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Found 6 papers, 0 papers with code
An edge detection-based deep learning approach for tear meniscus height measurement
For improved segmentation of the pupil and tear meniscus areas, the convolutional neural network Inceptionv3 was first implemented as an image quality assessment model, effectively identifying higher-quality images with an accuracy of 98. 224%.
Automatic nodule identification and differentiation in ultrasound videos to facilitate per-nodule examination
Ultrasound is a vital diagnostic technique in health screening, with the advantages of non-invasive, cost-effective, and radiation free, and therefore is widely applied in the diagnosis of nodules.
Automatic CT Segmentation from Bounding Box Annotations using Convolutional Neural Networks
Existing automatic segmentation methods are mainly based on fully supervised learning and have an extremely high demand for precise annotations, which are very costly and time-consuming to obtain.
Segmentation of Levator Hiatus Using Multi-Scale Local Region Active contours and Boundary Shape Similarity Constraint
In this paper, a multi-scale framework with local region based active contour and boundary shape similarity constraint is proposed for the segmentation of levator hiatus in ultrasound images.
Automatic 3D liver location and segmentation via convolutional neural networks and graph cut
Purpose Segmentation of the liver from abdominal computed tomography (CT) image is an essential step in some computer assisted clinical interventions, such as surgery planning for living donor liver transplant (LDLT), radiotherapy and volume measurement.
Flip-Rotate-Pooling Convolution and Split Dropout on Convolution Neural Networks for Image Classification
We also introduce two rotational convolution techniques, i. e. rotate-pooling convolution (RPC) and flip-rotate-pooling convolution (FRPC) to boost CNNs' performance on the robustness for rotation transformation.
