A5002865675- AI in cancer detection
- Colorectal Cancer Screening and Detection
- Radiomics and Machine Learning in Medical Imaging
- Gastric Cancer Management and Outcomes
- Digital Imaging for Blood Diseases
- Cancer and Skin Lesions
- Biomedical Text Mining and Ontologies
- Neurofibromatosis and Schwannoma Cases
- Cutaneous Melanoma Detection and Management
- Soft tissue tumor case studies
- Pancreatic and Hepatic Oncology Research
- Cell Image Analysis Techniques
University Hospitals Coventry and Warwickshire NHS Trust
2018-2024
University Hospitals of Leicester NHS Trust
2021-2024
National Health Service
2023
University Hospital Coventry
2022
Leicester Royal Infirmary
2022
The development of deep segmentation models for computational pathology (CPath) can help foster the investigation interpretable morphological biomarkers. Yet, there is a major bottleneck in success such approaches be-cause supervised learning require an abundance accurately labelled data. This issue exacerbated field CPath because generation detailed annotations usually demands input pathologist to be able distinguish between different tissue constructs and nuclei. Manually labelling nuclei...
Recent advances in whole-slide imaging (WSI) technology have led to the development of a myriad computer vision and artificial intelligence-based diagnostic, prognostic, predictive algorithms. Computational Pathology (CPath) offers an integrated solution utilise information embedded pathology WSIs beyond what can be obtained through visual assessment. For automated analysis validation machine learning (ML) models, annotations at slide, tissue, cellular levels are required. The annotation...
Objective To develop an interpretable artificial intelligence algorithm to rule out normal large bowel endoscopic biopsies, saving pathologist resources and helping with early diagnosis. Design A graph neural network was developed incorporating domain knowledge classify 6591 whole-slides images (WSIs) of biopsies from 3291 patients (approximately 54% female, 46% male) as or abnormal (non-neoplastic neoplastic) using clinically driven features. One UK National Health Service (NHS) site used...
Aims To conduct a definitive multicentre comparison of digital pathology (DP) with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples. Methods A total 2024 cases (608 breast, 607 GI, 609 skin, 200 renal) were studied, 207 250 Cases examined by four pathologists (16 study across the speciality groups), using both LM DP, order randomly assigned 6 weeks between viewings. Reports compared clinical management concordance (CMC), meaning...
Histopathological examination is a crucial step in the diagnosis and treatment of many major diseases. Aiming to facilitate diagnostic decision making improve workload pathologists, we developed an artificial intelligence (AI)-based prescreening tool that analyses whole-slide images (WSIs) large-bowel biopsies identify typical, non-neoplastic, neoplastic biopsies.This retrospective cohort study was conducted with internal development slides acquired from hospital UK three external validation...
Abstract Histopathological examination is a pivotal step in the diagnosis and treatment planning of many major diseases. To facilitate diagnostic decision-making reduce workload pathologists, we present an AI-based pre-screening tool capable identifying normal neoplastic colon biopsies. learn differential histological patterns from whole slides images (WSIs) stained with hematoxylin eosin (H&E), our proposed weakly supervised deep learning method requires only slide-level labels no...
As digital pathology replaces conventional glass slide microscopy as a means of reporting cellular samples, the annotation whole images is rapidly becoming part pathologist's regular practice. Currently, there no recognizable organization these annotations, and result, pathologists adopt an arbitrary approach to defining regions interest, leading irregularity inconsistency limiting downstream efficient use this valuable effort. In study, we propose Standardized Annotation Reporting Style for...
Neurofibromatosis type 1 is an autosomal dominant condition which can manifest as multiple neurofibromas within subcutaneous tissue. Neurofibromas of the breast are rare and most often encountered on nipple-areolar complexes. A 33-year-old woman presented with large, bilateral, fleshy, skin tags She underwent bilateral diagnostic excision lesions macroscopically, both nipple specimens displaying polypoid lesions. Histological examination showed comprising underlying dermal proliferation...
The development of deep segmentation models for computational pathology (CPath) can help foster the investigation interpretable morphological biomarkers. Yet, there is a major bottleneck in success such approaches because supervised learning require an abundance accurately labelled data. This issue exacerbated field CPath generation detailed annotations usually demands input pathologist to be able distinguish between different tissue constructs and nuclei. Manually labelling nuclei may not...
Abstract Objectives Develop an interpretable AI algorithm to rule out normal large bowel endoscopic biopsies saving pathologist resources. Design Retrospective study. Setting One UK NHS site was used for model training and internal validation. External validation conducted on data from two other sites one in Portugal. Participants 6,591 whole-slides images of 3,291 patients (54% Female, 46% Male). Main outcome measures Area under the receiver operating characteristic precision recall curves...
Abstract Background Histopathological examination is a pivotal step in the diagnosis and treatment planning of many major diseases. With aims facilitating diagnostic decision-making improving use pathologists’ time, we developed an AI-based pre-screening tool that analyses whole slide images (WSIs) large bowel biopsies to identify normal, inflammatory, neoplastic biopsies. Methods To learn differential histological patterns from digitised WSIs biopsy slides stained with Haematoxylin Eosin...
Recent advances in whole slide imaging (WSI) technology have led to the development of a myriad computer vision and artificial intelligence (AI) based diagnostic, prognostic, predictive algorithms. Computational Pathology (CPath) offers an integrated solution utilize information embedded pathology WSIs beyond what we obtain through visual assessment. For automated analysis validation machine learning (ML) models, annotations at slide, tissue cellular levels are required. The annotation...
Introduction: Digital pathology (DP) is the examination of digitised histopathology slides on computer workstations as opposed to brightfield and immunofluorescent light microscopy (LM). Deployment in routine practice requires demonstration that pathologists using DP provide equivalent reports comparison LM, current standard care.Purpose: Multicentre with LM for reporting measure intra inter-observer variation both modalities.Methods: Sample size 2000 cases (600 breast, 600 gastrointestinal...