1 code implementation • 19 Jan 2023 • Antanas Kascenas, Pedro Sanchez, Patrick Schrempf, Chaoyang Wang, William Clackett, Shadia S. Mikhael, Jeremy P. Voisey, Keith Goatman, Alexander Weir, Nicolas Pugeault, Sotirios A. Tsaftaris, Alison Q. O'Neil
Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance.
no code implementations • 31 Jul 2020 • Patrick Schrempf, Hannah Watson, Shadia Mikhael, Maciej Pajak, Matúš Falis, Aneta Lisowska, Keith W. Muir, David Harris-Birtill, Alison Q. O'Neil
Training medical image analysis models requires large amounts of expertly annotated data which is time-consuming and expensive to obtain.
no code implementations • WS 2019 • Matus Falis, Maciej Pajak, Aneta Lisowska, Patrick Schrempf, Lucas Deckers, Shadia Mikhael, Sotirios Tsaftaris, Alison O{'}Neil
We present a semantically interpretable system for automated ICD coding of clinical text documents.
no code implementations • 10 Oct 2019 • Mattias Appelgren, Patrick Schrempf, Matúš Falis, Satoshi Ikeda, Alison Q. O'Neil
However, the data required to train models for every language may be difficult, expensive and time-consuming to obtain, particularly for low-resource languages.