Search Results for author:
Found 13 papers, 7 papers with code
ExplainFix: Explainable Spatially Fixed Deep Networks
We are first to demonstrate that all spatial filters in state-of-the-art convolutional deep networks can be fixed at initialization, not learned.
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
Colon Nuclei Instance Segmentation using a Probabilistic Two-Stage Detector
Cancer is one of the leading causes of death in the developed world.
AutoFITS: Automatic Feature Engineering for Irregular Time Series
We hypothesise that, in irregular time series, the time at which each observation is collected may be helpful to summarise the dynamics of the data and improve forecasting performance.
Phase diagram for strongly interacting matter in the presence of a magnetic field using the Polyakov-Nambu-Jona-Lasinio model with magnetic field dependent coupling strengths
A study of the magnetic field dependence in the range $eB=0-0. 6~\mathrm{GeV}^2$ of the location of these CEPs reveals that the initial one as well as several of the new ones only survive up to a critical magnetic field.
High Energy Physics - Phenomenology
Learned Pre-Processing for Automatic Diabetic Retinopathy Detection on Eye Fundus Images
Diabetic Retinopathy is the leading cause of blindness in the working-age population of the world.
Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning
Diabetic Retinopathy (DR) is a leading cause of blindness in working age adults.
O-MedAL: Online Active Deep Learning for Medical Image Analysis
Our online method enhances performance of its underlying baseline deep network.
UOLO - automatic object detection and segmentation in biomedical images
We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images.
MedAL: Deep Active Learning Sampling Method for Medical Image Analysis
Active learning techniques can be used to minimize the number of required training labels while maximizing the model's performance. In this work, we propose a novel sampling method that queries the unlabeled examples that maximize the average distance to all training set examples in a learned feature space.
A FFT-based finite-difference solver for massively-parallel direct numerical simulations of turbulent flows
We present an efficient solver for massively-parallel direct numerical simulations of incompressible turbulent flows.
Fluid Dynamics
Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis
Specifically, we apply the \emph{shades of gray} color constancy technique to color-normalize the entire training set of images, while retaining the estimated illuminants.
Towards Adversarial Retinal Image Synthesis
These pairs are then used to learn a mapping from a binary vessel tree to a new retinal image.
