Extract, detect, eliminate: Enhancing reliability and performance of computational pathology through artifact processing pipelines
Author:
Funder
EU Framework Programme for Research and Innovation Marie Skłodowska-Curie Actions
Publisher
Elsevier BV
Reference11 articles.
1. The devil is in the details: whole slide image acquisition and processing for artifacts detection, color variation, and data augmentation: a review;Kanwal;IEEE Access,2022
2. HistoQC: an open-source quality control tool for digital pathology slides;Janowczyk;JCO Clin. Cancer Inform.,2019
3. Quality control stress test for deep learning-based diagnostic model in digital pathology;Schömig-Markiefka;Mod. Pathol.,2021
4. Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images;Kanwal;Comput. Med. Imaging Graph.,2023
5. A TV-based image processing framework for blind color deconvolution and classification of histological images;Pérez-Bueno;Digit. Signal Process.,2020
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs;2024-03-13
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