An Efficient Cervical Whole Slide Image Analysis Framework Based on Multi-scale Semantic and Spatial Deep Features

Digital gigapixel whole slide image (WSI) is widely used in clinical diagnosis, and automated WSI analysis is key for computer-aided diagnosis. Currently, analyzing the integrated descriptor of probabilities or feature maps from massive local patches encoded by ResNet classifier is the main manner for WSI-level prediction. Feature representations of the sparse and tiny lesion cells in cervical slides, however, are still challengeable for the under-promoted upstream encoders, while the unused spatial representations of cervical cells are the available features to supply the semantics analysis. As well as patches sampling with overlap and repetitive processing incur the inefficiency and the unpredictable side effect. This study designs a novel inline connection network (InCNet) by enriching the multi-scale connectivity to build the lightweight model named You Only Look Cytopathology Once (YOLCO) with the additional supervision of spatial information. The proposed model allows the input size enlarged to megapixel that can stitch the WSI without any overlap by the average repeats decreased from $10^3\sim10^4$ to $10^1\sim10^2$ for collecting features and predictions at two scales. Based on Transformer for classifying the integrated multi-scale multi-task features, the experimental results appear $0.872$ AUC score better and $2.51\times$ faster than the best conventional method in WSI classification on multicohort datasets of 2,019 slides from four scanning devices.Comment: 16 pages, 8 figures, already submitted to Medical Image Analysi

Construction of a Recombinant Eukaryotic Expression Plasmid Containing Human Calcitonin Gene and Its Expression in NIH3T3 Cells

Aim. To construct a recombinant eukaryotic expression plasmid containing human calcitonin (hCT) gene and express the gene in murine fibroblast NIH3T3 cells. Materials and Methods. A murine Igκ-chain leader sequence and hCT gene were synthesized and cloned into pCDNA3.0 to form the pCDNA3.0-Igκ-hCT eukaryotic expression vector, which was transfected into NIH3T3 cells. The mRNA and protein expressions and secretion of hCT were detected. Primarily cultured osteoclasts were incubated with the supernatant of pCDNA3.0-Igk-hCT-transfected NIH3T3 cells, and their numbers were counted and morphology observed. Results. The expression and secretion of hCT were successfully detected in pCDNA3.0-Igk-hCT-transfected NIH3T3 cells. The number of osteoclasts was decreased and the cells became crumpled when they were incubated with the supernatant of pCDNA3.0-Igk-hCT-transfected NIH3T3 cells. Conclusion. A recombinant eukaryotic expression vector containing hCT gene was successfully constructed and expressed in NIH3T3 cells. The secreted recombinant hCT inhibited the growth and morphology of osteoclasts

Remodeling and Estimation for Sparse Partially Linear Regression Models

When the dimension of covariates in the regression model is high, one usually uses a submodel as a working model that contains signi�cant variables. �ut it may be highly biased and the resulting estimator of the parameter of interest may be very poor when the coefficients of removed variables are not exactly zero. In this paper, based on the selected submodel, we introduce a two-stage remodeling method to get the consistent estimator for the parameter of interest. More precisely, in the �rst stage, by a multistep adjustment, we reconstruct an unbiased model based on the correlation information between the covariates; in the second stage, we further reduce the adjusted model by a semiparametric variable selection method and get a new estimator of the parameter of interest simultaneously. Its convergence rate and asymptotic normality are also obtained. e simulation results further illustrate that the new estimator outperforms those obtained by the submodel and the full model in the sense of mean square errors of point estimation and mean square prediction errors of model prediction

Remodeling and Estimation for Sparse Partially Linear Regression Models

When the dimension of covariates in the regression model is high, one usually uses a submodel as a working model that contains significant variables. But it may be highly biased and the resulting estimator of the parameter of interest may be very poor when the coefficients of removed variables are not exactly zero. In this paper, based on the selected submodel, we introduce a two-stage remodeling method to get the consistent estimator for the parameter of interest. More precisely, in the first stage, by a multistep adjustment, we reconstruct an unbiased model based on the correlation information between the covariates; in the second stage, we further reduce the adjusted model by a semiparametric variable selection method and get a new estimator of the parameter of interest simultaneously. Its convergence rate and asymptotic normality are also obtained. The simulation results further illustrate that the new estimator outperforms those obtained by the submodel and the full model in the sense of mean square errors of point estimation and mean square prediction errors of model prediction