Deep Learning Provides Rapid Screen for Breast Cancer Metastasis with Sentinel Lymph Nodes

Deep learning has been shown to be useful to detect breast cancer metastases by analyzing whole slide images of sentinel lymph nodes. However, it requires extensive scanning and analysis of all the lymph nodes slides for each case. Our deep learning study focuses on breast cancer screening with only a small set of image patches from any sentinel lymph node, positive or negative for metastasis, to detect changes in tumor environment and not in the tumor itself. We design a convolutional neural network in the Python language to build a diagnostic model for this purpose. The excellent results from this preliminary study provided a proof of concept for incorporating automated metastatic screen into the digital pathology workflow to augment the pathologists' productivity. Our approach is unique since it provides a very rapid screen rather than an exhaustive search for tumor in all fields of all sentinel lymph nodes.

Imaging around corners with single-pixel detector by computational ghost imaging

We have designed a single-pixel camera with imaging around corners based on computational ghost imaging. It can obtain the image of an object when the camera cannot look at the object directly. Our imaging system explores the fact that a bucket detector in a ghost imaging setup has no spatial resolution capability. A series of experiments have been designed to confirm our predictions. This camera has potential applications for imaging around corner or other similar environments where the object cannot be observed directly.