Semantic Segmentation of Neuronal Bodies in Fluorescence Microscopy Using a 2D+3D CNN Training Strategy with Sparsely Annotated DataSemantic segmentationNeuronal segmentationPseudo-labelingSemantic segmentation
Paper tables with annotated results for SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentation
该方法的优点是: 1、at inference time we can reverse the application direction of our network in order to propagate pose information from manually annotated frames to unlabeled frames 2、we can improve the accuracy of a pose estimator by training it on an augmented dataset 3、we can use our P...
Pseudo-labelling-aided semantic segmentation on sparsely annotated 3D point cloudsSemantic segmentation of point cloudsPseudo-labellingDeep neural networkManually labelling point cloud scenes for use as training data in machine learning applications is a time- and labour-intensive task. In this paper, we...
Learning from sparsely annotated data for semantic segmentation in histopathology images.John-Melle BokhorstHans PinckaersPeter van ZwamIris NagtegaalJeroen van der LaakFrancesco CiompiPMLRInternational Conference on Medical Imaging with Deep Learning
PurposeSemantic segmentation is one of the most significant tasks in medical image computing, whereby deep neural networks have shown great success. Unfortunately, supervised approaches are very data-intensive, and obtaining reliable annotations is time-consuming and expensive. Sparsely labeled approaches, ...
Multi-cue semantic calibrationSparsely annotated image segmentation has gained popularity due to its ability to significantly reduce the labeling burden on training data. However, existing methods still struggle to learn complete object structures, especially for complex shadow objects. This paper discusses...