Overview / Usage

The project aims at developing algorithms for automated detection of liver cancer in whole-slide images (WSIs). The liver is a visceral organ most often involved in the metastatic spread of cancer. For the best practice, early diagnosis of liver cancer is important but many people don't even know that they have hepatitis. Hepatocellular Carcinoma(HCC) represents about 90% of primary liver cancers and constitutes a major global health problem. This work would enable automating pathological work-flow and thereby aid pathologists in saving time.

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Methodology / Approach

Fully convolution neural networks have been shown to produce state-of-art results for image segmentation tasks. Keras framework would be utilized for the development of training and inference pipeline.

Technologies Used

Tensorflow, Keras, GPU, CPU

Collaborators