6 citations · 7 across the 9 of their papers we have counts for
5 papers · 1 filter
Cross-Domain Validation of a Resection-Trained Self-Supervised Model on Multicentre Mesothelioma Biopsies
Farzaneh Seyedshahi, Francesca Damiola, Sylvie Lantuejoul +2
Accurate subtype classification and outcome prediction in mesothelioma are essential for guiding therapy and patient care. Most computational pathology models are trained on large…
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…
Training-Free Pretrained Model Merging
Zhengqi Xu, Ke Yuan, Huiqiong Wang +3
Recently, model merging techniques have surfaced as a solution to combine multiple single-talent models into a single multi-talent model. However, previous endeavors in this field…
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…