Learning pre-and post-contrast representation for breast cancer segmentation in DCE-MRI
H Wu, Y Huo, Y Pan, Z Xu, R Huang… - 2022 IEEE 35th …, 2022 - ieeexplore.ieee.org
Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a
considerable role in high-risk breast cancer diagnosis and image-based prognostic …
considerable role in high-risk breast cancer diagnosis and image-based prognostic …
Prostate cancer segmentation from MRI by a multistream fusion encoder
M Jiang, B Yuan, W Kou, W Yan, H Marshall… - Medical …, 2023 - Wiley Online Library
Background Targeted prostate biopsy guided by multiparametric magnetic resonance
imaging (mpMRI) detects more clinically significant lesions than conventional systemic …
imaging (mpMRI) detects more clinically significant lesions than conventional systemic …
Modality-Specific Information Disentanglement From Multi-Parametric MRI for Breast Tumor Segmentation and Computer-Aided Diagnosis
Breast cancer is becoming a significant global health challenge, with millions of fatalities
annually. Magnetic Resonance Imaging (MRI) can provide various sequences for …
annually. Magnetic Resonance Imaging (MRI) can provide various sequences for …
Feature-enhanced multi-sequence MRI-based fusion mechanism for breast tumor segmentation
H Wang, T Zhu, S Ding, P Wang, B Chen - Biomedical Signal Processing …, 2024 - Elsevier
Multi-sequence MRI plays a crucial role in the effective segmentation of breast tumors,
contributing to accurate clinical diagnosis and treatment. However, the problem of missing …
contributing to accurate clinical diagnosis and treatment. However, the problem of missing …
[HTML][HTML] Edge of discovery: Enhancing breast tumor MRI analysis with boundary-driven deep learning
Manually segmenting breast lesion images poses a labor-intensive and expensive
undertaking for radiologists. Therefore, the adoption of an automated diagnostic approach …
undertaking for radiologists. Therefore, the adoption of an automated diagnostic approach …
CF2-Net: Coarse-to-fine fusion convolutional network for breast ultrasound image segmentation
Z Ning, K Wang, S Zhong, Q Feng, Y Zhang - arXiv preprint arXiv …, 2020 - arxiv.org
Breast ultrasound (BUS) image segmentation plays a crucial role in a computer-aided
diagnosis system, which is regarded as a useful tool to help increase the accuracy of breast …
diagnosis system, which is regarded as a useful tool to help increase the accuracy of breast …
Brain tumor segmentation for missing modalities by supplementing missing features
Y Zhu, S Wang, R Lin, Y Hu… - 2021 IEEE 6th …, 2021 - ieeexplore.ieee.org
Brain tumor segmentation in multi-modal magnetic resonance images is an essential step in
brain cancer diagnosis and treatment. Despite the recent success of multi-Modalities fusion …
brain cancer diagnosis and treatment. Despite the recent success of multi-Modalities fusion …
Deep Multimodal Fusion Network for the Retinogeniculate Visual Pathway Segmentation
The segmentation of the retinogeniculate visual pathway (RGVP) is a significant quantitative
tool for analyzing the anatomy and trajectory of individual RGVP. However, due to the …
tool for analyzing the anatomy and trajectory of individual RGVP. However, due to the …
A multimodal feature fusion image dehazing method with scene depth prior
Z Zhengpeng, C Yan, Z Shuai, B Lijing… - IET Image …, 2023 - Wiley Online Library
Current dehazing networks usually only learn haze features in a single‐image colour space
and often suffer from uneven dehazing, colour, and edge degradation when confronted with …
and often suffer from uneven dehazing, colour, and edge degradation when confronted with …
Joint Dense Residual and Recurrent Attention Network for DCE‐MRI Breast Tumor Segmentation
Breast cancer detection largely relies on imaging characteristics and the ability of clinicians
to easily and quickly identify potential lesions. Magnetic resonance imaging (MRI) of breast …
to easily and quickly identify potential lesions. Magnetic resonance imaging (MRI) of breast …