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

Idiopathic non-cirrhotic portal hypertension and porto-sinusoidal vascular disease: Review of current data

Idiopathic non-cirrhotic portal hypertension (INCPH) is a clinicopathologic disease entity characterized by the presence of clinical signs and symptoms of portal hypertension (PH) in the absence of liver cirrhosis or known risk factors accountable for PH. Multiple hematologic, immune-related, infectious, hereditary and metabolic risk factors have been associated with this disorder. Still, the exact etiopathogenesis is largely unknown. The recently proposed porto-sinusoidal vascular disease (PSVD) scheme broadens the spectrum of the disease by also including patients without clinical PH who are found to have similar histopathologic findings on core liver biopsies. Three histomorphologic lesions have been identified as specific for PSVD to include obliterative portal venopathy, nodular regenerative hyperplasia and incomplete septal cirrhosis/fibrosis. However, these findings are often subtle, under-recognized and subjective with low interobserver agreement among pathologists. Additionally, the natural history of the subclinical forms of the disease remains unexplored. The clinical course is more favorable compared to cirrhosis patients, especially in the absence of clinical PH or liver dysfunction. There are no universally accepted guidelines in regard to diagnosis and treatment of INCPH/PSVD. Hence, this review emphasizes the need to raise awareness of this entity by highlighting its complex pathophysiology and clinicopathologic associations. Lastly, formulation of standardized diagnostic criteria with clinical validation is necessary to avoid misclassifying vascular diseases of the liver and to develop and implement targeted therapeutic strategies

Photoelectrochemical and electrochemical ratiometric aptasensing: a case study of streptomycin

There has been much interest in constructing ratiometric sensors using different sensing techniques because of their synergistic effect, although the simultaneous collection of the signals is challenging. Herein, we propose a ratiometric aptasensing strategy based on the dual-detection model with a photoelectrochemical (PEC) “signalon” and an electrochemical (EC) “signal-off”. As a proof-of-concept study, CdTe quantum dots (CdTe QDs) and a methylene blue-labeled aptamer (MB-Apt) were used to generate PEC and EC signals in the sensing system. The target-induced conformational change of MB-Apt pushed MB away from the electrode, thereby decreasing the EC signal; at the same time, the reduced steric hindrance favored the restoration of the PEC signal from the CdTe QDs. Thus, this PEC-EC strategy can achieve the PEC “signal-on” and EC “signal-off” states simultaneously, as well as allowing quantitative analysis of the target based on the ratio of the current intensities. As a model application, an aptasensor fabricated for streptomycin detection showed a wide linear range from 0.03 to 100 μM with a detection limit of 10 nM (S/N = 3). The proposed sensing platform displayed superior analytical properties compared with methods based on PEC or EC alone. Our work provides an efficient dual-detection modelbased ratiometric strategy for advanced analysis, and paves the way to the simultaneous acquisition of signals

Context-Based Dynamic Pricing with Online Clustering

We consider a context-based dynamic pricing problem of online products which have low sales. Sales data from Alibaba, a major global online retailer, illustrate the prevalence of low-sale products. For these products, existing single-product dynamic pricing algorithms do not work well due to insufficient data samples. To address this challenge, we propose pricing policies that concurrently perform clustering over products and set individual pricing decisions on the fly. By clustering data and identifying products that have similar demand patterns, we utilize sales data from products within the same cluster to improve demand estimation and allow for better pricing decisions. We evaluate the algorithms using the regret, and the result shows that when product demand functions come from multiple clusters, our algorithms significantly outperform traditional single-product pricing policies. Numerical experiments using a real dataset from Alibaba demonstrate that the proposed policies, compared with several benchmark policies, increase the revenue. The results show that online clustering is an effective approach to tackling dynamic pricing problems associated with low-sale products. Our algorithms were further implemented in a field study at Alibaba with 40 products for 30 consecutive days, and compared to the products which use business-as-usual pricing policy of Alibaba. The results from the field experiment show that the overall revenue increased by 10.14%