Self-interactive learning: Fusion and evolution of multi-scale histomorphology features for molecular traits prediction in computational pathology

A conversation with Yang Hu about his recent article: Self-interactive learning: Fusion and evolution of multi-scale histomorphology features for molecular traits prediction in computational pathology. This episode discusses Yang Hu et al, ‘Self-interactive learning: Fusion and evolution of multi-scale histomorphology features for molecular traits prediction in computational pathology’, Medical Image Analysis, 101 (2025) https://doi.org/10.1016/j.media.2024.103437 The relevant study was supported by the PathLAKE Centre of Excellence for digital pathology and artificial intelligence which is funded by the Data to Early Diagnosis and Precision Medicine strand of the HM Government’s Industrial Strategy Challenge Fund, managed and delivered by Innovate UK on behalf of UK Research and Innovation (UKRI) (Grant ref: 104689/application number 18181), as well as NIHR Oxford Biomedical Research Centre. Views expressed are not necessarily those of the PathLAKE Consortium members, the NHS, the UKRI, the NIHR, Innovate UK or the Department of Health. The speaker would also like to thank the support from the Quantitative Bio-Image Group, led by Professor Jens Rittscher.