CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models

no code implementations • 17 Jul 2023 • Fan Fan, Georgia Martinez, Thomas Desilvio, John Shin, Yijiang Chen, Bangchen Wang, Takaya Ozeki, Maxime W. Lafarge, Viktor H. Koelzer, Laura Barisoni, Anant Madabhushi, Satish E. Viswanath, Andrew Janowczyk

Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability.

PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labeling

no code implementations • 13 Jul 2023 • Cedric Walker, Tasneem Talawalla, Robert Toth, Akhil Ambekar, Kien Rea, Oswin Chamian, Fan Fan, Sabina Berezowska, Sven Rottenberg, Anant Madabhushi, Marie Maillard, Laura Barisoni, Hugo Mark Horlings, Andrew Janowczyk

Using >100, 000 objects, we demonstrate a >7x improvement in labels per second over unaided labeling, with minimal impact on labeling accuracy, thus enabling high-throughput labeling of large datasets.

Digital staining in optical microscopy using deep learning -- a review

no code implementations • 14 Mar 2023 • Lucas Kreiss, Shaowei Jiang, Xiang Li, Shiqi Xu, Kevin C. Zhou, Alexander Mühlberg, Kyung Chul Lee, Kanghyun Kim, Amey Chaware, Michael Ando, Laura Barisoni, Seung Ah Lee, Guoan Zheng, Kyle Lafata, Oliver Friedrich, Roarke Horstmeyer

Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology.

Specificity