Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In order to eliminate the operator dependency and improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is a valuable and beneficial means for breast cancer detection and classification. Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification. In this paper, the approaches used in these stages are summarized and their advantages and disadvantages are discussed. The performance evaluation of CAD system is investigated as well.

Abstract-Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture. This paper addresses these limitations by proposing a method to directly classify cervical cells -without prior segmentation -based on deep features, using convolutional neural networks (ConvNets). First, the ConvNet is pre-trained on a natural image dataset. It is subsequently fine-tuned on a cervical cell dataset consisting of adaptively re-sampled image patches coarsely centered on the nuclei. In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets. Results show that our method outperforms previous algorithms in classification accuracy (98.3%), area under the curve (AUC) (0.99) values, and especially specificity (98.3%), when applied to the Herlev benchmark Pap smear dataset and evaluated using five-fold cross-validation. Similar superior performances are also achieved on the HEMLBC (H&E stained manual LBC) dataset. Our method is promising for the development of automation-assisted reading systems in primary cervical screening.

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Summary Six transcription factors of APETALA2/ethylene‐response factor (AP2/ERF) family were cloned and analyzed in Artemisia annua. Real‐time quantitative polymerase chain reaction (RT‐Q‐PCR) showed that AaORA exhibited similar expression patterns to those of amorpha‐4,11‐diene synthase gene (ADS), cytochrome P450‐dependent hydroxylase gene (CYP71AV1) and double bond reductase 2 gene (DBR2) in different tissues of A. annua. AaORA is a trichome‐specific transcription factor, which is expressed in both glandular secretory trichomes (GSTs) and nonglandular T‐shaped trichomes (TSTs) of A. annua. The result of subcellular localization shows that AaORA is targeted to the nuclei and the cytoplasm. Overexpression and RNA interference (RNAi) of AaORA in A. annua regulated, positively and significantly, the expression levels of ADS, CYP71AV1, DBR2 and AaERF1. The up‐regulated or down‐regulated expression levels of these genes resulted in a significant increase or decrease in artemisinin and dihydroartemisinic acid. The results demonstrate that AaORA is a positive regulator in the biosynthesis of artemisinin. Overexpression of AaORA in Arabidopsis thaliana increased greatly the transcript levels of the defense marker genes PLANT DEFENSIN1.2 (PDF1.2), HEVEIN‐LIKE PROTEIN (HEL) and BASIC CHITINASE (B‐CHI). After inoculation with Botrytis cinerea, the phenotypes of AaORA overexpression in A. thaliana and AaORA RNAi in A. annua demonstrate that AaORA is a positive regulator of disease resistance to B. cinerea.