Histokt: Cross Knowledge Transfer in Computational Pathology Article Swipe
Ryan Zhang
,
Jiadai Zhu
,
Stephen C. Yang
,
Mahdi S. Hosseini
,
Angelo Genovese
,
Lina Chen
,
Corwyn Rowsell
,
Savvas Damaskinos
,
Sonal Varma
,
Konstantinos N. Plataniotis
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/icassp43922.2022.9747400
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/icassp43922.2022.9747400
The lack of well-annotated datasets in computational pathology (CPath) obstructs the application of deep learning techniques for classifying medical images. Many CPath workflows involve transferring learned knowledge between various image domains through transfer learning. Currently, most transfer learning research follows a model-centric approach, tuning network parameters to improve transfer results over few datasets. In this paper, we take a data-centric approach to the transfer learning problem and examine the existence of generalizable knowledge between histopathological datasets. First, we create a standardization workflow for aggregating existing histopathological data. We then measure inter-domain knowledge by training ResNet18 models across multiple histopathological datasets, and cross-transferring between them to determine the quantity and quality of innate shared knowledge. Additionally, we use weight distillation to share knowledge between models without additional training. We find that hard to learn, multi-class datasets benefit most from pretraining, and a two stage learning framework incorporating a large source domain such as ImageNet allows for better utilization of smaller datasets. Furthermore, we find that weight distillation enables models trained on purely histopathological features to outperform models using external natural image data.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/icassp43922.2022.9747400
- OA Status
- green
- Cited By
- 9
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221150821
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4221150821Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/icassp43922.2022.9747400Digital Object Identifier
- Title
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Histokt: Cross Knowledge Transfer in Computational PathologyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-04-27Full publication date if available
- Authors
-
Ryan Zhang, Jiadai Zhu, Stephen C. Yang, Mahdi S. Hosseini, Angelo Genovese, Lina Chen, Corwyn Rowsell, Savvas Damaskinos, Sonal Varma, Konstantinos N. PlataniotisList of authors in order
- Landing page
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https://doi.org/10.1109/icassp43922.2022.9747400Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://hdl.handle.net/2434/929747Direct OA link when available
- Concepts
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Computer science, Transfer of learning, Workflow, Artificial intelligence, Domain (mathematical analysis), Standardization, Machine learning, Knowledge transfer, Deep learning, Domain knowledge, Data mining, Database, Knowledge management, Operating system, Mathematics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 1, 2024: 2, 2023: 5, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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