eXplainable Deep Intelligence Lab

Partnership and Collaborative Hub

Research Projects

Our research lab is focused on developing cutting-edge technologies in the fields of Computer Vision (CV) and Natural Language Processing (NLP) for Computer Aided Detection (CAD) on local and Cloud computing clusters. Specifically, we are working on applied Deep Learning (DL) for localization and segmentation, sentiment analysis and response generation with the focus on explainability (XAI).

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CV
NLP
XAI
Cloud

Knee Localization

Enabling more precise and efficient analysis of issues and injuries in medical imaging can be facilitated by localizing a region of interest within an image.

Sentiment Analysis

The utilization of sentiment analysis techniques for depression detection can assist in identifying expressed emotions and opinions, enabling the identification of potential threats or opportunities.

Few-Shot Learning

Few-shot learning for segmentation provides the potential to significantly reduce the need for large amounts of annotated data, which is often costly and time-consuming to acquire.

Medication Effectiveness

ML models can be trained to predict an outcome of interest from a set of input features. XAI techniques and algorithms can be utilized to shed a light on feature importance to make better clinical decisions based on evaluated treatment efficacy.

Cloud Performance Modeling

Many critical applications are running on the cloud, ewhere scaleable infinite processing power is provided. However, due to collocation of Virtual Machine (VM) and performance interfierences, perfomamnce aware scheduling of critical and time sentsitive jobs to the cloud is needed.

Question Answering from Electronic Health Records (EHR)

Pretrained language models can be utlizied to address clinical questions from unstructure free form text in EHR. Providing potential to improve clinical decision making by accessing critical information in EHR notes.

Fairness in Segmentation

Automatic segmentation offers the potential to help surgeons in preoperative planning. However, analyzing the fairness and any probable racial and gender bias in such models has been very limited. .

Multimodal Pain Prediction

Multimodal models can be designed to integrate and process multiple sources of data as input to identify potential sources of bias and increase model fairness.

Transfer Learning for Retinal Disease Classfication

To accurately predict the most significant abnormality among multiple categories of retinal disease, with limited data in each category, state-of-the-art pretrained multi-class models are required.

Hamidreza Moradi, PhD

Director North Carolina A&T State University

Our Research Team

Our research team is highly diverse and consists of individuals with a range of educational backgrounds and experience, including graduate and PhD students, doctors, and faculties, all of whom bring unique perspectives and expertise to the research projects. This diversity in the team ensures that there is a broad range of skills, knowledge, and experience that can be drawn upon to effectively tackle the research questions at hand. The team's diversity also promotes creativity, innovation, and inclusivity, and helps to ensure that the research outcomes are rigorous and well-rounded.

Shashank Shekhar, MD

Clinical Advisory Board Duke University

Matthew Morris, PhD

Clinical Advisory Board Vanderbilt University Medical Center

Hamidreza Arabnia, PhD

Academic Advisory Board University of Georgia

Sally Hodder, MD

Clinical Advisory Board University of West Virginia

William B. Hillegass, MD, PhD

Clinical and Biostatistical Advisory Board University of Mississippi Medical Center

Ehsan Alam

Graduate Research Assistant North Carolina A&T State University

Raihana Tasnim

Graduate Research Assistant North Carolina A&T State University

Nickolas Littlefield

Graduate Research Assistant University of Pittsburgh

David M. Lee

Graduate Research Assistant University of Mississippi Medical Center

Tran T. Le

Graduate Research Assistant University of Mississippi Medical Center

Ahmad P. Tafti, PhD

Extramural Collaborator University of Pittsburgh

Soheyla Amirian, PhD

Extramural Collaborator University of Georgia

Candace Howard-Claudio, MD

Extramural Collaborator University of Mississippi Medical Center

News

Dec. 2023: Our collaborative work with Vanderbilt University Medical Center got recognition from the VUMC Reporter.
Dec. 2023: Our AI/ML book Chapter is published by Springer and avaiable to read.
Oct. 2023: Our manuscript accepted at PLOS ONE on the effect of socioeconomic factors on patients survival.
Sep. 2023: We presented our work on the use of LLM for EHR question and answering in IEEE BHI.
Sep. 2023: We presented our work on unbiased image segmentation in IEEE BHI.
Mar. 2023: We submited a paper to IEEE EBAIC on heap bony antomy segmentation.a>
Mar. 2023: A manuscript and a poster got accepted at IEEE ICHI.
Mar. 2023: We are presenting a Deep Learning workshop in collaboration with the Marquette University on March 24.
Feb. 2023: Our manuscript accepted at PLOS ONE on predicting Covid-19 thraputics effectiveness using Machine Learning models.
Feb. 2023: We recived Amazon Web Services (AWS) research support.
Jan. 2023: We recived the Google Cloud research award.
Dec. 2022: We presetened our knowledge infused embedding approach at IEEE CSCI .
Dec. 2022: We recived the Oracle research award.
Oct. 2022: Our manuscript accepted at Journal of the American Medical Informatics Association (JAMIA open).
Feb. 2022: Probabilistic modeling of COVID-19 outcome presented at Conference on Retroviruses and Opportunistic Infections (CROI) .
Dec. 2021: Our modfied measure of COVID-19 severity levels presented at Open Forum Infectious Diseases (OFID).
Jul. 2021: We presented our set of cloud micro-benchmarks at Springer GCC.
Jan. 2021: Our manuscript on online performance modeling accepted at IEEE Transactions on Cloud Computing (TCC).

Contact Us

Connect with Us and Reach Out to Our Team

Our Address

North Carolina A&T State University

Department of Computer Science

MERIC 331, 1101 E Market St, Greensboro, NC 27401

Email Us

hmoradi {\a/} ncat {dot} edu

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Welcome to

eXplainable

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