\ Computer Aided Diagnostics



Automatic Detection and Classification of Tumor Infiltrating Lymphocytes


Prof. Sharat Chandran


  • Prof. Rinti Banerjee (Bio)
  • Prof. Suyash Awate (CSE)
  • Prof. Jayanthi Sivaswamy (External)



On January 1, 2012, in the United States there were approximately 13,683,850 men and women alive who had a history of cancer, making it a common threat to all families. As technology becomes more efficient, a trend towards computer aided diagnostic (CAD) tools for identification, prognosis prediction and re-occurrence likelihood is becoming a reality.

The work in this thesis revolves around two methods that forward this technological front. First, we discuss Hierarchical Normalized Cuts (HNCuts) which has been expressly designed for high-throughput high quality segmentation of stained cells from histopathology images. Second, we discuss how the output of HNCuts can be fed into our Local Morphometric Scale (LMS) algorithm to provide pixel level classification of tumor versus stroma regions. We complete the talk by presenting and application and associated results in the domain of tumor infiltrating lymphocyte (TILs) detection, a valuable prognostic indicator for patient outcome.