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Predicting Ovarian Cancer Treatment Response in Histopathology using Hierarchical Vision Transformers and Multiple Instance Learning.

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Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images., , , , и . CoRR, (2021)A Multi-level Approach for Identifying Process Change in Cancer Pathways., , , , , и . Business Process Management Workshops, том 362 из Lecture Notes in Business Information Processing, стр. 595-607. Springer, (2019)Assessing Domain Adaptation Techniques for Mitosis Detection in Multi-scanner Breast Cancer Histopathology Images., , , и . MIDOG/MOOD/Learn2Reg@MICCAI, том 13166 из Lecture Notes in Computer Science, стр. 14-22. Springer, (2021)AuguR: A Scalable Open-Source Interactive Web Application for Routinely Collected Data., , , , и . MedInfo, том 290 из Studies in Health Technology and Informatics, стр. 744-747. IOS Press, (2021)The Pitfalls of Using Open Data to Develop Deep Learning Solutions for COVID-19 Detection in Chest X-Rays., , , , и . MedInfo, том 290 из Studies in Health Technology and Informatics, стр. 679-683. IOS Press, (2021)Process Mining to Explore Variations in Endometrial Cancer Pathways from GP Referral to First Treatment., , , , , и . MIE, том 281 из Studies in Health Technology and Informatics, стр. 769-773. IOS Press, (2021)Predicting Ovarian Cancer Treatment Response in Histopathology using Hierarchical Vision Transformers and Multiple Instance Learning., , , , , и . CoRR, (2023)Efficient subtyping of ovarian cancer histopathology whole slide images using active sampling in multiple instance learning., , , , , и . CoRR, (2023)Learning disentangled representations for explainable chest x-ray classification using Dirichlet VAEs., , , и . Medical Imaging: Image Processing, том 12464 из SPIE Proceedings, SPIE, (2023)