Mitko Veta | DeepAI

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∙ 06/05/2023

A Deep Learning Approach Utilizing Covariance Matrix Analysis for the ISBI Edited MRS Reconstruction Challenge

This work proposes a method to accelerate the acquisition of high-qualit...

0 Julian P. Merkofer, et al. ∙

∙ 01/16/2023

LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset

We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held ...

3 Yiping Jiao, et al. ∙

∙ 09/27/2021

Optimized Automated Cardiac MR Scar Quantification with GAN-Based Data Augmentation

Background: The clinical utility of late gadolinium enhancement (LGE) ca...

6 Didier R. P. R. M. Lustermans, et al. ∙

∙ 02/15/2021

Corneal Pachymetry by AS-OCT after Descemet's Membrane Endothelial Keratoplasty

Corneal thickness (pachymetry) maps can be used to monitor restoration o...

0 Friso G. Heslinga, et al. ∙

∙ 11/25/2020

Physics-informed neural networks for myocardial perfusion MRI quantification

Tracer-kinetic models allow for the quantification of kinetic parameters...

19 Rudolf L. M. van Herten, et al. ∙

∙ 10/07/2020

Deep Learning-Based Grading of Ductal Carcinoma In Situ in Breast Histopathology Images

Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can...

0 Suzanne C Wetstein, et al. ∙

∙ 08/26/2020

Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation

Cine cardiac magnetic resonance (CMR) has become the gold standard for t...

0 Cian M. Scannell, et al. ∙

∙ 08/26/2020

Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology

Unsupervised learning enables modeling complex images without the need f...

0 Maxime W. Lafarge, et al. ∙

∙ 04/24/2020

Quantifying Graft Detachment after Descemet's Membrane Endothelial Keratoplasty with Deep Convolutional Neural Networks

Purpose: We developed a method to automatically locate and quantify graf...

0 Friso G. Heslinga, et al. ∙

∙ 02/20/2020

Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis

Rotation-invariance is a desired property of machine-learning models for...

61 Maxime W. Lafarge, et al. ∙

∙ 11/22/2019

Direct Classification of Type 2 Diabetes From Retinal Fundus Images in a Population-based Sample From The Maastricht Study

Type 2 Diabetes (T2D) is a chronic metabolic disorder that can lead to b...

0 Friso G. Heslinga, et al. ∙

∙ 10/31/2019

Deep learning assessment of breast terminal duct lobular unit involution: towards automated prediction of breast cancer risk

Terminal ductal lobular unit (TDLU) involution is the regression of milk...

11 Suzanne C Wetstein, et al. ∙

∙ 09/05/2019

Intensity augmentation for domain transfer of whole breast segmentation in MRI

The segmentation of the breast from the chest wall is an important first...

0 Linde S. Hesse, et al. ∙

∙ 07/27/2019

Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI

The quantification of myocardial perfusion MRI has the potential to prov...

0 Cian M. Scannell, et al. ∙

∙ 07/22/2018

Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge

Tumor proliferation is an important biomarker indicative of the prognosi...

0 Mitko Veta, et al. ∙

∙ 04/10/2018

Roto-Translation Covariant Convolutional Networks for Medical Image Analysis

We propose a framework for rotation and translation covariant deep learn...

0 Erik J. Bekkers, et al. ∙

∙ 01/10/2018

Inferring a Third Spatial Dimension from 2D Histological Images

Histological images are obtained by transmitting light through a tissue ...

0 Maxime W. Lafarge, et al. ∙

∙ 07/19/2017

Domain-adversarial neural networks to address the appearance variability of histopathology images

Preparing and scanning histopathology slides consists of several steps, ...

0 Maxime W. Lafarge, et al. ∙

∙ 07/11/2017

Adversarial training and dilated convolutions for brain MRI segmentation

Convolutional neural networks (CNNs) have been applied to various automa...

0 Pim Moeskops, et al. ∙

∙ 06/12/2017

Exploring the similarity of medical imaging classification problems

Supervised learning is ubiquitous in medical image analysis. In this pap...

0 Veronika Cheplygina, et al. ∙

∙ 06/20/2016

Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation

The size of nuclei in histological preparations from excised breast tumo...

0 Mitko Veta, et al. ∙

∙ 11/21/2014

Assessment of algorithms for mitosis detection in breast cancer histopathology images

The proliferative activity of breast tumors, which is routinely estimate...

0 Mitko Veta, et al. ∙

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