1 citations · 2 across the 5 of their papers we have counts for
5 papers
Towards deployment-centric multimodal AI beyond vision and language
Xianyuan Liu, Jiayang Zhang, Shuo Zhou +45
Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as h…
Divide and Conquer Self-Supervised Learning for High-Content Imaging
Lucas Farndale, Paul Henderson, Edward W Roberts +1
Self-supervised representation learning methods often fail to learn subtle or complex features, which can be dominated by simpler patterns which are much easier to learn. This limi…
Synthetic Privileged Information Enhances Medical Image Representation Learning
Lucas Farndale, Chris Walsh, Robert Insall +1
Multimodal self-supervised representation learning has consistently proven to be a highly effective method in medical image analysis, offering strong task performance and producing…
TriDeNT: Triple Deep Network Training for Privileged Knowledge Distillation in Histopathology
Lucas Farndale, Robert Insall, Ke Yuan
Computational pathology models rarely utilise data that will not be available for inference. This means most models cannot learn from highly informative data such as additional imm…
More From Less: Self-Supervised Knowledge Distillation for Routine Histopathology Data
Lucas Farndale, Robert Insall, Ke Yuan
Medical imaging technologies are generating increasingly large amounts of high-quality, information-dense data. Despite the progress, practical use of advanced imaging technologies…