首页|Northeastern University Reports Findings in Cervical Cancer (CAISHI: A benchmark histopathological H&E image dataset for cervical adenocarcinoma in situ identification, retrieval and few-shot learning evaluation)
Northeastern University Reports Findings in Cervical Cancer (CAISHI: A benchmark histopathological H&E image dataset for cervical adenocarcinoma in situ identification, retrieval and few-shot learning evaluation)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Cervical Ca ncer is the subject of a report. According to news reporting originating from Li aoning, People's Republic of China, by NewsRx correspondents, research stated, " A benchmark histopathological Hematoxylin and Eosin (H&E) image dat aset for Cervical Adenocarcinoma (CAISHI), containing 2240 histopathological ima ges of Cervical Adenocarcinoma (AIS), is established to fill the current data ga p, of which 1010 are images of normal cervical glands and another 1230 are image s of cervical AIS. The sampling method is endoscope biopsy." Our news editors obtained a quote from the research from Northeastern University, "Pathological sections are obtained by H&E staining from Shengjin g Hospital, China Medical University. These images have a magnification of 100 a nd are captured by the Axio Scope. A1 microscope. The size of the image is 3840 x 2160 pixels, and the format is ‘.png'. The collection of CAISHI is subject to an ethical review by China Medical University with approval number 2022PS841K. T hese images are analyzed at multiple levels, including classification tasks and image retrieval tasks. A variety of computer vision and machine learning methods are used to evaluate the performance of the data. For classification tasks, a v ariety of classical machine learning classifiers such as -means, support vector machines (SVM), and random forests (RF), as well as convolutional neural network classifiers such as Residual Network 50 (ResNet50), Vision Transformer (ViT), I nception version 3 (Inception-V3), and Visual Geometry Group Network 16 (VGG-16), are used. In addition, the Siamese network is used to evaluate few-shot learni ng tasks. In terms of image retrieval functions, color features, texture feature s, and deep learning features are extracted, and their performances are tested. CAISHI can help with the early diagnosis and screening of cervical cancer."
LiaoningPeople's Republic of ChinaAs iaAdenocarcinomaCancerCervical CancerCyborgsEmerging TechnologiesHea lth and MedicineMachine LearningOncologyWomen's Health
NETL
NSTL
