Search Results for author:
Found 15 papers, 2 papers with code
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.
A feature agnostic approach for glaucoma detection in OCT volumes
Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly used for the diagnosis and monitoring of glaucoma.
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.
Higher-Order Clique Reduction Without Auxiliary Variables
While the method does not reduce all terms, it can be used with existing techniques that transformsarbitrary terms (by introducing auxiliary variables) and improve the speed.
Fashion Style in 128 Floats: Joint Ranking and Classification Using Weak Data for Feature Extraction
We propose a novel approach for learning features from weakly-supervised data by joint ranking and classification.
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.
Inference of visual field test performance from OCT volumes using deep learning
Visual field tests (VFT) are pivotal for glaucoma diagnosis and conducted regularly to monitor disease progression.
Dueling Deep Q-Network for Unsupervised Inter-frame Eye Movement Correction in Optical Coherence Tomography Volumes
We also compare our model with elastix intensity based medical image registration approach, where significant improvement is achieved by our model for both noisy and denoised volumes.
Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography
Optical Coherence Tomography (OCT) is pervasive in both the research and clinical practice of Ophthalmology.
Uncertainty guided semi-supervised segmentation of retinal layers in OCT images
First, a teacher segmentation model is trained from the labeled samples using Bayesian deep learning.
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.
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.
