Histopathological characteristics of adenomyosis: structure and microstructure

Adenomyosis is a benign uterine disease that pathologically shows endometrial glands and stroma in the myometrium. There are multiple lines of evidence that adenomyosis is associated with abnormal bleeding, painful menstruation, chronic pelvic pain, infertility, and spontaneous pregnancy loss. Pathologists have researched adenomyosis by studying tissue specimens from its first report more than 150 years ago, and differing viewpoints on its pathological alterations have been advanced. However, the gold standard histopathological definition of adenomyosis remains controversial to date. The diagnostic accuracy of adenomyosis has steadily increased due to the continual identification of unique molecular markers. This article provides a brief description of the pathological aspects of adenomyosis and discusses adenomyosis categorization based on histology. The clinical findings of uncommon adenomyosis are also presented to offer a thorough and detailed pathological profile. Furthermore, we describe the histolo

Real-Time Vehicle Detection from Short-range Aerial Image with Compressed MobileNet

Vehicle detection from short-range aerial image faces challenges including vehicle blocking, irrelevant object interference, motion blurring, color variation etc., leading to the difficulty to achieve high detection accuracy and real-time detection speed. In this paper, benefiting from the recent development in MobileNet family network engineering, we propose a compressed MobileNet which is not only internally resistant to the above listed challenges but also gains the best detection accuracy/speed tradeoff when comparing with the original MobileNet. In a nutshell, we reduce the bottleneck architecture number during the feature map downsampling stage but add more bottlenecks during the feature map plateau stage, neither extra FLOPs nor parameters are thus involved but reduced inference time and better accuracy are expected. We conduct experiment on our collected 5-k short-range aerial images, containing six vehicle categories: truck, car, bus, bicycle, motorcycle, crowded bicycles and crowded motorcycles. Our proposed compressed MobileNet achieves 110 FPS (GPU), 31 FPS (CPU) and 15 FPS (mobile phone), 1.2 times faster and 2% more accurate (mAP) than the original MobileNet