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Publications

Image by National Cancer Institute
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A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography

Computed tomographic pulmonary angiography (CTPA) is the diagnostic standard for confirming pulmonary embolism (PE).

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Image by Markus Spiske
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Deep-learning-based automatic detection of pulmonary nodules from chest radiographs

To assess a deep learning-based artificial intelligence model for the detection of pulmonary nodules on chest radiographs and to compare its performance with board-certified human readers.

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Image by Markus Spiske
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Vulnerability Due to Training Order in Split Learning

Split learning (SL) is a privacy-preserving distributed deep learning method used to train a collaborative model without the need for sharing of patient's raw data between clients.

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Image by National Cancer Institute
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Levels of Autonomous Radiology

Radiology, being one of the younger disciplines of medicine with a history of just over a century, has witnessed tremendous technological advancements and has revolutionized the way we practice medicine today.

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Image by Kayla Speid
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Validation of a Deep Learning Model to aid in COVID-19 Detection from Digital Chest Radiographs

Using artificial intelligence in imaging practice helps ensure study list reprioritization, prompt attention to urgent studies, and reduces the reporting turn-around time.

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Image by CDC
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A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers

The study provided, for the first time, an extensive overview of available CAD products that can interpret CXR images for TB 

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Image by JESHOOTS.COM
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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).

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Computer Robot
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Survey of Personalization Techniques for Federated Learning

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

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Leg Injury
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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. 

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3D Scans

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.

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Image by Robina Weermeijer
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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.

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Image by CDC
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Reducing Labelled Data Requirement for Pneumonia Segmentation using Image Augmentations

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

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Doctor Examining CT Scan
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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.

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Image by CDC
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A Classical-Quantum Convolutional Neural Network for Detecting Pneumonia from Chest Radiographs

While many quantum computing techniques for machine learning have been proposed, their performance on real-world datasets remains to be studied.

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Image by Martin Sanchez
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Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Model

While developing artificial intelligence (AI)-based algorithms to solve problems, the amount of data plays a pivotal role - large amount of data helps the researchers and engineers to develop robust AI algorithms.

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Image by Alexander Jawfox
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Artificial intelligence in musculoskeletal radiology

Artificial intelligence (AI) in radiology has been establishing its mark. We are witnessing gradual inroads of AI in almost all branches of medicine, and radiology subspecialties are not immune to this change.

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Image by CDC
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DeepTek’s AI Model ranked
at par with Radiologist - Nature Scientific Reports

In the largest comparative evaluation of AI for TB, DeepTek’s Public Health Screening Solution - Genki, performed at par with expert Radiologist with over 30 years of experience

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Image by Michael Dziedzic

Real-world analysis of artificial
intelligence in musculoskeletal trauma

Musculoskeletal trauma accounts for a large percentage of emergency room visits and is amongst the top causes of unscheduled patient visits to the emergency room.

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Data Cloud

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.

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Computer Sketch

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.

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Image by Clay Banks

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.

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Image by Robina Weermeijer
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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.

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Analyzing Scans
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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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Protective Gear
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An artificial intelligence system for predicting mortality in COVID-19 patients using chest X-rays: a retrospective study

Background Early prediction of disease severity in COVID-19 patients is essential. 

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