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20202026
most citedExploring Overcomplete Representations for Single Image Deraining using CNNs

37 citations · 130 across the 18 of their papers we have counts for

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6 papers · 1 filter

eess.IV2024

Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis

Eva Prakash, Jeya Maria Jose Valanarasu, Zhihong Chen +9

Purpose: To explore best-practice approaches for generating synthetic chest X-ray images and augmenting medical imaging datasets to optimize the performance of deep learning models…

eess.IV2024★ 2 cited

Unlocking Robust Segmentation Across All Age Groups via Continual Learning

Chih-Ying Liu, Jeya Maria Jose Valanarasu, Camila Gonzalez +3

Most deep learning models in medical imaging are trained on adult data with unclear performance on pediatric images. In this work, we aim to address this challenge in the context o…

eess.IV2022★ 1 cited

Simultaneous Bone and Shadow Segmentation Network using Task Correspondence Consistency

Aimon Rahman, Jeya Maria Jose Valanarasu, Ilker Hacihaliloglu +1

Segmenting both bone surface and the corresponding acoustic shadow are fundamental tasks in ultrasound (US) guided orthopedic procedures. However, these tasks are challenging due t…

eess.IV2022★ 4 cited

On-the-Fly Test-time Adaptation for Medical Image Segmentation

Jeya Maria Jose Valanarasu, Pengfei Guo, Vibashan VS +1

One major problem in deep learning-based solutions for medical imaging is the drop in performance when a model is tested on a data distribution different from the one that it is tr…

eess.IV2020★ 37 cited

Exploring Overcomplete Representations for Single Image Deraining using CNNs

Rajeev Yasarla, Jeya Maria Jose Valanarasu, Vishal M. Patel

Removal of rain streaks from a single image is an extremely challenging problem since the rainy images often contain rain streaks of different size, shape, direction and density. M…

eess.IV2020★ 1 cited

KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation

Jeya Maria Jose Valanarasu, Vishwanath A. Sindagi, Ilker Hacihaliloglu +1

Most methods for medical image segmentation use U-Net or its variants as they have been successful in most of the applications. After a detailed analysis of these "traditional" enc…