Projects and Publications
Our collaboration with esteemed industry partners and the community has facilitated the creation of numerous research projects spanning various application domains. The IAPI Research Lab welcomes students, researchers, and companies to delve into collaborative opportunities and research endeavors. To initiate discussions regarding potential projects, please reach out to us.
Alzheimer Disease Stage Classification
Alzheimer’s disease, a neuro degenerative disorder, poses significant challenges in early diagnosis and treatment. Non-invasive imaging techniques such as brain Magnetic Resonance Imaging (MRI) offer promise but require advanced analysis methods to detect subtle structural changes indicative of AD. This research focused on devising specialized solutions for Alzheimer disease stage classification.
Pharmaceutical Drug Classification
Accurate drug classification through deep learning approaches enhance medication safety by minimizing errors in drug identification and dosage, ultimately safeguarding patient health. These advanced techniques provide a promising solution to prevent the risks associated with incorrect medication use, ensuring that patients receive the most effective treatment for their condition.
Table detection and Content Extraction
Relational tables are abundant sources of valuable information. Each of these tables found on the web is characterized by labeled and typed columns, effectively functioning as a structured database. Relational tables are abundant sources of valuable information. Each of these tables found on the web is characterized by labeled and typed columns, effectively functioning as a structured database. This research introduces a comprehensive deep learning methodology that is tailored for precise identification and extraction of rows and columns from document images containing tables.
Explainable AI in Chest Image Analysis & Report Generation
The use of machine learning in healthcare has the potential to revolutionize virtually every aspect of the industry. However, the lack of transparency in AI applications may lead to the problem of trustworthiness and reliability of the information provided by these applications. Medical practitioners rely on such systems for clinical decision making, but without adequate explanations, diagnosis made by these systems cannot be completely trusted.