8 citations · 8 across the 3 of their papers we have counts for
4 papers
Synergy vs. Noise: Performance-Guided Multimodal Fusion For Biochemical Recurrence-Free Survival in Prostate Cancer
Seth Alain Chang, Muhammad Mueez Amjad, Noorul Wahab +3
Multimodal deep learning (MDL) has emerged as a transformative approach in computational pathology. By integrating complementary information from multiple data sources, MDL models…
ModalSurv: Investigating opportunities and limitations of multimodal deep survival learning in prostate and bladder cancer
Noorul Wahab, Ethar Alzaid, Jiaqi Lv +3
Accurate survival prediction is essential for personalised cancer treatment. We propose ModalSurv, a multimodal deep survival framework integrating clinical, MRI, histopathology, a…
Large Multimodal Model based Standardisation of Pathology Reports with Confidence and their Prognostic Significance
Ethar Alzaid, Gabriele Pergola, Harriet Evans +2
Pathology reports are rich in clinical and pathological details but are often presented in free-text format. The unstructured nature of these reports presents a significant challen…
TIAViz: A Browser-based Visualization Tool for Computational Pathology Models
Mark Eastwood, John Pocock, Mostafa Jahanifar +8
Digital pathology has gained significant traction in modern healthcare systems. This shift from optical microscopes to digital imagery brings with it the potential for improved dia…