The proposed research proposes to develop novel image processing and machine learning algorithms for the detection and segmentation of cancerous regions from high-resolution images of tissue slides, distinguishing them from healthy/benign regions. The proposed research plans to advance (i) A reliable framework for use in the accurate segmentation of cancerous regions in tissue slides; (ii) New algorithms for texture analysis; (iii) Innovative representations that can increase the system throughput; (iv) Effective machine learning techniques and transparent user interfaces to assist in the reduction of the time that a pathologist needs to examine each slide; and (v) Extensive testing to a variety of cancers including prostate and breast cancer.
The proposed work has the potential to yield algorithms to facilitate the design of systems that can increase the likelihood of cancer detection. The work is supported by the Industry Advisory Board as well as individual industry members of the center and has the potential to extend the center?s portfolio. The PIs plan to introduce the research content into summer robotic camps for middle schoolers from underrepresented groups, create a website along with a wiki, visit local groups and K-12 schools, and include the research products into the curricula of the participating institutions.
