IIIT-Hyderabad, NIMS join hands to digitize pathological slides

The dataset is part of work being done at the IIITH's iHub-Data, a Technology Innovation Hub focused on large-scale datasets to help develop solutions through applied research.

Hyderabad: With the installation of a digital scanner at Nizam’s Institute of Medical Sciences (NIMS) soon this month to convert traditional histopathological slides into computerized images, the International Institute of Information Technology Hyderabad (IIITH) here will take the first step towards an Indian Pathology Dataset.

The dataset is part of work being done at the IIITH’s iHub-Data, a Technology Innovation Hub focused on large-scale datasets to help develop solutions through applied research.

One of the initiatives in the healthcare domain is currently centred around image diagnosis of cancer. According to Prof. Deva Priyakumar, Academic Head, iHub-Data, the Indian Pathology Dataset will be an effort where large-scale data collection of histopathology images from Indian patients will be used to develop AI solutions for accelerated and accurate diagnosis of different types of cancer.

In traditional histopathology or a biopsy, a tissue sample is routinely processed to make sections out of it. These sections are then stained with pigments onto a glass slide to provide a contrast and reveal cellular components under microscopic observation.

“All of this is a manual and tedious process where a pathologist has to count and see various expressions in morphometry,” Dr. Shantveer Uppin, Head of Department, Pathology, NIMS, says in an IIITH blog.

“With digitization, the entire slide is scanned and converted into a digital one which can be viewed on a computer and magnified (if required) to look at the finer details,” Dr. Uppin says.

With cancer being one of the core areas of iHub’s research, the plan is to first create a dataset of histopathological slides, according to Prof. Bapi Raju, healthcare lead, iHub-Data.

Apart from cancer, plans are on to digitize lupus nephritis samples, an auto-immune condition affecting the kidneys. “There are various classes of lupus which are analysed manually. We want to build an AI tool where based on scanned images, it will identify these features and help in better diagnosis,” says Dr. Uppin.

“While some medical institutions store glass slides for five years for future reference, at NIMS, we archive them for 15 years and more. This requires a lot of space and infrastructure and slides also get affected over time. With digital archiving, the images can be barcoded and made easily accessible,” says Dr. Uppin.

iHub-Data is also exploring a consortium of hospitals and diagnostic centres. “Cancer has a lot of diversity, and we need a lot of samples. It’s not enough that we engage with one hospital. We are looking at other institutions too that can contribute to this effort and participate actively in annotating histopathological samples,” adds Prof. Raju, adding that preliminary discussions were on to have a centralized scanning facility housed at IIITH.

“Our aim is to create this repository such that not only can everyone contribute to it but it is also publicly accessible,” he adds.

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