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Found 9 papers, 0 papers with code
Self-supervised Landmark Learning with Deformation Reconstruction and Cross-subject Consistency Objectives
We argue that data with complicated deformations can not easily be modeled with point-based registration when only a limited number of points is used to extract influential landmark points.
Interactive Radiotherapy Target Delineation with 3D-Fused Context Propagation
The proposed method is evaluated on two published radiotherapy target contouring datasets of nasopharyngeal and esophageal cancer.
Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network
Specifically, we first utilize a 3D convolutional neural network with ROI-pooling to extract the GTV$_{LN}$'s instance-wise appearance features.
Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy
Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance.
Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification
We focus on the detection and segmentation of oncology-significant (or suspicious cancer metastasized) lymph nodes (OSLNs), which has not been studied before as a computational task.
Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search
This is the goal of our work, where we introduce stratified organ at risk segmentation (SOARS), an approach that stratifies OARs into anchor, mid-level, and small & hard (S&H) categories.
Radiotherapy Target Contouring with Convolutional Gated Graph Neural Network
Instead, inspired by the treating methodology of considering meaningful information across slices, we used Gated Graph Neural Network to frame this problem more efficiently.
Cube Padding for Weakly-Supervised Saliency Prediction in 360° Videos
Then, we concatenate all six faces while utilizing the connectivity between faces on the cube for image padding (i. e., Cube Padding) in convolution, pooling, convolutional LSTM layers.
Cube Padding for Weakly-Supervised Saliency Prediction in 360° Videos
Then, we concatenate all six faces while utilizing the connectivity between faces on the cube for image padding (i. e., Cube Padding) in convolution, pooling, convolutional LSTM layers.
