Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers
Faculty of Science and Engineering, Manchester Metropolitan University（曼彻斯特城市大学科学与工程学院）；Lanchashire Teaching Hospitals（兰开夏教学医院）；Waikato District Health Board（怀卡托地区卫生局）
Moi Hoon Yap, Bill Cassidy, Joseph M. Pappachan, Claire O'Shea, David Gillespie, Neil Reeves
备注：
4 pages, 6 figures and 3 tables
发表信息：
Conference: 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)
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 ground truth annotation, data curation and data analysis. The final release of DFUC2021 consists of 15,683 DFU patches, with 5,955 training, 5,734 for testing and 3,994 unlabeled DFU patches. The ground truth labels are four classes, i.e. control, infection, ischaemia and both conditions. We curate the dataset using image hashing techniques and analyse the separability using UMAP projection. We benchmark the performance of five key backbones of deep learning, i.e. VGG16, ResNet101, InceptionV3, DenseNet121 and EfficientNet on DFUC2021. We report the optimised results of these key backbones with different strategies. Based on our observations, we conclude that EfficientNetB0 with data augmentation and transfer learning provided the best results for multi-class (4-class) classification with macro-average Precision, Recall and F1-score of 0.57, 0.62 and 0.55, respectively. In ischaemia and infection recognition, when trained on one-versus-all, EfficientNetB0 achieved comparable results with the state of the art. Finally, we interpret the results with statistical analysis and Grad-CAM visualisation.
Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospital（兰开夏教学医院）；University of Manchester（曼彻斯特大学）；Manchester Royal Infirmary（曼彻斯特皇家医院）；Sun Yat-sen University（中山大学）
Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospitals NHS Foundation Trust（兰开夏教学医院NHS基金会信托）；Waikato District Health Board（怀卡托地区卫生局）；University of Southern California（南加利福尼亚大学）
DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospital（兰开夏教学医院）；Waikato District Health Board（怀卡托地区卫生委员会）；Manipal College of Health Professions（马尼帕尔健康专业学院）；University of Waikato（怀卡托大学）；University of Manchester（曼彻斯特大学）；University of Southern California（南加州大学）；Baylor College of Medicine（贝勒医学院）
Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospitals NHS Foundation Trust（兰开夏教学医院NHS基金会信托）；Waikato District Health Board（怀卡托地区卫生委员会）；United Lincolnshire Hospitals NHS Trust（联合林肯郡医院NHS信托）；Jersey General Hospital（泽西总医院）；Eastbourne District General Hospital（伊斯特本地区综合医院）
Boosting EfficientNets Ensemble Performance via Pseudo-Labels and Synthetic Images by pix2pixHD for Infection and Ischaemia Classification in Diabetic Foot Ulcers
University of Applied Sciences and Arts Dortmund (FH Dortmund)（多特蒙德应用科学与艺术大学）；University Hospital Essen（埃森大学医院）
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospitals NHS Foundation Trust（兰开夏教学医院NHS基金会信托）；Waikato District Health Board（怀卡托地区卫生委员会）
DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification
Manchester Metropolitan University（曼彻斯特城市大学）；University of Manchester（曼彻斯特大学）；Lancashire Teaching Hospital（兰开夏教学医院）；Lancashire Care NHS Foundation Trust（兰开夏护理NHS基金会信托）
A Cloud-based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospitals NHS Foundation Trust（兰开夏教学医院NHS基金会信托）；The University of Manchester（曼彻斯特大学）；Manchester University NHS Foundation Trust（曼彻斯特大学NHS基金会信托）；Salford Royal NHS Foundation Trust（索尔福德皇家NHS基金会信托）
A quantitative comparison of plantar soft tissue strainability distribution and homogeneity between ulcerated and non-ulcerated patients using strain elastography
Staffordshire University（斯塔福德郡大学）；AR Diabetes Hospitals（AR糖尿病医院）；Technische Universität München（慕尼黑工业大学）
UlcerGPT: A Multimodal Approach Leveraging Large Language and Vision Models for Diabetic Foot Ulcer Image Transcription
KITE Research Institute（凯特研究所）；University Health Network（大学健康网络）；Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）；University of Toronto Mississauga（多伦多大学密西沙加分校）
Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation
Manchester Metropolitan University（曼彻斯特城市大学）；Keio University（庆应义塾大学）；Baker Heart and Diabetes Institute（贝克心脏与糖尿病研究所）；University of Applied Sciences and Arts Dortmund (FH Dortmund)（多特蒙德应用科学与艺术大学）；Dartmouth College（达特茅斯学院）；Shanghai University（上海大学）
ConMatFormer: A Multi-attention and Transformer Integrated ConvNext based Deep Learning Model for Enhanced Diabetic Foot Ulcer Classification
Department of Information Technology（信息科技系）；Charles Darwin University（查尔斯·达尔文大学）；Department of Computer Science and Engineering（计算机科学与工程系）；University of Liberal Arts Bangladesh（孟加拉自由大学）；BRAC University（BRAC大学）；Center for Advanced Analytics (CAA), COE for Artificial Intelligence, Faculty of Engineering & Technology (FET)（高级分析中心（CAA）、人工智能学院、工程与技术学院）；Multimedia University Melaka（马六甲多媒体大学）；American International University-Bangladesh（美国国际大学-孟加拉）
Domain-Specific Deep Learning Feature Extractor for Diabetic Foot Ulcer Detection
Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）；KITE, Toronto Rehabilitation Institute, University Health Network（大学健康网络多伦多康复研究所KITE部门）
Synthesizing Diabetic Foot Ulcer Images with Diffusion Model
Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）；University Health Network（多伦多大学健康网络）；Zivot Limb Preservation Centre, Peter Lougheed Centre（齐沃特肢体保护中心（彼得·劳埃德中心））；Cumming School of Medicine, University of Calgary（卡尔加里大学卡明医学院）
WoundNet-Ensemble: A Novel IoMT System Integrating Self-Supervised Deep Learning and Multi-Model Fusion for Automated, High-Accuracy Wound Classification and Healing Progression Monitoring
The Catholic University of America（美国天主教大学）；Center for Advanced Research in Computer Engineering（计算机工程高级研究中心）
Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification
Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology（密苏里科技大学机械与航空航天工程系）；Intelligent System Center, Missouri University of Science and Technology（密苏里科技大学智能系统中心）；Center for Biomedical Research, Missouri University of Science and Technology（密苏里科技大学生物医学研究中心）；Wound Care Department, Charles George Department of Veterans Affairs Medical Center（查尔斯·乔治退伍军人事务医疗中心伤口护理部）
