Classifying Hematoxylin and Eosin Images Using a Super-Resolution Segmentor and a Deep Ensemble Classifier

Developing an automatic and credible diagnostic system to analyze the type,stage,and level of the liver cancer from Hematoxylin and Eosin(H&E)images is a very challenging and time-consuming endeavor,even for experienced pathologists,due to the non-uniform illumination and artifacts.Albeit several Machine Learning(ML)and Deep Learning(DL)approaches are employed to increase the performance of automatic liver cancer diagnostic systems,the classi-fication accuracy of these systems still needs significant improvement to satisfy the real-time requirement of the diagnostic situations.In this work,we present a new Ensemble Classifier(hereafter called ECNet)to classify the H&E stained liver histopathology images effectively.The proposed model employs a Dropout Extreme Learning Machine(DrpXLM)and the Enhanced Convolutional Block Attention Modules(ECBAM)based residual network.ECNet applies Voting Mechanism(VM)to integrate the decisions of individual classifiers using the average of probabilities rule.Initially,the nuclei regions in the H&E stain are seg-mented through Super-resolution Convolutional Networks(SrCN),and then these regions are fed into the ensemble DL network for classification.The effectiveness of the proposed model is carefully studied on real-world datasets.The results of our meticulous experiments on the Kasturba Medical College(KMC)liver dataset reveal that the proposed ECNet significantly outperforms other existing classifica-tion networks with better accuracy,sensitivity,specificity,precision,and Jaccard Similarity Score(JSS)of 96.5%,99.4%,89.7%,95.7%,and 95.2%,respectively.We obtain similar results from ECNet when applied to The Cancer Genome Atlas Liver Hepatocellular Carcinoma(TCGA-LIHC)dataset regarding accuracy(96.3%),sensitivity(97.5%),specificity(93.2%),precision(97.5%),and JSS(95.1%).More importantly,the proposed ECNet system consumes only 12.22 s for training and 1.24 s for testing.Also,we carry out the Wilcoxon statistical test to determine whether the ECNet provides a considerable improvement with respect to evaluation metrics or not.From extensive empirical analysis,we can conclude that our ECNet is the better liver cancer diagnostic model related to state-of-the-art classifiers.

A high-precision image classification network model based on a voting mechanism

With the development of satellite remote sensing technology,image classification task,as the basis of remote sensing data interpretation,has received wide attention to improving accuracy and robustness.At the same time,in-depth learning technology has been widely used in remote sensing and has a far-reaching impact.Since the existing image classification methods ignore the feature that the general image semantics are the same as the semantics of a single pixel,this paper presents an algorithm that uses the semantics of an image to achieve high-precision image classification.Based on the idea of partial substitution for global,this algorithm designs a split result voting mechanism and builds a Vgg-Vote network model.This mechanism votes on the semantically segmented result of an image and uses the maximum filtering function to select the category containing the most significant number of pixels as the prediction category of the image.Experiments on UC Merced Land-User complete datasets and five types of incomplete datasets with varying degrees of interference,including noise,data occlusion and loss,show that the Vote mechanism dramatically improves the classification accuracy,robustness and anti-jamming capability of Vgg-Vote.

Incentive Compatibility of Voting Procedure

The present paper investigates the problem of incentive compatibility of voting mechanism and shows that the mechanism of approval voting is incentive-compatible,i.e. its mechanism gives voters incentive for voting sincerely.

A new physical unclonable function architecture

This paper describes a new silicon physical unclonable function （PUF） architecture that can be fabri- cated on a standard CMOS process. Our proposed architecture is built using process sensors, difference amplifier, comparator, voting mechanism and diffusion algorithm circuit. Multiple identical process sensors are fabricated on the same chip. Due to manufacturing process variations, each sensor produces slightly different physical charac- teristic values that can be compared in order to create a digital identification for the chip. The diffusion algorithm circuit ensures further that the PUF based on the proposed architecture is able to effectively identify a population of ICs. We also improve the stability of PUF design with respect to temporary environmental variations like temperature and supply voltage with the introduction of difference amplifier and voting mechanism. The PUF built on the proposed architecture is fabricated in 0.18 μm CMOS technology. Experimental results show that the PUF has a good output statistical characteristic of uniform distribution and a high stability of 98.1% with respect to temperature variation from -40 to 100 ℃, and supply voltage variation from 1.7 to 1.9 V.