[HTML][HTML] Breast cancer detection and diagnosis using mammographic data: Systematic review
Background Machine learning (ML) has become a vital part of medical imaging research.
ML methods have evolved over the years from manual seeded inputs to automatic …
ML methods have evolved over the years from manual seeded inputs to automatic …
Systematic review of computing approaches for breast cancer detection based computer aided diagnosis using mammogram images
Breast cancer is one of the most prevalent types of cancer that plagues females. Mortality
from breast cancer could be reduced by diagnosing and identifying it at an early stage. To …
from breast cancer could be reduced by diagnosing and identifying it at an early stage. To …
Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms
Abstract Background and Objective Deep learning detection and classification from medical
imagery are key components for computer-aided diagnosis (CAD) systems to efficiently …
imagery are key components for computer-aided diagnosis (CAD) systems to efficiently …
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Medical images differ from natural images in significantly higher resolutions and smaller
regions of interest. Because of these differences, neural network architectures that work well …
regions of interest. Because of these differences, neural network architectures that work well …
Thyroid diagnosis from SPECT images using convolutional neural network with optimization
L Ma, C Ma, Y Liu, X Wang - Computational intelligence and …, 2019 - Wiley Online Library
Thyroid disease has now become the second largest disease in the endocrine field; SPECT
imaging is particularly important for the clinical diagnosis of thyroid diseases. However …
imaging is particularly important for the clinical diagnosis of thyroid diseases. However …
Utilizing automated breast cancer detection to identify spatial distributions of tumor-infiltrating lymphocytes in invasive breast cancer
Quantitative assessment of spatial relations between tumor and tumor-infiltrating
lymphocytes (TIL) is increasingly important in both basic science and clinical aspects of …
lymphocytes (TIL) is increasingly important in both basic science and clinical aspects of …
Deep learning in selected cancers' image analysis—a survey
TG Debelee, SR Kebede, F Schwenker… - Journal of …, 2020 - mdpi.com
Deep learning algorithms have become the first choice as an approach to medical image
analysis, face recognition, and emotion recognition. In this survey, several deep-learning …
analysis, face recognition, and emotion recognition. In this survey, several deep-learning …
Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets
D Ueda, A Yamamoto, N Onoda, T Takashima, S Noda… - Plos one, 2022 - journals.plos.org
Objectives The objective of this study was to develop and validate a state-of-the-art, deep
learning (DL)-based model for detecting breast cancers on mammography. Methods …
learning (DL)-based model for detecting breast cancers on mammography. Methods …
Meta ordinal regression forest for medical image classification with ordinal labels
The performance of medical image classification has been enhanced by deep convolutional
neural networks (CNNs), which are typically trained with cross-entropy (CE) loss. However …
neural networks (CNNs), which are typically trained with cross-entropy (CE) loss. However …
Improving computer-aided detection for digital breast tomosynthesis by incorporating temporal change
Purpose To develop a deep learning algorithm that uses temporal information to improve the
performance of a previously published framework of cancer lesion detection for digital breast …
performance of a previously published framework of cancer lesion detection for digital breast …