Smart Healthcare: Deep Learning-Based Abnormality Detection in Medical Imaging Internship

About Internship

Professor from School of Advanced Studies, Bengaluru, is accepting interns interested in the field of Computer Science, in the research area of Artificial intelligence and Machine learning.

The project titled "Smart Healthcare: Deep Learning-Based Abnormality Detection in Medical Imaging" aims to leverage advanced deep learning techniques to improve the accuracy and efficiency of medical image analysis for detecting abnormalities. This research focuses on developing robust and scalable deep neural networks capable of processing various medical imaging modalities such as MRI, CT scans, X-rays, and ultrasound. By automating the identification of pathological features, the system intends to assist clinicians in early diagnosis, reducing human error, and accelerating medical workflows. The study will involve dataset collection and preprocessing, model design and training, performance evaluation, and clinical validation to ensure high reliability and adaptability in real-world healthcare settings. Candidate Roles and Responsibilities Literature Review Conduct comprehensive reviews of current state-of-the-art deep learning models and medical imaging techniques to identify research gaps and optimal methodologies. Data Collection and Management Gather and preprocess diverse medical image datasets while ensuring data quality, normalization, and augmentation to improve model performance. Model Development Design and implement deep learning architectures (e.g., CNNs, GANs, transformers) tailored to detect abnormalities in different imaging modalities. Training and Optimization Perform model training, fine-tuning, and hyperparameter optimization using GPUs and high-performance computing clusters. Evaluation and Validation Assess model accuracy, sensitivity, specificity, and robustness using benchmark datasets and clinical cases to validate results. Implementation of Explainability Integrate explainable AI methods to make the abnormality detection interpretable for clinicians. Collaboration and Reporting Work closely with healthcare experts for clinical insights, document progress, present findings in meetings, and contribute to scientific publications and conference