Veronika Cheplygina

research

∙ 03/07/2022

Predicting Bearings' Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry

In the pharmaceutical industry, the maintenance of production machines m...

Dovile Juodelyte, et al. ∙

research

∙ 01/07/2022

Effect of Prior-based Losses on Segmentation Performance: A Benchmark

Today, deep convolutional neural networks (CNNs) have demonstrated state...

Rosana {EL JURDI}, et al. ∙

research

∙ 07/27/2021

ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification

We present ENHANCE, an open dataset with multiple annotations to complem...

Ralf Raumanns, et al. ∙

research

∙ 07/13/2021

Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification

Transfer learning is a commonly used strategy for medical image classifi...

Irma van den Brandt, et al. ∙

research

∙ 04/12/2021

Common Limitations of Image Processing Metrics: A Picture Story

While the importance of automatic image analysis is increasing at an eno...

Annika Reinke, et al. ∙

research

∙ 03/18/2021

How I failed machine learning in medical imaging – shortcomings and recommendations

Medical imaging is an important research field with many opportunities f...

Gaël Varoquaux, et al. ∙

research

∙ 01/12/2021

Using uncertainty estimation to reduce false positives in liver lesion detection

Despite the successes of deep learning techniques at detecting objects i...

Ishaan Bhat, et al. ∙

research

∙ 11/20/2020

Crowdsourcing Airway Annotations in Chest Computed Tomography Images

Measuring airways in chest computed tomography (CT) scans is important f...

Veronika Cheplygina, et al. ∙

research

∙ 11/16/2020

High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey

Today, deep convolutional neural networks (CNNs) have demonstrated state...

Rosana El Jurdia, et al. ∙

research

∙ 06/30/2020

Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning

Early detection of lung cancer has been proven to decrease mortality sig...

Linde S. Hesse, et al. ∙

research

∙ 05/20/2020

Risk of Training Diagnostic Algorithms on Data with Demographic Bias

One of the critical challenges in machine learning applications is to ha...

Samaneh Abbasi-Sureshjani, et al. ∙

research

∙ 04/28/2020

Multi-task Learning with Crowdsourced Features Improves Skin Lesion Diagnosis

Machine learning has a recognised need for large amounts of annotated da...

Ralf Raumanns, et al. ∙

research

∙ 02/25/2019

A Survey of Crowdsourcing in Medical Image Analysis

Rapid advances in image processing capabilities have been seen across ma...

Silas Ørting, et al. ∙

research

∙ 10/12/2018

Cats or CAT scans: transfer learning from natural or medical image source datasets?

Transfer learning is a widely used strategy in medical image analysis. I...

Veronika Cheplygina, et al. ∙

research

∙ 06/21/2018

Characterizing multiple instance datasets

In many pattern recognition problems, a single feature vector is not suf...

Veronika Cheplygina, et al. ∙

research

∙ 06/21/2018

Crowd disagreement of medical images is informative

Classifiers for medical image analysis are often trained with a single c...

Veronika Cheplygina, et al. ∙

research

∙ 06/19/2018

Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans

Supervised feature learning using convolutional neural networks (CNNs) c...

Silas Nyboe Ørting, et al. ∙

research

∙ 04/17/2018

Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Machine learning (ML) algorithms have made a tremendous impact in the fi...

Veronika Cheplygina, et al. ∙

research

∙ 06/12/2017

Exploring the similarity of medical imaging classification problems

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

Veronika Cheplygina, et al. ∙

research

∙ 06/07/2017

Early Experiences with Crowdsourcing Airway Annotations in Chest CT

Measuring airways in chest computed tomography (CT) images is important ...

Veronika Cheplygina, et al. ∙

research

∙ 06/07/2017

Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images

A method for automatically quantifying emphysema regions using High-Reso...

Isabel Pino Peña, et al. ∙

research

∙ 03/15/2017

Label Stability in Multiple Instance Learning

We address the problem of instance label stability in multiple instance ...

Veronika Cheplygina, et al. ∙

research

∙ 03/15/2017

Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners

Supervised learning has been very successful for automatic segmentation ...

Veronika Cheplygina, et al. ∙

research

∙ 03/15/2017

Classification of COPD with Multiple Instance Learning

Chronic obstructive pulmonary disease (COPD) is a lung disease where ear...

Veronika Cheplygina, et al. ∙

research

∙ 01/18/2017

Transfer learning for multi-center classification of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a lung disease which can...

Veronika Cheplygina, et al. ∙

research

∙ 12/11/2016

Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning...

Marc-André Carbonneau, et al. ∙

research

∙ 06/02/2014

On Classification with Bags, Groups and Sets

Many classification problems can be difficult to formulate directly in t...

Veronika Cheplygina, et al. ∙

research

∙ 02/06/2014

Quantile Representation for Indirect Immunofluorescence Image Classification

In the diagnosis of autoimmune diseases, an important task is to classif...

David M. J. Tax, et al. ∙

research

∙ 02/06/2014

Dissimilarity-based Ensembles for Multiple Instance Learning

In multiple instance learning, objects are sets (bags) of feature vector...

Veronika Cheplygina, et al. ∙

research

∙ 09/22/2013

Multiple Instance Learning with Bag Dissimilarities

Multiple instance learning (MIL) is concerned with learning from sets (b...

Veronika Cheplygina, et al. ∙

Veronika Cheplygina

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