Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography [PDF]
Artificial Intelligence In Medicine, 142(2023), 102555, 2023, 2021 Digital mammography is still the most common imaging tool for breast cancer screening. Although the benefits of using digital mammography for cancer screening outweigh the risks associated with the x-ray exposure, the radiation dose must be kept as low as possible while maintaining the diagnostic utility of the generated images, thus minimizing patient
arxiv +1 more source
Rayleigh imaging in spectral mammography [PDF]
Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging,
97830A (2016), 2021 Spectral imaging is the acquisition of multiple images of an object at different energy spectra. In mammography, dual-energy imaging (spectral imaging with two energy levels) has been investigated for several applications, in particular material decomposition, which allows for quantitative analysis of breast composition and quantitative contrast ...
arxiv +1 more source
Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification [PDF]
16th International Workshop on Breast Imaging (IWBI2022). 12286.
2022. 169 -- 177, 2022 Early detection of breast cancer in mammography screening via deep-learning based computer-aided detection systems shows promising potential in improving the curability and mortality rates of breast cancer. However, many clinical centres are restricted in the amount and heterogeneity of available data to train such models to (i) achieve promising ...
arxiv +1 more source
A Deep Learning Approach for Virtual Contrast Enhancement in Contrast Enhanced Spectral Mammography [PDF]
Computerized Medical Imaging and Graphics 116 (2024) 102398, 2023 Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic imaging technique that first needs intravenously administration of an iodinated contrast medium; then, it collects both a low-energy image, comparable to standard mammography, and a high-energy image.
arxiv +1 more source
Analysis of supervised and semi-supervised GrowCut applied to segmentation of masses in mammography images [PDF]
Computer Methods in Biomechanics and Biomedical Engineering:
Imaging & Visualization, v. 5, p. 1-19, 2017, 2017 Breast cancer is already one of the most common form of cancer worldwide. Mammography image analysis is still the most effective diagnostic method to promote the early detection of breast cancer. Accurately segmenting tumors in digital mammography images is important to improve diagnosis capabilities of health specialists and avoid misdiagnosis.
arxiv +1 more source
VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography [PDF]
arXiv, 2022 Mammography, or breast X-ray, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe or CADx) tools have been developed to support physicians and improve the accuracy of interpreting mammography.
arxiv
Deep Curriculum Learning in Task Space for Multi-Class Based Mammography Diagnosis [PDF]
arXiv, 2021 Mammography is used as a standard screening procedure for the potential patients of breast cancer. Over the past decade, it has been shown that deep learning techniques have succeeded in reaching near-human performance in a number of tasks, and its application in mammography is one of the topics that medical researchers most concentrate on.
arxiv
Independent evaluation of state-of-the-art deep networks for mammography [PDF]
arXiv, 2022 Deep neural models have shown remarkable performance in image recognition tasks, whenever large datasets of labeled images are available. The largest datasets in radiology are available for screening mammography. Recent reports, including in high impact journals, document performance of deep models at or above that of trained radiologists.
arxiv
Improving Mass Detection in Mammography Images: A Study of Weakly Supervised Learning and Class Activation Map Methods [PDF]
arXiv, 2023 In recent years, weakly supervised models have aided in mass detection using mammography images, decreasing the need for pixel-level annotations. However, most existing models in the literature rely on Class Activation Maps (CAM) as the activation method, overlooking the potential benefits of exploring other activation techniques.
arxiv
Supervised Machine Learning Algorithm for Detecting Consistency between Reported Findings and the Conclusions of Mammography Reports [PDF]
arXiv, 2022 Objective. Mammography reports document the diagnosis of patients' conditions. However, many reports contain non-standard terms (non-BI-RADS descriptors) and incomplete statements, which can lead to conclusions that are not well-supported by the reported findings.
arxiv