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20202022
most citedRole of Edge Device and Cloud Machine Learning in Point-of-Care Solutions Using Imaging Diagnostics for Population Screening

4 citations · 14 across the 9 of their papers we have counts for

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cs.LG2020★ 1 cited

Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare

Manish Gawali, Arvind C S, Shriya Suryavanshi +5

In this paper, we compare three privacy-preserving distributed learning techniques: federated learning, split learning, and SplitFed. We use these techniques to develop binary clas…

eess.IV2020

Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks

Rohit Lokwani, Ashrika Gaikwad, Viraj Kulkarni +2

COVID-19 is an infectious disease that causes respiratory problems similar to those caused by SARS-CoV (2003). Currently, swab samples are being used for its diagnosis. The most co…

quant-ph2020

Quantum Computing Methods for Supervised Learning

Viraj Kulkarni, Milind Kulkarni, Aniruddha Pant

The last two decades have seen an explosive growth in the theory and practice of both quantum computing and machine learning. Modern machine learning systems process huge volumes o…

cs.CY2020★ 4 cited

Role of Edge Device and Cloud Machine Learning in Point-of-Care Solutions Using Imaging Diagnostics for Population Screening

Amit Kharat, Vinay Duddalwar, Krishna Saoji +8

Edge devices are revolutionizing diagnostics. Edge devices can reside within or adjacent to imaging tools such as digital Xray, CT, MRI, or ultrasound equipment. These devices are…

eess.IV2020★ 3 cited

Automatic Grading of Knee Osteoarthritis on the Kellgren-Lawrence Scale from Radiographs Using Convolutional Neural Networks

Sudeep Kondal, Viraj Kulkarni, Ashrika Gaikwad +2

The severity of knee osteoarthritis is graded using the 5-point Kellgren-Lawrence (KL) scale where healthy knees are assigned grade 0, and the subsequent grades 1-4 represent incre…

cs.LG2020

Survey of Personalization Techniques for Federated Learning

Viraj Kulkarni, Milind Kulkarni, Aniruddha Pant

Federated learning enables machine learning models to learn from private decentralized data without compromising privacy. The standard formulation of federated learning produces on…