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Found 18 papers, 1 papers with code
Prediction of Breast Cancer Recurrence Risk Using a Multi-Model Approach Integrating Whole Slide Imaging and Clinicopathologic Features
The aim of this study was to develop a multi-model approach integrating the analysis of whole slide images and clinicopathologic data to predict their associated breast cancer recurrence risks and categorize these patients into two risk groups according to the predicted score: low and high risk.
Vision Transformer-Based Deep Learning for Histologic Classification of Endometrial Cancer
Endometrial cancer, the fourth most common cancer in females in the United States, with the lifetime risk for developing this disease is approximately 2. 8% in women.
DiffGEPCI: 3D MRI Synthesis from mGRE Signals using 2.5D Diffusion Model
We introduce a new framework called DiffGEPCI for cross-modality generation in magnetic resonance imaging (MRI) using a 2. 5D conditional diffusion model.
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.
Automated Kidney Segmentation by Mask R-CNN in T2-weighted Magnetic Resonance Imaging
Despite the recent advances of deep learning algorithms in medical imaging, the automatic segmentation algorithms for kidneys in MRI exams are still scarce.
Sensitivity and Specificity Evaluation of Deep Learning Models for Detection of Pneumoperitoneum on Chest Radiographs
State-of-the-art deep learning models (ResNet101, InceptionV3, DenseNet161, and ResNeXt101) were trained on a subset of this dataset, and the automated classification performance was evaluated on the rest of the dataset by measuring the AUC, sensitivity, and specificity for each model.
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.
A Refined Deep Learning Architecture for Diabetic Foot Ulcers Detection
Diabetic Foot Ulcers (DFU) that affect the lower extremities are a major complication of diabetes.
Artificial Intelligence-Based Image Classification for Diagnosis of Skin Cancer: Challenges and Opportunities
Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer.
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.
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.
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.
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.
