CATANET: A RESEARCH-BASED PRODUCT FOR METASTATIC BREAST CANCER STAGE PREDICTION USING DEEP LEARNING
Pathologists spend hours to detect metastasis in hematoxylin and eosin (H&E) stained whole-slide images of lymph node sections. Digital pathology is a new, rapidly expanding field of medical imaging. In digital pathology, whole-slide scanners are used to digitize glass slides containing tissue specimens at high resolution (up to 160nm per pixel). ‘CataNet’ focuses on the detection of micro and macro metastasis in lymph node digitized images. This subject is highly relevant; lymph node metastases occur in most cancer types (e.g. breast, prostate, and colon). The lymph nodes in the underarm are the first place breast cancer is likely to spread. The project is divided into two phases. For Slide-Level Classification we detect and localize the cancer cells in whole slide images. For this purpose, we first extract Region of Interest with Image processing, then construct Training Data tiles from ROI, Train Deep Convolutional model for tile-based classification, building tumor probability heat-maps using trained model and report detected tumor. At the end of phase 1, given a hematoxylin and eosin (H&E) stained whole-slide images of lymph node, we are able to tell whether the image contains tumor or not, and if present then localize it within the image. For Patient-Level Classification we determine the pathologic N-stage (pN-stage) label per patient by extracting the features from the probability maps generated by Phase 1, training a classifier on these features to determine slide level metastasis label and the determining the pN stage of patient using predetermined criteria. At the end of phase 2, given at least 5 whole-slide images of a patient, the product shall be able to determine the pN-stage as provided by the Union for International Cancer Control (UICC). The aim is to contribute our minor share for the service of this country by building this tool indigenously and helping the local research community.
For code please reach out at ubaig.bese15seecs@seecs.edu.pk