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  2. Supervisor: Dr Muhammad Saqib Sohail
Supervisor: Dr Muhammad Saqib Sohail

 

An explainable AI-based system for Automated X-Ray Report Generation

 

Supervisor:Dr Muhammad Saqib Sohail/ Snr. Associate Professor

Co-Supervisor:Dr Khawaja Qasim Maqbool/Snr. Assistant Professor

Supervisor’s google scholar profile: https://scholar.google.com/citations?user=XRuF7HAAAAAJ&hl=en

Campus/School/Dept: BULC /-/CS

 

Topic Brief Description:

Government hospitals in Pakistan face significant challenges due to a shortage of doctors and overwhelming patient loads, particularly in radiology departments. This creates substantial challenges in patient care. Long delays in the evaluation of X-ray images and the generation of diagnostic reports often hinder timely treatment. This project proposes an explainable AI-based system for automated X-ray report generation. The system will leverage state-of-the-art artificial intelligence methodologies to analyze X-ray images and produce accurate diagnostic reports. Additionally, we will identify the features learned by the model and their impact on accurate X-ray classification and reporting. By automating the reporting process, the framework aims to alleviate the burden on medical professionals, significantly reduce patient wait times, and enhance operational efficiency within hospitals. With its scalable and adaptable design, the proposed solution will be tailored to meet the pressing demands of the radiology departments of Pakistan’s overburdened public healthcare system.

 

Research Objectives:

To develop a scalable and adaptive AI-based framework for automated X-ray report generation to enhance diagnostic efficiency and alleviate the workload in Pakistan’s public healthcare system.

Deliverable:

A fully functional AI-powered software prototype capable of analyzing X-ray images and generating diagnostic reports in real-time.

 

Research Questions:

  1. How can historical X-ray data be used to train an explainable AI-based system for automated report generation?
  2. How does the choice of model architecture affect system performance?

Why you should apply:

  1. Opportunity to do research work on cutting-edge technology. You will be designing explainable models for classifying X-Ray scans.
  2. Community-focused project solving a real-world problem faced by ordinary Pakistanis.
  3. Opportunity to work with a highly motivated team with international experience.
  4. Excellent opportunity to develop your technical and soft skills.

 

Candidate’s Eligibility Profile:

  1. The applicant must have an MS/MPhil/Equivalent degree in CS/CE/EE with CGPA >0. An ideal candidate would have a strong background in Machine learning and artificial intelligence. Additional understanding of detection and estimation/signal processing would be a plus.
  2. Good programming skills are a must (experience in any of Python, R, or C/C++). Experience in developing/training machine learning/artificial intelligence models is desirable.
  3. Candidates with prior publications are strongly encouraged to apply.
  4. Excellent communication skills.
  5. Proficiency in spoken and written English is essential.
  6. We value independence while promoting teamwork and collaboration with colleagues.