activity
20182021
most citedChest X-rays Classification: A Multi-Label and Fine-Grained Problem

26 citations · 51 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2021

Uncertainty guided semi-supervised segmentation of retinal layers in OCT images

Suman Sedai, Bhavna Antony, Ravneet Rai +5

Deep convolutional neural networks have shown outstanding performance in medical image segmentation tasks. The usual problem when training supervised deep learning methods is the l…

eess.IV2021★ 4 cited

Analyzing Epistemic and Aleatoric Uncertainty for Drusen Segmentation in Optical Coherence Tomography Images

Tinu Theckel Joy, Suman Sedai, Rahil Garnavi

Age-related macular degeneration (AMD) is one of the leading causes of permanent vision loss in people aged over 60 years. Accurate segmentation of biomarkers such as drusen that p…

cs.LG2020

Dueling Deep Q-Network for Unsupervised Inter-frame Eye Movement Correction in Optical Coherence Tomography Volumes

Yasmeen M. George, Suman Sedai, Bhavna J. Antony +4

In optical coherence tomography (OCT) volumes of retina, the sequential acquisition of the individual slices makes this modality prone to motion artifacts, misalignments between ad…

q-bio.PE2020

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young +93

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international…

cs.CV2018★ 4 cited

Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning

Suman Sedai, Bhavna Antony, Dwarikanath Mahapatra +1

Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases. In this paper, we propose a method for retinal layer segmentation a…

cs.CV2018★ 2 cited

Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in X-ray images

Suman Sedai, Dwarikanath Mahapatra, Zongyuan Ge +2

Localization of chest pathologies in chest X-ray images is a challenging task because of their varying sizes and appearances. We propose a novel weakly supervised method to localiz…

cs.CV2018★ 26 cited

Chest X-rays Classification: A Multi-Label and Fine-Grained Problem

Zongyuan Ge, Dwarikanath Mahapatra, Suman Sedai +2

The widely used ChestX-ray14 dataset addresses an important medical image classification problem and has the following caveats: 1) many lung pathologies are visually similar, 2) a…

cs.CV2018★ 15 cited

GAN Based Medical Image Registration

Dwarikanath Mahapatra

Conventional approaches to image registration consist of time consuming iterative methods. Most current deep learning (DL) based registration methods extract deep features to use i…