Search Results for author:
Found 14 papers, 3 papers with code
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.
Diffusion-based Holistic Texture Rectification and Synthesis
We present a novel framework for rectifying occlusions and distortions in degraded texture samples from natural images.
Controllable Multi-domain Semantic Artwork Synthesis
To address this problem, we propose a dataset, which we call ArtSem, that contains 40, 000 images of artwork from 4 different domains with their corresponding semantic label maps.
P2Net: A Post-Processing Network for Refining Semantic Segmentation of LiDAR Point Cloud based on Consistency of Consecutive Frames
To remedy this problem, we propose to explicitly train a network to refine these results predicted by an existing segmentation method.
Adaptive occlusion sensitivity analysis for visually explaining video recognition networks
The key idea here is to occlude a specific volume of data by a 3D mask in an input 3D temporal-spatial data space and then measure the change degree in the output score.
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.
DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement
The remastering of vintage film comprises of a diversity of sub-tasks including super-resolution, noise removal, and contrast enhancement which aim to restore the deteriorated film medium to its original state.
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.
Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval
Due to the variance of the images, which include extreme viewpoint changes such as having to retrieve images of the exterior of a landmark from images of the interior, this is very challenging for approaches based exclusively on visual similarity.
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.
Joint Gap Detection and Inpainting of Line Drawings
We evaluate our method qualitatively on a diverse set of challenging line drawings and also provide quantitative results with a user study, where it significantly outperforms the state of the art.
Mastering Sketching: Adversarial Augmentation for Structured Prediction
Our approach augments a simplification network with a discriminator network, training both networks jointly so that the discriminator network discerns whether a line drawing is a real training data or the output of the simplification network, which in turn tries to fool it.
Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification
We present a novel technique to automatically colorize grayscale images that combines both global priors and local image features.
