This paper presents a novel convolutional neural networks (CNN)-based approach for the detection of breast cancer in invasive ductal carcinoma tissue regions using whole slide images (WSI). It has been observed that breast cancer has been a leading cause of death among women. It also remains a...
A Deep Learning Method for Breast Cancer Classification in the Pathology Images, 视频播放量 21、弹幕量 0、点赞数 1、投硬币枚数 2、收藏人数 0、转发人数 0, 视频作者 算法咖, 作者简介 每天进步一点点。信息时代,新知识浩如烟海,不断拓展知识。,相关视频:[AI视
Deep Learning Prediction of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Clinical Implication-Applied Preprocessed CT Images Background: Accurate detection of axillary lymph node (ALN) metastases in breast cancer is crucial for clinical staging and treatment planning. This study ... TY...
DRDA-Net: Dense residual dual-shuffle attention network for breast cancer classification using histopathological images Deep learningHistopathology imagesBreast cancer is caused by the uncontrolled growth and division of cells in the breast, whereby a mass of tissue called a... S Chattopadhyay,A Dey,...
An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer Deep learning for medical image analysis is a promising new avenue to predict treatment response, however the clinical application of these methods has been so far limited. Here,...
The rapid development of deep learning, a family of machine learning techniques, has spurred much interest in its application to medical imaging problems. Here, we develop a deep learning algorithm that can accurately detect breast cancer on screening mammograms using an “end-to-end” training app...
Cancerous brain tumor detection using hybrid deep learning framework Deep learning has exploded in popularity in recent years, particularly in medical image processing, medical image analysis, and bioinformatics. As a result,... S Kothari,S Chiwhane,S Jain,... - 《Indonesian Journal of Electrical...
Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all micr...
Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer cells
Because histologic types are subjective and difficult to reproduce between pathologists, tissue morphology often takes a back seat to molecular testing for the selection of breast cancer treatments. This work explores whether a deep-learning algorithm can learn objective histologic H&E features that pred...