Titlebook: OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging; Second International Luping Zhou,Duygu Sarikaya,Hong

玷污

Feature Aggregation Decoder for Segmenting Laparoscopic Scenesion. Scene segmentation approaches often rely on encoder-decoder architectures that encode a representation of the input to be decoded to semantic pixel labels. In this paper, we propose to use the deep . model for the encoder and a simple yet effective decoder that relies on a feature aggregation m

小畫像

Preoperative Planning for Guidewires Employing Shape-Regularized Segmentation and Optimized Trajectoally driven guidewires replace manual insertion and leave the surgeon the task of planning optimal trajectories based on segmentation of associated risk structures. However, such a pipeline brings new challenges. While Deep learning based segmentation such as U-Net can achieve outstanding Dice score

漸強

integrated

Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Dnt success of deep learning-based methods in computer vision, several neural network approaches have been proposed for fully automatic RSD prediction based solely on visual data from the endoscopic camera. We investigate whether RSD prediction can be improved using unsupervised temporal video segmen

interlude

ABASH

negligence

Deep Transfer Learning for Whole-Brain FMRI Analyses often hindered by the small sample size and high dimensionality of these datasets. Especially, in clinical settings, where patient data are scarce. In this work, we demonstrate that transfer learning represents a solution to this problem. Particularly, we show that a DL model, which has been previo

EWER

別炫耀

Aviary

Data Pooling and Sampling of?Heterogeneous Image Data for White Matter Hyperintensity Segmentationen training Deep Neural Networks (DNN) to segment WMH, data pooling may be used to increase the training dataset size. However, it is not yet fully understood how pooling of heterogeneous data influences the segmentation performance. In this contribution, we investigate the impact of sampling ratios