26 citations
- University College LondonGB5 papers
- CRUK Lung Cancer Centre of ExcellenceGB2 papers
- Imperial College LondonGB2 papers
- Institute of Cancer ResearchGB2 papers
- Biotechnologisches ZentrumDE1 paper
- Breast Cancer NowGB1 paper
- Cancer Institute (WIA)IN1 paper
- Cancer Research UKGB1 paper
- Center for Systems Biology DresdenDE1 paper
- Central European UniversityAT1 paper
- Department of Science and TechnologyIN1 paper
- Group Image (Poland)PL1 paper
9 papers
Development and pilot evaluation of a virtual reality simulator for HDR prostate brachytherapy
Anton Varlukhin, Mackenzie Smith, Fahad Alam +8
Purpose: To develop a virtual reality simulator for high dose rate prostate brachytherapy and to test whether participation is associated with immediate gains in self-reported conf…
Interpretable Transformation and Analysis of Timelines through Learning via Surprisability
Osnat Mokryn, Teddy Lazebnik, Hagit Ben Shoshan
The analysis of high-dimensional timeline data and the identification of outliers and anomalies is critical across diverse domains, including sensor readings, biological and medica…
Leveraging large language models for structured information extraction from pathology reports
Jeya Balaji Balasubramanian, Daniel Adams, Ioannis Roxanis +4
Background: Structured information extraction from unstructured histopathology reports facilitates data accessibility for clinical research. Manual extraction by experts is time-co…
OMG-Net: A Deep Learning Framework Deploying Segment Anything to Detect Pan-Cancer Mitotic Figures from Haematoxylin and Eosin-Stained Slides
Zhuoyan Shen, Mikael Simard, Douglas Brand +14
Mitotic activity is an important feature for grading several cancer types. Counting mitotic figures (MFs) is a time-consuming, laborious task prone to inter-observer variation. Ina…
Mining higher-order triadic interactions
Marta Niedostatek, Anthony Baptista, Jun Yamamoto +4
Complex systems often involve higher-order interactions which require us to go beyond their description in terms of pairwise networks. Triadic interactions are a fundamental type o…
Cell abundance aware deep learning for cell detection on highly imbalanced pathological data
Yeman Brhane Hagos, Catherine SY Lecat, Dominic Patel +5
Automated analysis of tissue sections allows a better understanding of disease biology and may reveal biomarkers that could guide prognosis or treatment selection. In digital patho…