Abstract: Disclosed is a method for enhancing memory utilization and throughput of a computing platform in training a deep neural network (DNN). The critical features of the method includes: calculating a memory size for every operation in a computational graph, storing the operations in the computational graph in multiple groups with the operations in each group being executable in parallel and a total memory size less than a memory threshold of a computational device, sequentially selecting a group and updating a prefetched group buffer, and simultaneously executing the group and prefetching data for a group in the prefetched group buffer to the corresponding computational device when the prefetched group buffer is update. Because of group execution and data prefetch, the memory utilization is optimized and the throughput is significantly increased to eliminate issues of out-of-memory and thrashing.

Abstract: A method for predicting epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma is provided. The method utilizes a lung adenocarcinoma EGFR mutation classification model based on a deep learning model, and performs back-propagation training on the deep learning model by using whole-slide pathological images and corresponding pathological data. The trained lung adenocarcinoma EGFR mutation classification model can determine whether a to-be-classified slide-level image with lung adenocarcinoma features have EGFR mutations.

Abstract: Disclosed are an object detection method and a convolution neural network. The method is performed through hierarchical architecture of the CNN and includes extracting groups of augmented feature maps from an input image through a backbone and two other groups of feature maps, identifying positive and negative samples with an IOU-based sampling scheme to be proposals for foreground and background through a proposal-sampling classifier, mapping the proposals to regions on the groups of augmented feature maps through the region proposal module, pooling the regions to fixed scale feature maps based on ROI aligning, fusing the fixed scale feature maps, and flattening the fused feature maps to generate an ROI feature vector through an ROI aligner for object classification and box regression. Because extracted features in the groups of augmented feature maps range from spatially-rich features to semantically-rich features, enhanced performance in object classification and box regression can be secured.

Abstract: Disclosed are an object detection method and a convolution neural network. The method is performed through hierarchical architecture of the CNN and includes extracting groups of augmented feature maps from an input image through a backbone and two other groups of feature maps, identifying positive and negative samples with an IOU-based sampling scheme to be proposals for foreground and background through a proposal-sampling classifier, mapping the proposals to regions on the groups of augmented feature maps through the region proposal module, pooling the regions to fixed scale feature maps based on ROI aligning, fusing the fixed scale feature maps, and flattening the fused feature maps to generate an ROI feature vector through an ROI aligner for object classification and box regression. Because extracted features in the groups of augmented feature maps range from spatially-rich features to semantically-rich features, enhanced performance in object classification and box regression can be secured.

Abstract: The present disclosure provides a method for controlling autonomous microscope system, microscope system, and computer readable storage medium. Taking the advantage of a neural network trained in a reinforcement learning scheme, the method automatizes the analysis process of biological sample executed by microscope system and therefore improves the diagnosis efficiency.

Abstract: The present disclosure provides a method for controlling autonomous microscope system, microscope system, and computer readable storage medium. Taking the advantage of a neural network trained in a reinforcement learning scheme, the method automatizes the analysis process of biological sample executed by microscope system and therefore improves the diagnosis efficiency.

Abstract: A method for analyzing an image of a biopsy specimen to determine a probability that the image includes an abnormal region is provided. The method involves a two-stage image analysis and adopts a combination of deep convolutional neural networks and staged and/or parallel computing to perform image recognition and classification. Such two-stage nasopharyngeal carcinoma detection module can detect and predict whole slide images into probabilities related to the nasopharyngeal carcinoma.