Search Results for author: Varun Mannam

Found 7 papers, 4 papers with code

Low-Energy Convolutional Neural Networks (CNNs) using Hadamard Method

no code implementations6 Sep 2022 Varun Mannam

Convolutional neural networks (CNNs) are a potential approach for object recognition and detection.

Object Recognition

Deconvolution in Fluorescence Lifetime imaging microscopy (FLIM)

no code implementations16 Jan 2022 Varun Mannam, Xiaotong Yuan, Scott Howard

Fluorescence lifetime imaging microscopy (FLIM) is an important technique to understand the chemical micro-environment in cells and tissues since it provides additional contrast compared to conventional fluorescence imaging.

Low dosage 3D volume fluorescence microscopy imaging using compressive sensing

no code implementations3 Jan 2022 Varun Mannam, Jacob Brandt, Cody J. Smith, Scott Howard

To address this challenge, we present a compressive sensing (CS) based approach to fully reconstruct 3D volumes with the same signal-to-noise ratio (SNR) with less than half of the excitation dosage.

Compressive Sensing

Deep learning-based super-resolution fluorescence microscopy on small datasets

1 code implementation7 Mar 2021 Varun Mannam, Yide Zhang, Xiaotong Yuan, Scott Howard

However, using the new approach, a network can be trained to achieve super-resolution images from this small dataset.


Machine learning for faster and smarter fluorescence lifetime imaging microscopy

1 code implementation5 Aug 2020 Varun Mannam, Yide Zhang, Xiao-Tong Yuan, Cara Ravasio, Scott S. Howard

Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique in biomedical research that uses the fluorophore decay rate to provide additional contrast in fluorescence microscopy.

BIG-bench Machine Learning lifetime image denoising

Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs

1 code implementation26 Feb 2020 Varun Mannam, Arman Kazemi

Extracting large amounts of data from biological samples is not feasible due to radiation issues, and image processing in the small-data regime is one of the critical challenges when working with a limited amount of data.

Small Data Image Classification

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