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Found 29 papers, 8 papers with code
Wound Tissue Segmentation in Diabetic Foot Ulcer Images Using Deep Learning: A Pilot Study
Additionally, our hybrid model outperforms the state-of-the-art methods with a 92. 99% DSC in performing binary segmentation of DFU wound areas when tested on the Chronic Wound dataset.
A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-rays
The proposed model's accuracy and versatility offer promising prospects for improving dental diagnoses, treatment planning, and personalized healthcare in the oral domain.
Integrated Image and Location Analysis for Wound Classification: A Deep Learning Approach
The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments.
FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentation
As the top decoder stage carries a limited number of feature maps, max-out scSE is bypassed there to form a shorted P-scSE.
MultiNet with Transformers: A Model for Cancer Diagnosis Using Images
Cancer is a leading cause of death in many countries.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Wound Severity Classification using Deep Neural Network
This study used wound photos to construct a deep neural network-based wound severity classifier that classified them into one of three classes: green, yellow, or red.
Machine learning techniques to identify antibiotic resistance in patients diagnosed with various skin and soft tissue infections
We trained an individual ML algorithm on each antimicrobial family to determine whether a Gram-Positive Cocci (GPC) or Gram-Negative Bacilli (GNB) bacteria will resist the corresponding antibiotic.
FUSeg: The Foot Ulcer Segmentation Challenge
Segmentation of wound boundaries in images is a key component of the care and diagnosis protocol since it is important to estimate the area of the wound and provide quantitative measurement for the treatment.
Automatic tracing of mandibular canal pathways using deep learning
A 3D U-Net architecture is used for model training.
Multi-modal Wound Classification using Wound Image and Location by Deep Neural Network
This study developed a deep neural network-based multi-modal classifier using wound images and their corresponding locations to categorize wound images into multiple classes, including diabetic, pressure, surgical, and venous ulcers.
Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks
We utilized samples generated by our proposed GAN in training a prognosis model to demonstrate its real-life application.
Multiclass Burn Wound Image Classification Using Deep Convolutional Neural Networks
A pre-trained deep convolutional neural network, AlexNet, is fine-tuned using a burn wound image dataset and utilized as the classifier.
A Deep Learning Study on Osteosarcoma Detection from Histological Images
In this study, transfer learning techniques, pre-trained CNNs, are adapted to a public dataset on osteosarcoma histological images to detect necrotic images from non-necrotic and healthy tissues.
C-Net: A Reliable Convolutional Neural Network for Biomedical Image Classification
In this study, we propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images.
Multiclass Wound Image Classification using an Ensemble Deep CNN-based Classifier
Acute and chronic wounds are a challenge to healthcare systems around the world and affect many people's lives annually.
Fully Automatic Wound Segmentation with Deep Convolutional Neural Networks
Fully automatic segmentation of wound areas in natural images is an important part of the diagnosis and care protocol since it is crucial to measure the area of the wound and provide quantitative parameters in the treatment.
Fully Automatic Intervertebral Disc Segmentation Using Multimodal 3D U-Net
With the advance of deep learning, various neural network models have gained great success in image analysis including the recognition of intervertebral discs.
Image Based Artificial Intelligence in Wound Assessment: A Systematic Review
Efficient and effective assessment of acute and chronic wounds can help wound care teams in clinical practice to greatly improve wound diagnosis, optimize treatment plans, ease the workload and achieve health related quality of life to the patient population.
A Mobile App for Wound Localization using Deep Learning
We present an automated wound localizer from 2D wound and ulcer images by using deep neural network, as the first step towards building an automated and complete wound diagnostic system.
An Application of Manifold Learning in Global Shape Descriptors
With the rapid expansion of applied 3D computational vision, shape descriptors have become increasingly important for a wide variety of applications and objects from molecules to planets.
siftservice.com - Turning a Computer Vision algorithm into a World Wide Web Service
Image features detection and description is a longstanding topic in computer vision and pattern recognition areas.
Application of Independent Component Analysis Techniques in Speckle Noise Reduction of Retinal OCT Images
Therefore the need for speckle noise reduction techniques is of high importance.
Sparse And Low Rank Decomposition Based Batch Image Alignment for Speckle Reduction of retinal OCT Images
Optical Coherence Tomography (OCT) is an emerging technique in the field of biomedical imaging, with applications in ophthalmology, dermatology, coronary imaging etc.
Fast Mesh-Based Medical Image Registration
The minimization process was achieved using a mesh based discretization of the distance measure and regularization term which resulted in a sparse system of linear equations, which due to the smaller size in comparison to the pixel-wise registration method, can be solved directly.
State-of-the-Art in Retinal Optical Coherence Tomography Image Analysis
Here, the focus is on applications of OCT in ophthalmology and retinal imaging.
Adaptive Mesh Representation and Restoration of Biomedical Images
The triangulation of images has become an active research area in recent years for its compressive representation and ease of image processing and visualization.
Structure Tensor Based Image Interpolation Method
Using an isotropic Gaussian filter, the structure tensor at each pixel of the input image is computed and the pixels are classified to three distinct classes; uniform region, corners and edges, according to the eigenvalues of the structure tensor.
An Optimization Method For Slice Interpolation Of Medical Images
Slice interpolation is a fast growing field in medical image processing.
