Providing quality health services and screening to rural populations in a nation as large as India can be extremely challenging. For example, India has only three accredited radiologists per million people. Using AI technology to provide more extensive, effective radiological screening has the potential for saving lives and providing overall improvements to health across the country. A unified approach to handling diverse medical images that span modalities presents a distinct challenge to researchers and developers, one requiring a compute-intensive processing platform and an innovative approach to the deep neural network model.
All texts in this page are taken from here
Background and Project History
A research article in The Journal of Global Radiology titled “Implementing Diagnostic Imaging Services in a Rural Setting of Extreme Poverty” outlined the obstacles faced by health practitioners to provide wide-ranging imaging services. Assessing the difficulty in obtaining consults with experts in radiology, the authors noted, “When doctors feel an X-ray or ultrasound is beyond their skill level to diagnose, limited options exist for consultation. This is of particular concern with ultrasound at hospitals like BH, where doctors estimate learning about 95% of diagnostic procedures from more specialized providers.”
To help address screening radiology challenges in rural India, Debdoot Sheet launched Project MIRIAD, exploring innovative AI-guided screening techniques. Based at the Indian Institute of Technology Kharagpur, Debdoot is the Assistant Professor in the Department of Electrical Engineering and Principal Investigator in the Kharagpur Learning, Imaging and Visualization Group. A presentation describing his objectives, Project MIRIAD: Exploring 3K+ CNNs Beyond ImageNet for Screening Radiology, summarized the scope of the challenge with some eye- opening statistics:
67 percent of the Indian population resides in rural areas. 90 percent of medical imaging facilities are in cities. There are only 3 radiologists per 1,000,000 people in the country. With a background that includes an undergraduate degree in electronics and communication engineering with specialization in digital signal processing, a master’s degree in computer vision for medical imaging, and a PhD in computational medical imaging and machine learning, Debdoot is well positioned to devise and apply new AI technologies for addressing the challenge and devise methods that help unify a diverse range of medical image types.
“During my research,” Debdoot said, “I’ve focused on compute optimizations for training learning-based models in settings with limited compute and data resources. Leading my research group as an assistant professor since 2014, we have been focusing on efficient computing platforms and hybrid computing-based learning systems for developing competing solutions in the medical image analysis space under significant computing power limitations.”
“We do not necessarily rely only on the community dominant graphics processing units (GPUs) for our deep neural networks (DNNs) to work,” he continued. “In fact, since we often work on introducing new layer definitions to accelerate learning and promote domain adaptation, CPUs are a better matched solution than GPUs. GPUs typically require a longer time for integrating the custom layer definitions into compliant libraries, and they sometimes do not permit certain classes of functions to be optimally implemented on platforms other than CPUs.”