Search Results for author: Sulaiman Vesal

Found 17 papers, 3 papers with code

Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification

1 code implementation24 Dec 2020 Sulaiman Vesal, Mingxuan Gu, Andreas Maier, Nishant Ravikumar

In this paper, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardiac LV morphology, regional-wall thickness (RWT), and additionally detecting the cardiac phase cycle (systole and diastole) for a given 3D Cine-magnetic resonance (MR) image sequence.

Multi-Task Learning

The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks

no code implementations7 Feb 2020 Faezeh Nejati Hatamian, Nishant Ravikumar, Sulaiman Vesal, Felix P. Kemeth, Matthias Struck, Andreas Maier

In this study, we investigate the impact of various data augmentation algorithms, e. g., oversampling, Gaussian Mixture Models (GMMs) and Generative Adversarial Networks (GANs), on solving the class imbalance problem.

Classification Data Augmentation +2

COPD Classification in CT Images Using a 3D Convolutional Neural Network

no code implementations4 Jan 2020 Jalil Ahmed, Sulaiman Vesal, Felix Durlak, Rainer Kaergel, Nishant Ravikumar, Martine Remy-Jardin, Andreas Maier

Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world.

Classification Computed Tomography (CT) +2

Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation

no code implementations11 Dec 2019 Dominik Eckert, Sulaiman Vesal, Ludwig Ritschl, Steffen Kappler, Andreas Maier

In this study, we propose a deep learning method based on Convolutional Neural Networks (CNNs) for mammogram denoising to improve the image quality.

Data Augmentation Denoising

Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation

no code implementations21 Aug 2019 Sulaiman Vesal, Nishant Ravikumar, Andreas Maier

We first train an encoder-decoder CNN on T2-weighted and balanced-Steady State Free Precession (bSSFP) MR images with pixel-level annotation and fine-tune the same network with a limited number of Late Gadolinium Enhanced-MR (LGE-MR) subjects, to adapt the domain features.

Domain Adaptation Left Ventricle Segmentation +2

A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT

no code implementations19 May 2019 Sulaiman Vesal, Nishant Ravikumar, Andreas Maier

Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is an essential part of planning effective treatment strategies to combat lung and esophageal cancer.

Computed Tomography (CT) Medical Image Segmentation

Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI

no code implementations5 Aug 2018 Sulaiman Vesal, Nishant Ravikumar, Andreas Maier

We employ a 3D fully convolutional network, with dilated convolutions in the lowest level of the network, and residual connections between encoder blocks to incorporate local and global knowledge.

Domain Adaptation Medical Image Segmentation

SkinNet: A Deep Learning Framework for Skin Lesion Segmentation

no code implementations25 Jun 2018 Sulaiman Vesal, Nishant Ravikumar, Andreas Maier

Early detection and segmentation of skin lesions are crucial for timely diagnosis and treatment, necessary to improve the survival rate of patients.

Lesion Segmentation

Hyper-Hue and EMAP on Hyperspectral Images for Supervised Layer Decomposition of Old Master Drawings

no code implementations29 Jan 2018 AmirAbbas Davari, Nikolaos Sakaltras, Armin Haeberle, Sulaiman Vesal, Vincent Christlein, Andreas Maier, Christian Riess

In this work, we propose an image processing pipeline that operates on hyperspectral images to separate such layers.

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