390 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of LeedsGB5 papers
- Alberta Machine Intelligence InstituteCA2 papers
- American College of RadiologyUS2 papers
- Aminu Kano Teaching HospitalNG2 papers
- Athinoula A. Martinos Center for Biomedical ImagingUS2 papers
- Baylor College of MedicineUS2 papers
- Brigham and Women's HospitalUS2 papers
- Case Western Reserve UniversityUS2 papers
- Centre Hospitalier Universitaire de SherbrookeCA2 papers
- Chinese University of Hong KongHK2 papers
- Christiana Care Health SystemUS2 papers
- Clatterbridge Cancer Centre NHS Foundation TrustGB2 papers
8 papers
Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Charlotte Jennings, Andrew Broad, Lucy Godson +3
Multimodal machine learning integrating histopathology and molecular data shows promise for cancer prognostication. We systematically reviewed studies combining whole slide images…
Interpretable Prediction of Lymph Node Metastasis in Rectal Cancer MRI Using Variational Autoencoders
Benjamin Keel, Aaron Quyn, David Jayne +2
Effective treatment for rectal cancer relies on accurate lymph node metastasis (LNM) staging. However, radiological criteria based on lymph node (LN) size, shape and texture morpho…
AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines
Douwe J. Spaanderman, Matthew Marzetti, Xinyi Wan +13
Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides an over…
Pathologists light level preferences using the microscope -- a study to guide digital pathology display use
Charlotte Jennings, Darren Treanor, David Brettle
There is a paucity of guidelines relating to displays in digital pathology making procurement decisions, and display configuration challenging. Experience suggests pathologists hav…
Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review
Mary Paterson, James Moor, Luisa Cutillo
Introduction: Cases of throat cancer are rising worldwide. With survival decreasing significantly at later stages, early detection is vital. Artificial intelligence (AI) and machin…
Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy
Clare McGenity, Emily L Clarke, Charlotte Jennings +5
Ensuring diagnostic performance of AI models before clinical use is key to the safe and successful adoption of these technologies. Studies reporting AI applied to digital pathology…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
The Federated Tumor Segmentation (FeTS) Challenge
Sarthak Pati, Ujjwal Baid, Maximilian Zenk +29
This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standar…