Search Results for author:
Found 9 papers, 0 papers with code
Data-Dependent Higher-Order Clique Selection for Artery-Vein Segmentation by Energy Minimization
We propose a novel segmentation method based on energy minimization of higher-order potentials.
Segmentation of Kidney Tumors on Non-Contrast CT Images using Protuberance Detection Network
In this paper, we present a novel framework, which can explicitly capture protruded regions in kidneys to enable a better segmentation of kidney tumors.
Visual Grounding of Whole Radiology Reports for 3D CT Images
Our framework combines two components of 1) anatomical segmentation of images, and 2) report structuring.
Image Synthesis-based Late Stage Cancer Augmentation and Semi-Supervised Segmentation for MRI Rectal Cancer Staging
To tackle this, we propose two kinds of approaches of image synthesis-based late stage cancer augmentation and semi-supervised learning which is designed for T-stage prediction.
Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks
The input to the second networks have an auxiliary channel in addition to the 3D CT images.
TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling
To the best of our knowledge, this is the first deep learning based approach which learns multi-label tree structure connectivity from images.
Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval
In this paper, we present a novel architecture based on conditional Generative Adversarial Networks (cGANs) with the goal of generating high resolution images of main body parts including head, chest, abdomen and legs.
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection
Accurate Computer-Assisted Diagnosis, relying on large-scale annotated pathological images, can alleviate the risk of overlooking the diagnosis.
Computation of Total Kidney Volume from CT images in Autosomal Dominant Polycystic Kidney Disease using Multi-Task 3D Convolutional Neural Networks
Autosomal Dominant Polycystic Kidney Disease (ADPKD) characterized by progressive growth of renal cysts is the most prevalent and potentially lethal monogenic renal disease, affecting one in every 500-100 people.
