Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches
Breast cancer is a significant global health burden, with increasing morbidity and mortality
worldwide. Early screening and accurate diagnosis are crucial for improving prognosis …
worldwide. Early screening and accurate diagnosis are crucial for improving prognosis …
A bottom-up review of image analysis methods for suspicious region detection in mammograms
Breast cancer is one of the most common death causes amongst women all over the world.
Early detection of breast cancer plays a critical role in increasing the survival rate. Various …
Early detection of breast cancer plays a critical role in increasing the survival rate. Various …
Arf-net: An adaptive receptive field network for breast mass segmentation in whole mammograms and ultrasound images
UNet adopting an encoder-decoder structure has been used widely in medical image
segmentation tasks for its outstanding performance. However, in our work, we find that UNet …
segmentation tasks for its outstanding performance. However, in our work, we find that UNet …
Deep multiscale multi-instance networks with regional scoring for mammogram classification
Mammography is one of the most commonly used methods for breast cancer screening.
Most mammogram classification or segmentation models are trained with additional manual …
Most mammogram classification or segmentation models are trained with additional manual …
Deep Learning-Based Computer-Aided Diagnosis Model for the Identification and Classification of Mammography Images
Cancer of the breast is an illness that has the potential to be fatal for females all over the
world. Even with the advancements that have been made in treatment, breast cancer cannot …
world. Even with the advancements that have been made in treatment, breast cancer cannot …
Breast tumor segmentation in digital mammograms using spiculated regions
H Pezeshki - Biomedical Signal Processing and Control, 2022 - Elsevier
Mammogram image segmentation is the process of partitioning mammograms into
meaningful and separate areas. However, during the segmentation process, masses are …
meaningful and separate areas. However, during the segmentation process, masses are …
Breast lesions screening of mammographic images with 2D spatial and 1D convolutional neural network-based classifier
CH Lin, HY Lai, PY Chen, JX Wu, CC Pai, CM Su… - Applied Sciences, 2022 - mdpi.com
Mammography is a first-line imaging examination that employs low-dose X-rays to rapidly
screen breast tumors, cysts, and calcifications. This study proposes a two-dimensional (2D) …
screen breast tumors, cysts, and calcifications. This study proposes a two-dimensional (2D) …
基于联邦学习的多线路高速列车转向架故障诊断
杜家豪， 秦娜， 贾鑫明， 张一鸣， 黄德青 - 西南交通大学学报, 2022 - xnjdxb.swjtu.edu.cn
单一线路高速列车转向架缺少足量故障数据特征, 导致故障诊断模型泛化能力有限,
为实现诊断多条线路高速列车的转向架故障, 提出一种基于联邦学习的转向架全局故障诊断方法 …
为实现诊断多条线路高速列车的转向架故障, 提出一种基于联邦学习的转向架全局故障诊断方法 …
Improved Loss Function for Mass Segmentation in Mammography Images Using Density and Mass Size
Mass segmentation is one of the fundamental tasks used when identifying breast cancer due
to the comprehensive information it provides, including the location, size, and border of the …
to the comprehensive information it provides, including the location, size, and border of the …
Mammogram mass segmentation and classification based on cross-view VAE and spatial hidden factor disentanglement
Y Ma, Y Peng - Physical and Engineering Sciences in Medicine, 2024 - Springer
Breast masses are the most important clinical findings of breast carcinomas. The mass
segmentation and classification in mammograms remain a crucial yet challenging topic in …
segmentation and classification in mammograms remain a crucial yet challenging topic in …