HealthCare Global Enterprises Ltd (HCG) on Monday announced that it has deployed Sigtuple's AI100 making HCG the first hospital chain to equip the Hematopathology labs across its network with AI-powered screening solutions for cancer detection and disease management. According to the company's press statement, SigTuple's AI100 is the premier solution for AI-assisted digital hematopathology. It is also the only digital hematopathology solution available that is economical and robust enough for wide-scale adoption, it claimed. "As manual microscopy is still the standard in diagnosing several critical disorders like cancers, infections, etc., in the absence of a pathologist at site in laboratories outside urban areas, these samples need to be shipped to central reference laboratories for review. Apart from the logistic challenges and associated delays in turnaround times, there is also limited expertise available for providing high quality diagnostics at remote locations," it stated.

Structural damage detection has become an interdisciplinary area of interest for various engineering fields, while the available damage detection methods are being in the process of adapting machine learning concepts. Most machine learning based methods heavily depend on extracted ``hand-crafted" features that are manually selected in advance by domain experts and then, fixed. Recently, deep learning has demonstrated remarkable performance on traditional challenging tasks, such as image classification, object detection, etc., due to the powerful feature learning capabilities. This breakthrough has inspired researchers to explore deep learning techniques for structural damage detection problems. However, existing methods have considered either spatial relation (e.g., using convolutional neural network (CNN)) or temporal relation (e.g., using long short term memory network (LSTM)) only. In this work, we propose a novel Hierarchical CNN and Gated recurrent unit (GRU) framework to model both spatial and temporal relations, termed as HCG, for structural damage detection. Specifically, CNN is utilized to model the spatial relations and the short-term temporal dependencies among sensors, while the output features of CNN are fed into the GRU to learn the long-term temporal dependencies jointly. Extensive experiments on IASC-ASCE structural health monitoring benchmark and scale model of three-span continuous rigid frame bridge structure datasets have shown that our proposed HCG outperforms other existing methods for structural damage detection significantly.