The solution takes a contextual information learning approach to efficiently segment anomalies in chest CT scans and reduces the need of large labelled dataset for training. Leveraging this solution health care pracitioniers can use a small set of labelled data to understand the anomaly or other chest condition and perform segmentation on a much larger set of unlabelled samples.

The solution is equipped with the capbility of precisely performing fine grained segmentation i.e sparsely labels where only a small and disburse parts of CT scan images is of interest. Our solution is recommended to be used as an assistant to the health care practitioners for large volume of data.

PACE - ML is Mphasis Framework and Methodology for end-to-end machine learning development and deployment. PACE-ML enables organizations to improve the quality & reliability of the machine learning solutions in production and helps automate, scale, and monitor them. Need customized Machine Learning and Deep Learning solutions? Get in touch!