Intelligence in Medicine
Publisher
Elsevier BV
Published
2021-09
Volume
119
Pages
102136
ISSN
0933-3657
DOI
10.1016/j.artmed.2021.102136
PMC
PMC8449014
Cited by
38
References
5
Language
en
Licence
Funders
  • National Cancer Institute (R01CA249758)
  • U.S. National Library of Medicine (R01LM012837)

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