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Found 7 papers, 4 papers with code
Evaluation Framework for Computer Vision-Based Guidance of the Visually Impaired
This paper presents an evaluation framework for computer vision-based guiding of the visually impaired persons in such critical situations.
Using the Polar Transform for Efficient Deep Learning-Based Aorta Segmentation in CTA Images
In this paper, we present a general approach to improving the semantic segmentation performance of neural networks in these tasks and validate our approach on the task of aorta segmentation.
Self-Supervised Learning as a Means To Reduce the Need for Labeled Data in Medical Image Analysis
In this paper, we evaluate a method of reducing the need for labeled data in medical image object detection by using self-supervised neural network pretraining.
Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial Networks
Deep learning has a great potential to alleviate diagnosis and prognosis for various clinical procedures.
A Survey of Left Atrial Appendage Segmentation and Analysis in 3D and 4D Medical Images
Atrial fibrillation (AF) is a cardiovascular disease identified as one of the main risk factors for stroke.
Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network
Fully automatic and reliable measurements of epicardial adipose tissue from CT scans could provide better disease risk assessment and enable the processing of large CT image data sets for a systemic epicardial adipose tissue study.
Training on Polar Image Transformations Improves Biomedical Image Segmentation
We show that our method produces state-of-the-art results for lesion, liver, and polyp segmentation and performs better than most common neural network architectures for biomedical image segmentation.
Ranked #1 on
Lesion Segmentation
on ISIC 2018
