no code implementations • 27 Mar 2024 • Anees Ur Rehman Hashmi, Dwarikanath Mahapatra, Mohammad Yaqub
Explaining Deep Learning models is becoming increasingly important in the face of daily emerging multimodal models, particularly in safety-critical domains like medical imaging.
1 code implementation • 14 Mar 2024 • Anees Ur Rehman Hashmi, Ibrahim Almakky, Mohammad Areeb Qazi, Santosh Sanjeev, Vijay Ram Papineni, Dwarikanath Mahapatra, Mohammad Yaqub
Large-scale generative models have demonstrated impressive capacity in producing visually compelling images, with increasing applications in medical imaging.
2 code implementations • 5 Jan 2024 • Siyuan Yan, Chi Liu, Zhen Yu, Lie Ju, Dwarikanath Mahapatra, Brigid Betz-Stablein, Victoria Mar, Monika Janda, Peter Soyer, ZongYuan Ge
To address these challenges, we propose a novel DG framework for medical image classification without relying on domain labels, called Prompt-driven Latent Domain Generalization (PLDG).
no code implementations • 10 Nov 2023 • Steven Korevaar, Ruwan Tennakoon, Ricky O'Brien, Dwarikanath Mahapatra, Alireza Bab-Hadiasha
This paper demonstrates that by using privileged information to predict the severity of intra-layer retinal fluid in optical coherence tomography scans, the classification accuracy of a deep learning model operating on out-of-distribution data improves from $0. 911$ to $0. 934$.
no code implementations • 24 Jul 2023 • Behzad Bozorgtabar, Dwarikanath Mahapatra, Jean-Philippe Thiran
Inspired by a modern self-supervised vision transformer model trained using partial image inputs to reconstruct missing image regions -- we propose AMAE, a two-stage algorithm for adaptation of the pre-trained masked autoencoder (MAE).
1 code implementation • 1 May 2023 • Litao Yang, Deval Mehta, Sidong Liu, Dwarikanath Mahapatra, Antonio Di Ieva, ZongYuan Ge
Due to the high resolution of the WSI and the unavailability of patch-level annotations, WSI classification is usually formulated as a weakly supervised problem, which relies on multiple instance learning (MIL) based on patches of a WSI.
no code implementations • CVPR 2023 • Siyuan Yan, Zhen Yu, Xuelin Zhang, Dwarikanath Mahapatra, Shekhar S. Chandra, Monika Janda, Peter Soyer, ZongYuan Ge
We introduce a human-in-the-loop framework in the model training process such that users can observe and correct the model's decision logic when confounding behaviors happen.
no code implementations • 16 Nov 2022 • Dwarikanath Mahapatra
Apart from enabling transparent medical image labeling and interpretation, we achieve report and image-based labeling comparable to prior methods, including state-of-the-art performance in some cases as evidenced by experiments on the Indiana Chest X-ray dataset
no code implementations • 13 Oct 2022 • Tom van Sonsbeek, XianTong Zhen, Dwarikanath Mahapatra, Marcel Worring
This shows how two-stage learning of labels from coarse to fine-grained, in particular with object level annotations, is an effective method for more optimal annotation usage.
no code implementations • 17 Aug 2022 • Litao Yang, Deval Mehta, Dwarikanath Mahapatra, ZongYuan Ge
Our unique contribution is two-fold - 1) We present a first of its kind multimodal WBC dataset for WBC classification; 2) We develop a high performing multimodal architecture which is also efficient and low in complexity at the same time.
no code implementations • 24 Jul 2022 • Dwarikanath Mahapatra
State of the art magnetic resonance (MR) image super-resolution methods (ISR) using convolutional neural networks (CNNs) leverage limited contextual information due to the limited spatial coverage of CNNs.
no code implementations • 27 Jun 2022 • Dwarikanath Mahapatra
The success of deep learning has set new benchmarks for many medical image analysis tasks.
1 code implementation • 12 Apr 2022 • Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Tom van Sonsbeek, XianTong Zhen, Dwarikanath Mahapatra, Marcel Worring, Cees G. M. Snoek
Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
no code implementations • 4 Apr 2022 • Dwarikanath Mahapatra
Using a simpler architecture, our method matches a state of the art SSL based GZSL method for natural images and outperforms all methods for medical images.
no code implementations • 21 Feb 2022 • Devika K, Venkata Ramana Murthy Oruganti, Dwarikanath Mahapatra, Ramanathan Subramanian
Among other findings, metrics employed for model training as well as reconstruction loss computation impact detection performance, and the coronal modality is found to best encode structural information for ASD detection.
no code implementations • 27 Jan 2022 • Sourya Dipta Das, Saikat Dutta, Nisarg A. Shah, Dwarikanath Mahapatra, ZongYuan Ge
Convolutional Neural Network models have successfully detected retinal illness from optical coherence tomography (OCT) and fundus images.
no code implementations • 15 Nov 2021 • Dwarikanath Mahapatra
Gleason grading from histopathology images is essential for accurate prostate cancer (PCa) diagnosis.
no code implementations • 1 Oct 2021 • Dwarikanath Mahapatra
Segmentation of Prostate Cancer (PCa) tissues from Gleason graded histopathology images is vital for accurate diagnosis.
no code implementations • 15 Jun 2021 • Dwarikanath Mahapatra, Ankur Singh
While medical image segmentation is an important task for computer aided diagnosis, the high expertise requirement for pixelwise manual annotations makes it a challenging and time consuming task.
no code implementations • 25 May 2021 • Shiba Kuanar, Dwarikanath Mahapatra, Vassilis Athitsos, K. R Rao
To achieve higher coding efficiency, Versatile Video Coding (VVC) includes several novel components, but at the expense of increasing decoder computational complexity.
no code implementations • 28 Apr 2021 • Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, Anand Rajan
Clear cell renal cell carcinoma (ccRCC) is one of the most common forms of intratumoral heterogeneity in the study of renal cancer.
no code implementations • 22 Apr 2021 • Lie Ju, Xin Wang, Lin Wang, Tongliang Liu, Xin Zhao, Tom Drummond, Dwarikanath Mahapatra, ZongYuan Ge
For example, there are estimated more than 40 different kinds of retinal diseases with variable morbidity, however with more than 30+ conditions are very rare from the global patient cohorts, which results in a typical long-tailed learning problem for deep learning-based screening models.
no code implementations • 13 Apr 2021 • Dwarikanath Mahapatra
We demonstrate the benefits of the proposed approach, termed Interpretability-Driven Sample Selection (IDEAL), in an active learning setup aimed at lung disease classification and histopathology image segmentation.
no code implementations • 28 Feb 2021 • Lie Ju, Xin Wang, Lin Wang, Dwarikanath Mahapatra, Xin Zhao, Mehrtash Harandi, Tom Drummond, Tongliang Liu, ZongYuan Ge
In this paper, we systematically discuss and define the two common types of label noise in medical images - disagreement label noise from inconsistency expert opinions and single-target label noise from wrong diagnosis record.
no code implementations • 19 Oct 2020 • Behzad Bozorgtabar, Dwarikanath Mahapatra, Guillaume Vray, Jean-Philippe Thiran
Deep anomaly detection models using a supervised mode of learning usually work under a closed set assumption and suffer from overfitting to previously seen rare anomalies at training, which hinders their applicability in a real scenario.
no code implementations • 5 Aug 2020 • Dwarikanath Mahapatra, Behzad Bozorgtabar, Jean-Philippe Thiran, Ling Shao
Although generative adversarial network (GAN) based style transfer is state of the art in histopathology color-stain normalization, they do not explicitly integrate structural information of tissues.
no code implementations • 4 Jul 2020 • Dwarikanath Mahapatra
Registration is an important part of many clinical workflows and factually, including information of structures of interest improves registration performance.
no code implementations • CVPR 2020 • Dwarikanath Mahapatra, Behzad Bozorgtabar, Jean-Philippe Thiran, Ling Shao
The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
no code implementations • 24 Mar 2020 • Lie Ju, Xin Wang, Xin Zhao, Huimin Lu, Dwarikanath Mahapatra, Paul Bonnington, ZongYuan Ge
In addition, we conduct additional experiments to show the effectiveness of SALL from the aspects of reliability and interpretability in the context of medical imaging application.
no code implementations • 18 Oct 2019 • Dwarikanath Mahapatra
This is achieved by unsupervised domain adaptation in the registration process and allows for easier application to different datasets without extensive retraining. To achieve our objective we train a network that transforms the given input image pair to a latent feature space vector using autoencoders.
1 code implementation • 11 Oct 2019 • Yunyan Xing, ZongYuan Ge, Rui Zeng, Dwarikanath Mahapatra, Jarrel Seah, Meng Law, Tom Drummond
We demonstrate the effectiveness of our model on two tasks: (i) we invite certified radiologists to assess the quality of the generated synthetic images against real and other state-of-the-art generative models, and (ii) data augmentation to improve the performance of disease localisation.
no code implementations • 23 Sep 2019 • Mukesh Saini, Benjamin Guthier, Hao Kuang, Dwarikanath Mahapatra, Abdulmotaleb El Saddik
While viewing on a mobile device, a user can manually zoom into this high resolution video to get more detailed view of objects and activities.
no code implementations • 6 Jul 2019 • Dwarikanath Mahapatra
We propose a method to predict severity of age related macular degeneration (AMD) from input optical coherence tomography (OCT) images.
no code implementations • 24 Apr 2019 • Behzad Bozorgtabar, Dwarikanath Mahapatra, Hendrik von Teng, Alexander Pollinger, Lukas Ebner, Jean-Phillipe Thiran, Mauricio Reyes
Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity.
no code implementations • 25 Mar 2019 • Dwarikanath Mahapatra, ZongYuan Ge
Registration is an important task in automated medical image analysis.
no code implementations • 6 Feb 2019 • Dwarikanath Mahapatra, Behzad Bozorgtabar
Our primary contribution is in proposing a multistage model where the output image quality of one stage is progressively improved in the next stage by using a triplet loss function.
no code implementations • 3 Feb 2019 • Shiba Kuanar, K. R. Rao, Dwarikanath Mahapatra, Monalisa Bilas
The night haze removal is a severely ill-posed problem especially due to the presence of various visible light sources with varying colors and non-uniform illumination.
1 code implementation • 12 Sep 2018 • Suman Sedai, Bhavna Antony, Dwarikanath Mahapatra, Rahil Garnavi
Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases.
no code implementations • 22 Aug 2018 • Suman Sedai, Dwarikanath Mahapatra, ZongYuan Ge, Rajib Chakravorty, Rahil Garnavi
We propose a novel weakly supervised method to localize chest pathologies using class aware deep multiscale feature learning.
no code implementations • 19 Jul 2018 • Zongyuan Ge, Dwarikanath Mahapatra, Suman Sedai, Rahil Garnavi, Rajib Chakravorty
In this work we have proposed a novel error function, Multi-label Softmax Loss (MSML), to specifically address the properties of multiple labels and imbalanced data.
no code implementations • 14 Jun 2018 • Dwarikanath Mahapatra, Behzad Bozorgtabar, Jean-Philippe Thiran, Mauricio Reyes
Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity.
no code implementations • 7 May 2018 • Dwarikanath Mahapatra
Conventional approaches to image registration consist of time consuming iterative methods.
no code implementations • 13 Oct 2017 • Dwarikanath Mahapatra, Behzad Bozorgtabar
We propose an image super resolution(ISR) method using generative adversarial networks (GANs) that takes a low resolution input fundus image and generates a high resolution super resolved (SR) image upto scaling factor of $16$.
no code implementations • 7 Dec 2016 • Dwarikanath Mahapatra
A novel approach is proposed that obtains consensus segmentations from experts using graph cuts (GC) and semi supervised learning (SSL).