Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.

In recent years, researches are concentrating on the effectiveness of Transfer Learning (TL) and Ensemble Learning (EL) techniques in cervical histopathology image analysis. However, there have been very few investigations that have described the stages of differentiation of cervical histopathological images. Therefore, in this article, we propose an Ensembled Transfer Learning (ETL) framework to classify well, moderate and poorly differentiated cervical histopathological images. First of all, we have developed Inception-V3, Xception, VGG-16, and Resnet-50 based TL structures. Then, to enhance the classification performance, a weighted voting based EL strategy is introduced. After that, to evaluate the proposed algorithm, a dataset consisting of 307 images, stained by three immunohistochemistry methods (AQP, HIF, and VEGF) is considered. In the experiment, we obtain the highest overall accuracy of 97.03% and 98.61% on AQP staining images and poor differentiation of VEGF staining images, individually. Finally, an additional experiment for classifying the benign cells from the malignant ones is carried out on the Herlev dataset and obtains an overall accuracy of 98.37%.

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The in situ exsolved nanoparticles from the perovskite matrix have achieved increasing attention and wide applications in solid oxide fuel cells (SOFCs) due to their excellent stability and high catalytic activity. Herein, an A-site-deficient (Ba 0.9 La 0.1 ) 0.95 Co 0.7 Fe 0.2 Nb 0.1 O 3 − δ (BL95CFN) perovskite oxide with in situ exsolved Co-Fe nanoparticles is developed and investigated as an anode for SOFCs. Compared with stoichiometric BLCFN, a tiny A-site deficiency (5%) promoted the exsolution of cobalt and iron from the perovskite matrix of BL95CFN. This hybrid anode catalyst showed excellent electrochemical performance. The polarization resistance is 0.11 Ω cm 2 for the BL95CFN anode at 750 °C, about 42% lower than that of the BLCFN anode. The maximum peak density (MPD) reaches 513.2 mW cm −2 at 750 °C for an electrolyte-supported single cell with the BL95CFN anode. The high electrochemical performance could be attributed to the accelerated hydrogen surface exchange process provided by exsolved Co-Fe nanoparticles and oxygen ion transport in the A-site-deficient perovskite matrix. High-resolution transmission electron microscope (HRTEM) reveals that exsolved Co-Fe nanoparticles are embedded in the oxide matrix, forming a firm anchoring structure, which ensures excellent anode coking tolerance. At the current density of 200 mA cm −2 , the output voltage of the single cell remained stable within 200 h in methane fuel. Overall, the high catalytic activity and coking tolerance of the hybrid electrocatalyst manifest a bright prospect of application in SOFCs and electrolytic cells.

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