Background: Histopathological grading is a significant risk factor for postsurgical recurrence in hepatocellular carcinoma (HCC). Preoperative knowledge of histopathological grading could provide instructive guidance for individualized treatment decision-making in HCC management. Purpose: This study aims to develop and validate a newly proposed deep learning model to predict histopathological grading in HCC with improved accuracy. Methods: In this dual-centre study, we retrospectively enrolled 384 HCC patients with complete clinical, pathological and radiological data. Aiming to synthesize radiological information derived from both tumour parenchyma and peritumoral microenvironment regions, a modelling strategy based on a multi-scale and multi-region dense connected convolutional neural network (MSMR-DenseCNNs) was proposed to predict histopathological grading using preoperative contrast enhanced computed tomography (CT) images. Multiscale inputs were defined as three-scale enlargement of an original minimum bounding box in width and height by given pixels, which correspondingly contained more peritumoral analysis areas with the enlargement.Multi-region inputs were defined as three regions of interest (ROIs) including a squared ROI, a precisely delineated tumour ROI, and a peritumoral tissue ROI. The DenseCNN structure was designed to consist of a shallow feature extraction layer, dense block module, and transition and attention module. The proposed MSMR-DenseCNN was pretrained by the ImageNet dataset to capture basic graphic characteristics from the images and was retrained by the collected retrospective CT images. The predictive ability of the MSMR-DenseCNN models on triphasic
The operational cycle identification of the load-haul-dump (LHD) can help support the production process optimization in the underground mining industry and thus reduce mining costs. However, most of the existing research works use only the hydraulic bucket signal of LHD as the data source, and the stability and robustness of the identification method are poor. A few advanced research works use the variational Bayesian Gaussian mixture model to introduce other signals, but the accuracy of this recognition method is not perfect at present. In addition, the current identification methods are unable to simultaneously recognize the four working conditions of the LHD which include loading, hauling, dumping, and transiting. To solve these problems, a random forest feature selection (RFFS) and bidirectional long short-term memory (Bi-LSTM) based operation cycle recognition algorithm is proposed. Firstly, RFFS is used to remove redundant features based on the multi-sensor signals of the LHD. Then, Bi-LSTM is applied to fully exploit the temporal correlation between different operation regimes and accurately recognize the operation cycles. The effectiveness and superiority of the algorithm are verified by the experiment on the actual data of the LHD. The proposed algorithm can recognize four working conditions simultaneously, among which the recognition accuracy of loading conditions is the highest, up to 95.42%, and the weighted accuracy of this algorithm can reach 91.75% using the occupied time of each working condition as the weighting factor.
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