1 code implementation • 16 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.
1 code implementation • 23 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.
1 code implementation • 23 Feb 2024 • Fengming Lin, Yan Xia, Michael MacRaild, Yash Deo, Haoran Dou, Qiongyao Liu, Nina Cheng, Nishant Ravikumar, Alejandro F. Frangi
The automated segmentation of cerebral aneurysms is pivotal for accurate diagnosis and treatment planning.
no code implementations • 24 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.
1 code implementation • 23 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.
no code implementations • 22 Nov 2023 • Nicolás Gaggion, Benjamin A. Matheson, Yan Xia, Rodrigo Bonazzola, Nishant Ravikumar, Zeike A. Taylor, Diego H. Milone, Alejandro F. Frangi, Enzo Ferrante
Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function.
no code implementations • 18 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.
no code implementations • 18 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.
1 code implementation • 19 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.
no code implementations • 24 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.
no code implementations • 13 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.
1 code implementation • 7 Aug 2023 • Fengming Lin, Yan Xia, Nishant Ravikumar, Qiongyao Liu, Michael MacRaild, Alejandro F Frangi
Accurate segmentation of brain vessels is crucial for cerebrovascular disease diagnosis and treatment.
no code implementations • 5 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.
no code implementations • 26 Jun 2023 • Haoran Dou, Nishant Ravikumar, Alejandro F. Frangi
The generation of virtual populations (VPs) of anatomy is essential for conducting in silico trials of medical devices.
1 code implementation • 26 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.
1 code implementation • 31 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.
no code implementations • 29 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.
1 code implementation • 17 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.
no code implementations • 6 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.
no code implementations • 7 Jan 2023 • Rodrigo Bonazzola, Enzo Ferrante, Nishant Ravikumar, Yan Xia, Bernard Keavney, Sven Plein, Tanveer Syeda-Mahmood, Alejandro F Frangi
Here, we propose a new framework for gene discovery entitled Unsupervised Phenotype Ensembles (UPE).
1 code implementation • 24 Nov 2022 • Xiang Chen, Yan Xia, Nishant Ravikumar, Alejandro F Frangi
In such scenarios, enforcing smooth, globally continuous deformation fields leads to incorrect/implausible registration results.
no code implementations • 4 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.
no code implementations • 22 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.
no code implementations • 1 Jul 2022 • Yuxin Zou, Haoran Dou, Yuhao Huang, Xin Yang, Jikuan Qian, Chaojiong Zhen, Xiaodan Ji, Nishant Ravikumar, Guoqiang Chen, Weijun Huang, Alejandro F. Frangi, Dong Ni
First, we formulate SP localization in 3D US as a tangent-point-based problem in RL to restructure the action space and significantly reduce the search space.
1 code implementation • 30 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.
no code implementations • 20 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.
no code implementations • 6 Apr 2022 • Marc Aubreville, Nikolas Stathonikos, Christof A. Bertram, Robert Klopleisch, Natalie ter Hoeve, Francesco Ciompi, Frauke Wilm, Christian Marzahl, Taryn A. Donovan, Andreas Maier, Jack Breen, Nishant Ravikumar, Youjin Chung, Jinah Park, Ramin Nateghi, Fattaneh Pourakpour, Rutger H. J. Fick, Saima Ben Hadj, Mostafa Jahanifar, Nasir Rajpoot, Jakob Dexl, Thomas Wittenberg, Satoshi Kondo, Maxime W. Lafarge, Viktor H. Koelzer, Jingtang Liang, YuBo Wang, Xi Long, Jingxin Liu, Salar Razavi, April Khademi, Sen yang, Xiyue Wang, Mitko Veta, Katharina Breininger
The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms.
1 code implementation • 14 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.
1 code implementation • 1 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.
no code implementations • 11 Aug 2021 • Shuangchi He, Zehui Lin, Xin Yang, Chaoyu Chen, Jian Wang, Xue Shuang, Ziwei Deng, Qin Liu, Yan Cao, Xiduo Lu, Ruobing Huang, Nishant Ravikumar, Alejandro Frangi, Yuanji Zhang, Yi Xiong, Dong Ni
In this study, we build a novel multi-label learning (MLL) scheme to identify multiple standard planes and corresponding anatomical structures of fetus simultaneously.
no code implementations • 2 Aug 2021 • Yuhao Huang, Xin Yang, Yuxin Zou, Chaoyu Chen, Jian Wang, Haoran Dou, Nishant Ravikumar, Alejandro F Frangi, Jianqiao Zhou, Dong Ni
Weakly-supervised segmentation (WSS) can help reduce time-consuming and cumbersome manual annotation.
no code implementations • 1 Aug 2021 • Zhendong Liu, Van Manh, Xin Yang, Xiaoqiong Huang, Karim Lekadir, Víctor Campello, Nishant Ravikumar, Alejandro F Frangi, Dong Ni
A style transfer model with style fusion is employed to generate the curriculum samples.
no code implementations • 31 Jul 2021 • Mingyuan Luo, Xin Yang, Xiaoqiong Huang, Yuhao Huang, Yuxin Zou, Xindi Hu, Nishant Ravikumar, Alejandro F Frangi, Dong Ni
In this paper, we propose a novel approach to sensorless freehand 3D US reconstruction considering the complex skill sequences.
1 code implementation • 9 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.
no code implementations • 11 Jun 2021 • Xiang Chen, Yan Xia, Nishant Ravikumar, Alejandro F Frangi
Image registration is a fundamental building block for various applications in medical image analysis.
1 code implementation • 15 Mar 2021 • Sulaiman Vesal, Mingxuan Gu, Ronak Kosti, Andreas Maier, Nishant Ravikumar
The proposed method is based on adversarial learning and adapts network features between source and target domain in different spaces.
1 code implementation • 24 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.
no code implementations • 1 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.
no code implementations • 6 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.
no code implementations • 22 Jun 2020 • Xiahai Zhuang, Jiahang Xu, Xinzhe Luo, Chen Chen, Cheng Ouyang, Daniel Rueckert, Victor M. Campello, Karim Lekadir, Sulaiman Vesal, Nishant Ravikumar, Yashu Liu, Gongning Luo, Jingkun Chen, Hongwei Li, Buntheng Ly, Maxime Sermesant, Holger Roth, Wentao Zhu, Jiexiang Wang, Xinghao Ding, Xinyue Wang, Sen yang, Lei LI
In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR.
1 code implementation • 26 Apr 2020 • Zhaohan Xiong, Qing Xia, Zhiqiang Hu, Ning Huang, Cheng Bian, Yefeng Zheng, Sulaiman Vesal, Nishant Ravikumar, Andreas Maier, Xin Yang, Pheng-Ann Heng, Dong Ni, Caizi Li, Qianqian Tong, Weixin Si, Elodie Puybareau, Younes Khoudli, Thierry Geraud, Chen Chen, Wenjia Bai, Daniel Rueckert, Lingchao Xu, Xiahai Zhuang, Xinzhe Luo, Shuman Jia, Maxime Sermesant, Yashu Liu, Kuanquan Wang, Davide Borra, Alessandro Masci, Cristiana Corsi, Coen de Vente, Mitko Veta, Rashed Karim, Chandrakanth Jayachandran Preetha, Sandy Engelhardt, Menyun Qiao, Yuanyuan Wang, Qian Tao, Marta Nunez-Garcia, Oscar Camara, Nicolo Savioli, Pablo Lamata, Jichao Zhao
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
no code implementations • 7 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.
no code implementations • 4 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.
no code implementations • 19 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.
no code implementations • 12 Dec 2019 • Felix Denzinger, Michael Wels, Nishant Ravikumar, Katharina Breininger, Anika Reidelshöfer, Joachim Eckert, Michael Sühling, Axel Schmermund, Andreas Maier
A second approach is based on deep learning and relies on centerline extraction as sole prerequisite.
no code implementations • 21 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.
no code implementations • 14 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.
no code implementations • 19 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.
1 code implementation • 20 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).
no code implementations • 5 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.
no code implementations • 5 Aug 2018 • Sulaiman Vesal, Shreyas Malakarjun Patil, Nishant Ravikumar, Andreas Maier
This underlines the need for an accurate and automatic approach to skin lesion segmentation.
no code implementations • 25 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.
no code implementations • 14 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.
no code implementations • 26 Feb 2018 • Sulaiman Vesal, Nishant Ravikumar, Amirabbas Davari, Stephan Ellmann, Andreas Maier
Breast cancer is one of the leading causes of mortality in women.
Classification Classification Of Breast Cancer Histology Images +3
no code implementations • 23 Feb 2018 • Sulaiman Vesal, Nishant Ravikumar, Stephan Ellman, Andreas Maier
Accurate segmentation of breast lesions is a crucial step in evaluating the characteristics of tumors.
no code implementations • 14 Dec 2017 • Sulaiman Vesal, Andres Diaz-Pinto, Nishant Ravikumar, Stephan Ellmann, Amirabbas Davari, Andreas Maier
The proposed method achieved an average Dice coefficient of 0. 7808$\pm$0. 1729 and Jaccard index of 0. 6704$\pm$0. 2167.
no code implementations • 9 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.