To help with better disease diagnosis and treatment, two institutes in Hyderabad collaborate to launch digital pathology datasets Premium

Enhancing clinical research in India with publicly accessible histopathological image datasets for cancer and kidney disease research.

In an effort towards enhancing clinical research in India, the International Institute of Information Technology Hyderabad (IIIT-H) has joined hands with the Nizam’s Institute of Medical Sciences (NIMS) to unveil publicly accessible datasets featuring digitised histopathological images. These datasets include high-resolution images of brain cancer and kidney disease (Lupus Nephritis), forming the cornerstone of the India Pathology Dataset (IPD) project.

Histopathological images are microscopic images of tissues that are used to diagnose diseases, especially cancer.

The initiative offers benefits ranging from reduced risk of damaging physical slides to improved clinical decision-making and bettering research opportunities with the help of Artificial Intelligence (AI).

“Traditionally, tissue samples are examined under a microscope. By digitising these slides, computers can visualise the images, enabling collaborative diagnosis among pathologists across locations,” explained Vinod P.K., who leads the project’s dataset curation efforts.

The IPD initiative represents a multi-stakeholder collaboration among academia, hospitals, industry, and the government, aiming to digitise tissue biopsy slides. Supported by the Technological Innovation Hub for Data Banks, Data Services, and Data Analytics (TiH-Data), IIIT-H has installed a whole-slide digital scanner at NIMS to facilitate this effort.

One of the first datasets that has been released is the IPD-Brain dataset in Nature Scientific Data, an open-access, online-only journal for descriptions of scientifically valuable datasets. 

Highlighting its clinical relevance, Megha Uppin from the Department of Pathology at NIMS noted that precise typing, sub-typing, and grading of cancers was critical for effective management. “This dataset allows machine learning models to explore regional and ethnic variations in brain tumours, enhancing diagnostic precision and identifying cancer subtypes,” she said.