Publications

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

COVID-19 is an infectious disease that causes respiratory
problems similar to those caused by SARS-CoV (2003).

Survey of Personalization Techniques for Federated Learning

Federated learning enables machine learning models
to learn from private decentralized data without
compromising privacy.

Automatic Grading of Knee Osteoarthritis on the Kellgren-Lawrence Scale from Radiographs 

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 increasing severity of the affliction. 

Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans

Image segmentation plays a pivotal role in several medical-imaging applications by assisting the segmentation of the regions of interest.

Automated chest radiograph diagnosis: A Twofer for Tuberculosis and Covid-19

TB is a pandemic which has challenged mankind for ages
and Covid 19 is a recent onset fast-spreading pandemic.

Reducing Labelled Data Requirement for Pneumonia Segmentation using Image Augmentations

Deep learning semantic segmentation algorithms can localize abnormalities or opacities from chest radiographs. 

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

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

Quantum Computing Methods for
Supervised Learning

The last two decades have seen explosive growth in the
theory and practice of both quantum computing and
machine learning.

Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare

In this paper, we compare three privacy-preserving distributed learning techniques: federated learning, split learning, and SplitFed.

Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly

We propose an automated method based on deep learning to compute the cardiothoracic ratio and detect the presence of cardiomegaly from chest radiographs.

Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice

The use of machine learning to develop intelligent software tools for the interpretation of radiology images has gained widespread attention in recent years.

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DeepTek’s mission is to make quality radiology services more affordable and accessible by leveraging the power of AI.

It is amongst very few Radiology AI companies which have successfully adopted its technology in a commercial mode – creating clear and quantifiable value for patients, hospitals, and radiologists. Currently, it is servicing over 50 hospitals and imaging centers and helping governments (India and APAC region) in their Tuberculosis, COVID 19, and other public health screening programs.

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