activity
20162026
most citedOutlier-based Autism Detection using Longitudinal Structural MRI

47 citations · 279 across the 41 of their papers we have counts for

collaborators
Showing 2022Show all

9 papers · 1 filter

eess.IV2022★ 1 cited

Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysis

Dwarikanath Mahapatra

We present a novel framework for explainable labeling and interpretation of medical images. Medical images require specialized professionals for interpretation, and are explained (…

cs.CV2022★ 1 cited

Probabilistic Integration of Object Level Annotations in Chest X-ray Classification

Tom van Sonsbeek, Xiantong Zhen, Dwarikanath Mahapatra +1

Medical image datasets and their annotations are not growing as fast as their equivalents in the general domain. This makes translation from the newest, more data-intensive methods…

eess.IV2022

Leukocyte Classification using Multimodal Architecture Enhanced by Knowledge Distillation

Litao Yang, Deval Mehta, Dwarikanath Mahapatra +1

Recently, a lot of automated white blood cells (WBC) or leukocyte classification techniques have been developed. However, all of these methods only utilize a single modality micros…

eess.IV2022★ 2 cited

Improved Super Resolution of MR Images Using CNNs and Vision Transformers

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…

eess.IV2022★ 2 cited

Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification

Dwarikanath Mahapatra

The success of deep learning has set new benchmarks for many medical image analysis tasks. However, deep models often fail to generalize in the presence of distribution shifts betw…

cs.CV2022★ 2 cited

LifeLonger: A Benchmark for Continual Disease Classification

Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Tom van Sonsbeek +4

Deep learning models have shown a great effectiveness in recognition of findings in medical images. However, they cannot handle the ever-changing clinical environment, bringing new…