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A transfer-learning approach for lesion detection in endoscopic images from the urinary tract
In this work we study the implementation of 3 different Convolutional Neural Networks (CNNs), using a 2-steps training strategy, to classify images from the urinary tract with and without lesions.
Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy
Of these, two architectures are taken as core-models, namely U-Net based in residual blocks($m_1$) and Mask-RCNN($m_2$), which are fed with single still-frames $I(t)$.
A Lumen Segmentation Method in Ureteroscopy Images based on a Deep Residual U-Net architecture
For the training of these networks, we analyze the use of two different color spaces: gray-scale and RGB data images.
A Logic-Based Framework Leveraging Neural Networks for Studying the Evolution of Neurological Disorders
The framework relies on a combined use of ML and ASP, and is general enough to be applied in several other application scenarios, which are outlined in the paper.
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