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Found 40 papers, 10 papers with code
Gaussian Random Fields as an Abstract Representation of Patient Metadata for Multimodal Medical Image Segmentation
This paper presents the first study to focus on integrating patient data into a chronic wound segmentation workflow.
An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Merging
This paper presents the first study to focus on darker-skin tones for chronic wound segmentation using models trained only on wound images exhibiting lighter skin.
Dermoscopic Dark Corner Artifacts Removal: Friend or Foe?
This paper provides a new guideline for skin lesions analysis with an emphasis on reproducibility.
Venn Diagram Multi-label Class Interpretation of Diabetic Foot Ulcer with Color and Sharpness Enhancement
We propose to reduce the four classes into two since both class wounds can be interpreted as the simultaneous occurrence of infection and ischaemia and none class wounds as the absence of infection and ischaemia.
Quantifying the Effect of Image Similarity on Diabetic Foot Ulcer Classification
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 networks.
Diabetic Foot Ulcer Grand Challenge 2022 Summary
The Diabetic Foot Ulcer Challenge 2022 focused on the task of diabetic foot ulcer segmentation, based on the work completed in previous DFU challenges.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images
Different from existing attention mechanisms, the hybrid adaptive attention module can guide the network to adaptively select more robust representation in channel and space dimensions to cope with more complex breast lesions segmentation.
Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation
This paper demonstrates that image processing using refined contour as ground truth can provide better agreement with machine predicted results.
V-LinkNet: Learning Contextual Inpainting Across Latent Space of Generative Adversarial Network
The RSTL layer easily adapts dual encoders by increasing the unique semantic information through direct communication.
Development of Diabetic Foot Ulcer Datasets: An Overview
This paper provides conceptual foundation and procedures used in the development of diabetic foot ulcer datasets over the past decade, with a timeline to demonstrate progress.
Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary
Diabetic foot ulcer classification systems use the presence of wound infection (bacteria present within the wound) and ischaemia (restricted blood supply) as vital clinical indicators for treatment and prediction of wound healing.
A Cloud-based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers
This research proposes a mobile and cloud-based framework for the automatic detection of diabetic foot ulcers and conducts an investigation of its performance.
3D-CNN for Facial Micro- and Macro-expression Spotting on Long Video Sequences using Temporal Oriented Reference Frame
Facial expression spotting is the preliminary step for micro- and macro-expression analysis.
Foreground-guided Facial Inpainting with Fidelity Preservation
It introduces the use of foreground segmentation masks to preserve the fidelity.
Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers
We describe the data preparation of DFUC2021 for ground truth annotation, data curation and data analysis.
Deep learning in magnetic resonance prostate segmentation: A review and a new perspective
Deep learning has been identified as a potential new technology for the delivery of precision radiotherapy in prostate cancer, where accurate prostate segmentation helps in cancer detection and therapy.
Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation
DFUC2020 provided participants with a comprehensive dataset consisting of 2, 000 images for training and 2, 000 images for testing.
R-MNet: A Perceptual Adversarial Network for Image Inpainting
We address the problem by proposing a Wasserstein GAN combined with a new reverse mask operator, namely Reverse Masking Network (R-MNet), a perceptual adversarial network for image inpainting.
DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection
Every 20 seconds, a limb is amputated somewhere in the world due to diabetes.
Anysize GAN: A solution to the image-warping problem
We develop a novel architecture that can be applied to existing latent vector based GAN structures that allows them to generate on-the-fly images of any size.
Symmetric Skip Connection Wasserstein GAN for High-Resolution Facial Image Inpainting
Additionally, we propose a Wasserstein-Perceptual loss function to preserve colour and maintain realism on a reconstructed image.
Spotting Macro- and Micro-expression Intervals in Long Video Sequences
Both macro- and micro-expression intervals in CAS(ME)$^2$ and SAMM Long Videos are spotted by employing the method of Main Directional Maximal Difference Analysis (MDMD).
SAMM Long Videos: A Spontaneous Facial Micro- and Macro-Expressions Dataset
We conduct facial expression spotting using this dataset and compare it with the baseline of MEGC III.
Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques
We found that our proposed Ensemble CNN deep learning algorithms performed better for both classification tasks as compared to handcrafted machine learning algorithms, with 90% accuracy in ischaemia classification and 73% in infection classification.
Automatic Lesion Boundary Segmentation in Dermoscopic Images with Ensemble Deep Learning Methods
In this work, we propose the use of fully automated deep learning ensemble methods for accurate lesion boundary segmentation in dermoscopic images.
Spotting Micro-Expressions on Long Videos Sequences
This paper presents baseline results for the first Micro-Expression Spotting Challenge 2019 by evaluating local temporal pattern (LTP) on SAMM and CAS(ME)2.
Deep Learning Methods and Applications for Region of Interest Detection in Dermoscopic Images
In addition, we developed an automated natural data-augmentation method from ROI detection to produce augmented copies of dermoscopic images, as a pre-processing step in the segmentation of skin lesions to further improve the performance of the current state-of-the-art deep learning algorithm.
Multi-Class Lesion Diagnosis with Pixel-wise Classification Network
Usually, deep classification networks are used for the lesion diagnosis to determine different types of skin lesions.
A Review on Facial Micro-Expressions Analysis: Datasets, Features and Metrics
Facial micro-expressions are very brief, spontaneous facial expressions that appear on the face of humans when they either deliberately or unconsciously conceal an emotion.
Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance Images
This paper presents an end-to-end solution for MRI thigh quadriceps segmentation.
DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification
In this paper, we have proposed the use of traditional computer vision features for detecting foot ulcers among diabetic patients, which represent a cost-effective, remote and convenient healthcare solution.
Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks
Melanoma is clinically difficult to distinguish from common benign skin lesions, particularly melanocytic naevus and seborrhoeic keratosis.
Objective Classes for Micro-Facial Expression Recognition
The best result achieves 86. 35\% accuracy when classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the result of the state-of-the-art 5-class emotional-based classification in CASME II.
Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation
Using 5-fold cross-validation, the proposed two-tier transfer learning FCN Models achieve a Dice Similarity Coefficient of 0. 794 ($\pm$0. 104) for ulcer region, 0. 851 ($\pm$0. 148) for surrounding skin region, and 0. 899 ($\pm$0. 072) for the combination of both regions.
Automated Assessment of Facial Wrinkling: a case study on the effect of smoking
The face was divided into ten predefined regions, where the wrinkles in each region was extracted.
A Comparative Study of the Clinical use of Motion Analysis from Kinect Skeleton Data
The analysis of human motion as a clinical tool can bring many benefits such as the early detection of disease and the monitoring of recovery, so in turn helping people to lead independent lives.
Objective Micro-Facial Movement Detection Using FACS-Based Regions and Baseline Evaluation
This paper proposes an individualised baseline micro-movement detection method using 3D Histogram of Oriented Gradients (3D HOG) temporal difference method.
