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Found 6 papers, 0 papers with code
Semi-supervised Bladder Tissue Classification in Multi-Domain Endoscopic Images
We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i. e. there is no exact equivalent for every image in both NBI and WLI domains.
Autonomous Intraluminal Navigation of a Soft Robot using Deep-Learning-based Visual Servoing
Navigation inside luminal organs is an arduous task that requires non-intuitive coordination between the movement of the operator's hand and the information obtained from the endoscopic video.
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.
Comparison of different CNNs for breast tumor classification from ultrasound images
The best performance was obtained by fine tuning VGG-16, with an accuracy of 0. 919 and an AUC of 0. 934.
