Search Results for author: Nishant Ravikumar

Found 57 papers, 18 papers with code

Histopathology Foundation Models Enable Accurate Ovarian Cancer Subtype Classification

1 code implementation16 May 2024 Jack Breen, Katie Allen, Kieran Zucker, Lucy Godson, Nicolas M. Orsi, Nishant Ravikumar

Five-class classification performance was evaluated through five-fold cross-validation, and these cross-validation models were ensembled for evaluation on a hold-out test set and an external set from the Transcanadian study.

Multiple Instance Learning whole slide images

Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning

1 code implementation23 Feb 2024 Fengming Lin, Yan Xia, Michael MacRaild, Yash Deo, Haoran Dou, Qiongyao Liu, Kun Wu, Nishant Ravikumar, Alejandro F. Frangi

Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models.

Contrastive Learning Unsupervised Domain Adaptation

Segmenting Cardiac Muscle Z-disks with Deep Neural Networks

no code implementations24 Jan 2024 Mihaela Croitor Ibrahim, Nishant Ravikumar, Alistair Curd, Joanna Leng, Oliver Umney, Michelle Peckham

In this study, we apply deep learning based segmentation models to extract Z-disks in images of striated muscle tissue.


Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping

1 code implementation23 Nov 2023 Jack Breen, Katie Allen, Kieran Zucker, Nicolas M. Orsi, Nishant Ravikumar

Artificial intelligence has found increasing use for ovarian cancer morphological subtyping from histopathology slides, but the optimal magnification for computational interpretation is unclear.

Multiple Instance Learning

Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation

no code implementations18 Nov 2023 Nurbanu Aksoy, Serge Sharoff, Selcuk Baser, Nishant Ravikumar, Alejandro F Frangi

Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images.

Semantic Similarity Semantic Textual Similarity

Radiology Report Generation Using Transformers Conditioned with Non-imaging Data

no code implementations18 Nov 2023 Nurbanu Aksoy, Nishant Ravikumar, Alejandro F Frangi

While recent deep-learning approaches for automated report generation from medical images have seen some success, most studies have relied on image-derived features alone, ignoring non-imaging patient data.

Predicting Ovarian Cancer Treatment Response in Histopathology using Hierarchical Vision Transformers and Multiple Instance Learning

1 code implementation19 Oct 2023 Jack Breen, Katie Allen, Kieran Zucker, Geoff Hall, Nishant Ravikumar, Nicolas M. Orsi

For some therapies, it is not possible to predict patients' responses, potentially exposing them to the adverse effects of treatment without any therapeutic benefit.

Multiple Instance Learning whole slide images

Learned Local Attention Maps for Synthesising Vessel Segmentations

no code implementations24 Aug 2023 Yash Deo, Rodrigo Bonazzola, Haoran Dou, Yan Xia, Tianyou Wei, Nishant Ravikumar, Alejandro F. Frangi, Toni Lassila

We present an encoder-decoder model for synthesising segmentations of the main cerebral arteries in the circle of Willis (CoW) from only T2 MRI.


Shape-guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature

no code implementations13 Aug 2023 Yash Deo, Haoran Dou, Nishant Ravikumar, Alejandro F. Frangi, Toni Lassila

The Circle of Willis (CoW) is the part of cerebral vasculature responsible for delivering blood to the brain.

Generative Adversarial Networks for Stain Normalisation in Histopathology

no code implementations5 Aug 2023 Jack Breen, Kieran Zucker, Katie Allen, Nishant Ravikumar, Nicolas M. Orsi

The rapid growth of digital pathology in recent years has provided an ideal opportunity for the development of artificial intelligence-based tools to improve the accuracy and efficiency of clinical diagnoses.

GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration

1 code implementation26 Jun 2023 Haoran Dou, Ning Bi, Luyi Han, Yuhao Huang, Ritse Mann, Xin Yang, Dong Ni, Nishant Ravikumar, Alejandro F. Frangi, Yunzhi Huang

In this study, we construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyperparameter-free balance on multiple losses.

Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic Review

1 code implementation31 Mar 2023 Jack Breen, Katie Allen, Kieran Zucker, Pratik Adusumilli, Andy Scarsbrook, Geoff Hall, Nicolas M. Orsi, Nishant Ravikumar

The inclusion criteria required that research evaluated AI on histopathology images for diagnostic or prognostic inferences in ovarian cancer.

Survival Prediction

Implicit Visual Bias Mitigation by Posterior Estimate Sharpening of a Bayesian Neural Network

no code implementations29 Mar 2023 Rebecca S Stone, Nishant Ravikumar, Andrew J Bulpitt, David C Hogg

The fairness of a deep neural network is strongly affected by dataset bias and spurious correlations, both of which are usually present in modern feature-rich and complex visual datasets.


Efficient subtyping of ovarian cancer histopathology whole slide images using active sampling in multiple instance learning

1 code implementation17 Feb 2023 Jack Breen, Katie Allen, Kieran Zucker, Geoff Hall, Nicolas M. Orsi, Nishant Ravikumar

Weakly-supervised classification of histopathology slides is a computationally intensive task, with a typical whole slide image (WSI) containing billions of pixels to process.

Classification Multiple Instance Learning +2

Learning disentangled representations for explainable chest X-ray classification using Dirichlet VAEs

no code implementations6 Feb 2023 Rachael Harkness, Alejandro F Frangi, Kieran Zucker, Nishant Ravikumar

We generate visual examples to show that our explainability method, when applied to the trained DirVAE, is able to highlight regions in CXR images that are clinically relevant to the class(es) of interest and additionally, can identify cases where classification relies on spurious feature correlations.

Classification Multi-Label Classification

A Generative Shape Compositional Framework to Synthesise Populations of Virtual Chimaeras

no code implementations4 Oct 2022 Haoran Dou, Seppo Virtanen, Nishant Ravikumar, Alejandro F. Frangi

Specifically, we propose a generative shape compositional framework which comprises two components - a part-aware generative shape model which captures the variability in shape observed for each structure of interest in the training population; and a spatial composition network which assembles/composes the structures synthesised by the former into multi-part shape assemblies (viz.

Anatomy Self-Supervised Learning +1

Three-dimensional micro-structurally informed in silico myocardium -- towards virtual imaging trials in cardiac diffusion weighted MRI

no code implementations22 Aug 2022 Mojtaba Lashgari, Nishant Ravikumar, Irvin Teh, Jing-Rebecca Li, David L. Buckley, Jurgen E. Schneider, Alejandro F. Frangi

We extend previous studies accounting for the cardiomyocyte shape variability, water exchange between the cardiomyocytes (intercalated discs), myocardial microstructure disarray, and four sheetlet orientations.

Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework

1 code implementation30 Jun 2022 Haoran Dou, Luyi Han, Yushuang He, Jun Xu, Nishant Ravikumar, Ritse Mann, Alejandro F. Frangi, Pew-Thian Yap, Yunzhi Huang

Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy.

Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation

no code implementations20 Apr 2022 Rebecca S Stone, Nishant Ravikumar, Andrew J Bulpitt, David C Hogg

While various methods have been proposed to mitigate such bias, the majority require explicit knowledge of the biases present in the training data in order to mitigate.

Face Detection

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

1 code implementation14 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.

Pneumonia Detection

Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images

1 code implementation1 Sep 2021 Jack Breen, Kieran Zucker, Nicolas M. Orsi, Nishant Ravikumar

Two baseline mitosis detection models based on U-Net and RetinaNet were investigated in combination with the aforementioned domain adaptation methods.

Mitosis Detection Style Transfer +2

A Deep Discontinuity-Preserving Image Registration Network

1 code implementation9 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 +1

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.

Model Optimization 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.

Decoder Domain Adaptation +6

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.

Image Segmentation Retinal Vessel Segmentation +1

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) Image Segmentation +5

Dilated deeply supervised networks for hippocampus segmentation in MRI

1 code implementation20 Mar 2019 Lukas Folle, Sulaiman Vesal, Nishant Ravikumar, Andreas Maier

Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimer's Disease (AD).

Hippocampus Segmentation +1

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 Image Segmentation +3

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 Segmentation +1

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.

Organ Segmentation Q-Learning +1

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.

Cannot find the paper you are looking for? You can Submit a new open access paper.