Digital Histopathology

The Digital Histopathology project funded by an HEC-NRPU grant helped us test and benchmark our low-cost, low-resolution (LCLR) approach to digital histopathology that uses 5MP field view images instead of whole-slide images (WSI). Our team of clinicians, and ML/DL engineers, along with the team of histopathologists at RMI developed a pipeline for the procurement of biopsy slides, digitisation of metadata, slide labelling, image acquisition and image storage. The first images for four cancers (breast, colon, oral, and gastrointestinal) was launched as HistoVault v1, with close to 18000 images (including both WSI and LCLR). The team trained and tested seven different AI models ultimately selecting and fine-tuning a hybrid TransUnet model for binary and multiclass classification of all four cancers.