Predicting clinical endpoints and visual changes with quality-weighted tissue-based renal histological features

Predicting clinical endpoints and visual changes with quality-weighted tissue-based renal histological features

Two common obstacles limiting the performance of data-driven algorithms in digital histopathology classification tasks are the lack of expert annotations and the narrow diversity of datasets. Multi-instance learning (MIL) can address the former challenge for the analysis of whole slide images (WSI),...

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Bibliographic Details
Main Authors: Ka Ho Tam, Maria F. Soares, Jesper Kers, Edward J. Sharples, Rutger J. Ploeg, Maria Kaisar, Jens Rittscher
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-04-01
Series:Frontiers in Transplantation
Subjects:
digital histopathology
kidney transplant
multi-instance learning
Bayesian Neural Network (BNN)
computer vision
Online Access:https://www.frontiersin.org/articles/10.3389/frtra.2024.1305468/full

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https://www.frontiersin.org/articles/10.3389/frtra.2024.1305468/full

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