We have located links that may give you full text access.
Unsupervised Learning For Cell-level Visual Representation with Generative Adversarial Networks.
The visual attributes of cells, such as the nuclear morphology and chromatin openness, are critical for histopathology image analysis. By learning cell-level visual representation, we can obtain a rich mix of features that are highly reusable for various tasks, such as cell-level classification, nuclei segmentation, and cell counting. In this paper, we propose a unified generative adversarial networks architecture with a new formulation of loss to perform robust cell-level visual representation learning in an unsupervised setting. Our model is not only label-free and easily trained but also capable of cell-level unsupervised classification with interpretable visualization, which achieves promising results in the unsupervised classification of bone marrow cellular components. Based on the proposed cell-level visual representation learning, we further develop a pipeline that exploits the varieties of cellular elements to perform histopathology image classification, the advantages of which are demonstrated on bone marrow datasets.
Full text links
Related Resources
Trending Papers
Executive Summary: State-of-the-Art Review: Unintended Consequences: Risk of Opportunistic Infections Associated with Long-term Glucocorticoid Therapies in Adults.Clinical Infectious Diseases 2024 April 11
Autoimmune Hemolytic Anemias: Classifications, Pathophysiology, Diagnoses and Management.International Journal of Molecular Sciences 2024 April 13
Clinical practice guidelines on the management of status epilepticus in adults: A systematic review.Epilepsia 2024 April 13
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app