233 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Manchester Metropolitan UniversityGB9 papers
- University of ManchesterGB3 papers
- Waikato District Health BoardNZ3 papers
- Lancaster UniversityGB2 papers
- University of Southern CaliforniaUS2 papers
- Baylor College of MedicineUS1 paper
- Dortmund University of Applied Sciences and ArtsDE1 paper
- Essen University HospitalDE1 paper
- King Saud Medical CitySA1 paper
- King Saud UniversitySA1 paper
- Manchester Royal InfirmaryGB1 paper
- Manchester University NHS Foundation TrustGB1 paper
9 papers
Gaussian Random Fields as an Abstract Representation of Patient Metadata for Multimodal Medical Image Segmentation
Bill Cassidy, Christian McBride, Connah Kendrick +4
The growing rate of chronic wound occurrence, especially in patients with diabetes, has become a concerning trend in recent years. Chronic wounds are difficult and costly to treat,…
An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Merging
Bill Cassidy, Christian Mcbride, Connah Kendrick +11
Chronic wounds and associated complications present ever growing burdens for clinics and hospitals world wide. Venous, arterial, diabetic, and pressure wounds are becoming increasi…
Quantifying the Effect of Image Similarity on Diabetic Foot Ulcer Classification
Imran Chowdhury Dipto, Bill Cassidy, Connah Kendrick +4
This research conducts an investigation on the effect of visually similar images within a publicly available diabetic foot ulcer dataset when training deep learning classification…
Diabetic Foot Ulcer Grand Challenge 2022 Summary
Connah Kendrick, Bill Cassidy, Neil D. Reeves +4
The Diabetic Foot Ulcer Challenge 2022 focused on the task of diabetic foot ulcer segmentation, based on the work completed in previous DFU challenges. The challenge provided 4000…
Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary
Bill Cassidy, Connah Kendrick, Neil D. Reeves +4
Diabetic foot ulcer classification systems use the presence of wound infection (bacteria present within the wound) and ischaemia (restricted blood supply) as vital clinical indicat…
A Cloud-based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers
Bill Cassidy, Neil D. Reeves, Joseph M. Pappachan +4
This research proposes a mobile and cloud-based framework for the automatic detection of diabetic foot ulcers and conducts an investigation of its performance. The system uses a cr…
Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers
Moi Hoon Yap, Bill Cassidy, Joseph M. Pappachan +3
This paper introduces the Diabetic Foot Ulcers dataset (DFUC2021) for analysis of pathology, focusing on infection and ischaemia. We describe the data preparation of DFUC2021 for g…
DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection
Bill Cassidy, Neil D. Reeves, Pappachan Joseph +10
Every 20 seconds, a limb is amputated somewhere in the world due to diabetes. This is a global health problem that requires a global solution. The MICCAI challenge discussed in thi…
Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques
Manu Goyal, Neil Reeves, Satyan Rajbhandari +3
Recognition and analysis of Diabetic Foot Ulcers (DFU) using computerized methods is an emerging research area with the evolution of image-based machine learning algorithms. Existi…