Search Results for author: Nisarg A. Shah

Found 15 papers, 4 papers with code

ALAP-AE: As-Lite-as-Possible Auto-Encoder

no code implementations19 Mar 2022 Nisarg A. Shah, Gaurav Bharaj

We present a novel algorithm to reduce tensor compute required by a conditional image generation autoencoder and make it as-lite-as-possible, without sacrificing quality of photo-realistic image generation.

Conditional Image Generation

Can No-reference features help in Full-reference image quality estimation?

no code implementations2 Mar 2022 Saikat Dutta, Sourya Dipta Das, Nisarg A. Shah

Recent works in Full-reference IQA research perform pixelwise comparison between deep features corresponding to query and reference images for quality prediction.

Image Quality Assessment Image Quality Estimation

ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images

no code implementations16 Feb 2022 Huihui Fang, Fei Li, Huazhu Fu, Xu sun, Xingxing Cao, Fengbin Lin, Jaemin Son, Sunho Kim, Gwenole Quellec, Sarah Matta, Sharath M Shankaranarayana, Yi-Ting Chen, Chuen-heng Wang, Nisarg A. Shah, Chia-Yen Lee, Chih-Chung Hsu, Hai Xie, Baiying Lei, Ujjwal Baid, Shubham Innani, Kang Dang, Wenxiu Shi, Ravi Kamble, Nitin Singhal, Ching-Wei Wang, Shih-Chang Lo, José Ignacio Orlando, Hrvoje Bogunović, Xiulan Zhang, Yanwu Xu, iChallenge-AMD study group

The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions.

Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Coding

no code implementations27 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.

Anomaly Detection

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results

1 code implementation19 Dec 2021 Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard McKinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gómez, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Raynaud, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Linmin Pei, Murat AK, Sarahi Rosas-González, Illyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh McHugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicolas Boutry, Alexis Huard, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-Andr Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel

In this study, we explore and evaluate a metric developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.

Brain Tumor Segmentation Translation +1

MSR-Net: Multi-Scale Relighting Network for One-to-One Relighting

no code implementations13 Jul 2021 Sourya Dipta Das, Nisarg A. Shah, Saikat Dutta

Deep image relighting allows photo enhancement by illumination-specific retouching without human effort and so it is getting much interest lately.

Image Relighting

Stacked Deep Multi-Scale Hierarchical Network for Fast Bokeh Effect Rendering from a Single Image

1 code implementation15 May 2021 Saikat Dutta, Sourya Dipta Das, Nisarg A. Shah, Anil Kumar Tiwari

Most of the existing methods utilized additional sensor's data or pretrained network for fine depth estimation of the scene and sometimes use portrait segmentation pretrained network module to segment salient objects in the image.

Bokeh Effect Rendering

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