Computer methods and programs in biomedicine
Aug 24, 2022
BACKGROUND AND OBJECTIVE: Breast lesions segmentation is an important step of computer-aided diagnosis system. However, speckle noise, heterogeneous structure, and similar intensity distributions bring challenges for breast lesion segmentation.
Although the categorization of ultrasound using the Breast Imaging Reporting and Data System (BI-RADS) has become widespread worldwide, the problem of inter-observer variability remains. To maintain uniformity in diagnostic accuracy, we have develope...
OBJECTIVE: To develop novel deep learning network (DLN) with the incorporation of the automatic segmentation network (ASN) for morphological analysis and determined the performance for diagnosis breast cancer in automated breast ultrasound (ABUS).
PURPOSE: To investigate the ability of deep learning (DL) using convolutional neural networks (CNNs) for distinguishing between normal and metastatic axillary lymph nodes on ultrasound images by comparing the diagnostic performance of radiologists.
Deep learning methods, especially convolutional neural networks, have been successfully applied to lesion segmentation in breast ultrasound (BUS) images. However, pattern complexity and intensity similarity between the surrounding tissues (i.e., back...
After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for...
In this paper, we propose a novel deep learning method for joint classification and segmentation of breast masses based on radio-frequency (RF) ultrasound (US) data. In comparison to commonly used classification and segmentation techniques, utilizing...
PURPOSE: Automatic classification and segmentation of tumors in breast ultrasound images enables better diagnosis and planning treatment strategies for breast cancer patients.
Computer methods and programs in biomedicine
Dec 31, 2021
Deep learning methods, especially convolutional neural networks, have advanced the breast lesion classification task using breast ultrasound (BUS) images. However, constructing a highly-accurate classification model still remains challenging due to c...
Conventional deep learning (DL) algorithm requires full supervision of annotating the region of interest (ROI) that is laborious and often biased. We aimed to develop a weakly-supervised DL algorithm that diagnosis breast cancer at ultrasound without...