Convolutional neural networks for breast cancer detection in mammography: A survey
Despite its proven record as a breast cancer screening tool, mammography remains labor-
intensive and has recognized limitations, including low sensitivity in women with dense …
intensive and has recognized limitations, including low sensitivity in women with dense …
Multi-view analysis of unregistered medical images using cross-view transformers
G Van Tulder, Y Tong, E Marchiori - … –October 1, 2021, Proceedings, Part III …, 2021 - Springer
Multi-view medical image analysis often depends on the combination of information from
multiple views. However, differences in perspective or other forms of misalignment can make …
multiple views. However, differences in perspective or other forms of misalignment can make …
Breast cancer detection and classification in mammogram using a three-stage deep learning framework based on PAA algorithm
J Jiang, J Peng, C Hu, W Jian, X Wang, W Liu - Artificial Intelligence in …, 2022 - Elsevier
In recent years, deep learning has been used to develop an automatic breast cancer
detection and classification tool to assist doctors. In this paper, we proposed a three-stage …
detection and classification tool to assist doctors. In this paper, we proposed a three-stage …
[HTML][HTML] Domain generalization in deep learning based mass detection in mammography: A large-scale multi-center study
Computer-aided detection systems based on deep learning have shown great potential in
breast cancer detection. However, the lack of domain generalization of artificial neural …
breast cancer detection. However, the lack of domain generalization of artificial neural …
Exploiting patch sizes and resolutions for multi-scale deep learning in mammogram image classification
Recent progress in deep learning (DL) has revived the interest on DL-based computer aided
detection or diagnosis (CAD) systems for breast cancer screening. Patch-based approaches …
detection or diagnosis (CAD) systems for breast cancer screening. Patch-based approaches …
[HTML][HTML] Attention-map augmentation for hypercomplex breast cancer classification
Breast cancer is the most widespread neoplasm among women and early detection of this
disease is critical. Deep learning techniques have become of great interest to improve …
disease is critical. Deep learning techniques have become of great interest to improve …
DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms Using Self-adversarial Learning
Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when abnormalities
are developing. It is widely utilized by radiologists for diagnosis. The question of “what the …
are developing. It is widely utilized by radiologists for diagnosis. The question of “what the …
Transformer based multi-view network for mammographic image classification
Z Sun, H Jiang, L Ma, Z Yu, H Xu - International Conference on Medical …, 2022 - Springer
Most of the existing multi-view mammographic image analysis methods adopt a simple
fusion strategy: features concatenation, which is widely used in many features fusion …
fusion strategy: features concatenation, which is widely used in many features fusion …
Learning multi-frequency features in convolutional network for mammography classification
Breast cancer is a common life-threatening disease among women. Computer-aided
methods can provide second opinion or decision support for early diagnosis in …
methods can provide second opinion or decision support for early diagnosis in …
Deep learning model for breast cancer diagnosis based on bilateral asymmetrical detection (BilAD) in digital breast tomosynthesis images
D Shimokawa, K Takahashi, D Kurosawa… - … Physics and Technology, 2023 - Springer
The purpose of this study was to develop a deep learning model to diagnose breast cancer
by embedding a diagnostic algorithm that examines the asymmetry of bilateral breast tissue …
by embedding a diagnostic algorithm that examines the asymmetry of bilateral breast tissue …