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Found 6 papers, 0 papers with code
FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation
This contribution presents a deep learning method for the segmentation of prostate zones in MRI images based on U-Net using additive and feature pyramid attention modules, which can improve the workflow of prostate cancer detection and diagnosis.
Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scores
This study focuses on comparing deep learning methods for the segmentation and quantification of uncertainty in prostate segmentation from MRI images.
Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures
Prostate cancer is the second-most frequently diagnosed cancer and the sixth leading cause of cancer death in males worldwide.
Impact of loss function in Deep Learning methods for accurate retinal vessel segmentation
The best average of Hausdorff distance and mean square error were obtained using the Nested U-Net with the Dice loss function, which had an average of 6. 32 and 0. 0241 respectively.
Experimental Large-Scale Jet Flames' Geometrical Features Extraction for Risk Management Using Infrared Images and Deep Learning Segmentation Methods
This research work explores the application of deep learning models in an alternative approach that uses the semantic segmentation of jet fires flames to extract main geometrical attributes, relevant for fire risk assessments.
Comparing Machine Learning based Segmentation Models on Jet Fire Radiation Zones
One such characterization would be the segmentation of different radiation zones within the flame, so this paper presents an exploratory research regarding several traditional computer vision and Deep Learning segmentation approaches to solve this specific problem.
