Search Results for author: Nishant Ravikumar

Found 30 papers, 3 papers with code

The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays

no code implementations14 Sep 2021 Rachael Harkness, Geoff Hall, Alejandro F Frangi, Nishant Ravikumar, Kieran Zucker

Model performance results have been exceptional when training and testing on open-source data, surpassing the reported capabilities of AI in pneumonia-detection prior to the COVID-19 outbreak.

Classification Pneumonia Detection

A Deep Discontinuity-Preserving Image Registration Network

no code implementations9 Jul 2021 Xiang Chen, Nishant Ravikumar, Yan Xia, Alejandro F Frangi

Image registration aims to establish spatial correspondence across pairs, or groups of images, and is a cornerstone of medical image computing and computer-assisted-interventions.

Image Registration Medical Image Registration

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

Fed-Sim: Federated Simulation for Medical Imaging

no code implementations1 Sep 2020 Daiqing Li, Amlan Kar, Nishant Ravikumar, Alejandro F. Frangi, Sanja Fidler

Since the model of geometry and material is disentangled from the imaging sensor, it can effectively be trained across multiple medical centers.

Federated Learning

Partially Conditioned Generative Adversarial Networks

no code implementations6 Jul 2020 Francisco J. Ibarrola, Nishant Ravikumar, Alejandro F. Frangi

With the introduction of Conditional GANs and their variants, these methods were extended to generating samples conditioned on ancillary information available for each sample within the dataset.

Image Generation

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

Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach

no code implementations19 Dec 2019 Siming Bayer, Xia Zhong, Weilin Fu, Nishant Ravikumar, Andreas Maier

In this work, we propose an imitation learning framework for the registration of 2D color funduscopic images for a wide range of applications such as disease monitoring, image stitching and super-resolution.

Image Registration Image Stitching +2

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 Divide-and-Conquer Approach towards Understanding Deep Networks

no code implementations14 Jul 2019 Weilin Fu, Katharina Breininger, Roman Schaffert, Nishant Ravikumar, Andreas Maier

We start with a high-performance U-Net and show by step-by-step conversion that we are able to divide the network into modules of known operators.

Retinal Vessel Segmentation

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

Action Learning for 3D Point Cloud Based Organ Segmentation

no code implementations14 Jun 2018 Xia Zhong, Mario Amrehn, Nishant Ravikumar, Shuqing Chen, Norbert Strobel, Annette Birkhold, Markus Kowarschik, Rebecca Fahrig, Andreas Maier

From this we conclude that our method is robust, and we believe that our method can be successfully applied to many more applications, in particular, in the interventional imaging space.

Q-Learning Transfer Learning

Frangi-Net: A Neural Network Approach to Vessel Segmentation

no code implementations9 Nov 2017 Weilin Fu, Katharina Breininger, Tobias Würfl, Nishant Ravikumar, Roman Schaffert, Andreas Maier

In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network ("Frangi-Net"), and illustrate that the Frangi-Net is equivalent to the original Frangi filter.

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