SUMMARY
Shiba P. Kuanar, Ph.D., explores advancements in the field of medical imaging analysis using artificial intelligence (AI) and machine learning (ML) methodologies in cancer research. He enhances disease understanding by integrating data from various medical specialties into AI models. Dr. Kuanar's primary focus revolves around the development and validation of deep learning and probability algorithms tailored to detect and assess disease severity with an emphasis on patient care.
In a recent shift, Dr. Kuanar has broadened his research scope to incorporate electronic health record text analysis, employing generative large language models (LLMs). By integrating these models with imaging pipelines and statistical learning theories, he aims to extract crucial feature information for healthcare data analysis.
Focus areas
- Abdominal image analysis. Dr. Kuanar applies deep learning pipelines to detect clinically significant prostate cancer and predicts outcomes through multiparametric Magnetic Resonance Imaging (MRI). Dr. Kuanar believes that these models could enhance the interpretation of MRI images and provide valuable supplementary insights to aid radiologists in their diagnostic process. Additionally, Dr. Kuanar's research explores biomarkers such as transition-zone PSA-density as potential alternatives to the conventional prostate-specific antigen test technique for screening prostate cancer.
- Unsupervised and active learning. Dr. Kuanar's recent work focuses on fine-tuning LLMs to accurately predict different class labels in clinical text within electronic health records, specifically tailored to address patient safety challenges. In an active learning context, Dr. Kuanar endeavors to leverage generative models to summarize the text and enhance the data labeling process.
- Multi-Modal Learning. Dr. Kuanar utilizes multimodal methods to integrate data from diverse sources, employing both traditional and deep learning techniques. Employing feature-level, decision-level and model-level fusion techniques, Dr. Kuanar endeavors to uncover essential insights within images, mitigating biases and improving model interpretability.
- Federated learning. Decentralized federated learning addresses privacy, computational and storage challenges in managing large medical archives across multiple hospitals. This collaborative effort seeks to assess the feasibility of using AI-assisted annotation to streamline federated learning for automated image segmentation tasks. The goal is to enhance overall model performance and mitigate bias stemming from interobserver variability in image annotation.
Significance to patient care
Medical images and clinical texts have important information for healthcare applications. Despite increasing digitization, the complexity and variability of clinical text and image modalities pose challenges for traditional ML. Dr. Kuanar's research explores AI/ML techniques to understand medical data better, tackle safety challenges, expedite decision-making and improve patient care.
Professional highlights
- Member, Society for Imaging Informatics in Medicine, Machine Learning Tools and Research Sub-Committee, 2022-present.
- Member, American Association for Cancer Research, 2021-present.
- Member, Coalition for Health Artificial Intelligence Team, Mayo Clinic, 2024.
- Member, Program Committee Deep Generative Models Workshop, Medical Image Computing and Computer Assisted Intervention Society, 2022.