Saved in:
| Main Authors: | Yang, Yongquan, Li, Fengling, Wei, Yani, Zhao, Yuanyuan, Fu, Jing, Xiao, Xiuli, Bu, Hong |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2306.10805 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy
by: Malhaire, Caroline, et al.
Published: (2025)
by: Malhaire, Caroline, et al.
Published: (2025)
Breast Cancer: Habitat imaging based on intravoxel incoherent motion for predicting pathologic complete response to neoadjuvant chemotherapy
by: Hui Zhang, et al.
Published: (2025)
by: Hui Zhang, et al.
Published: (2025)
A two-stage dual-task learning strategy for early prediction of pathological complete response to neoadjuvant chemotherapy for breast cancer using dynamic contrast-enhanced magnetic resonance images
by: Jing, Bowen, et al.
Published: (2025)
by: Jing, Bowen, et al.
Published: (2025)
K‐means clustering‐based analysis of quantitative ultrafast DCE‐MRI for predicting breast cancer response to neoadjuvant chemotherapy
by: Zhen Ren, et al.
Published: (2025)
by: Zhen Ren, et al.
Published: (2025)
An ultrasound‐based nomogram for predicting axillary node pathologic complete response after neoadjuvant chemotherapy in breast cancer: Modeling and external validation
by: Qijun Zheng, et al.
Published: (2024)
by: Qijun Zheng, et al.
Published: (2024)
Experts' cognition-driven safe noisy labels learning for precise segmentation of residual tumor in breast cancer
by: Yang, Yongquan, et al.
Published: (2023)
by: Yang, Yongquan, et al.
Published: (2023)
Integrating multiparametric MRI radiomics and clinical models to assess sensitivity to neoadjuvant chemotherapy in breast cancer: A multicenter study
by: Xinyi Zeng, et al.
Published: (2025)
by: Xinyi Zeng, et al.
Published: (2025)
A latent profile analysis of self‐advocacy and their relationship to differences in unmet cancer needs in patients with breast cancer chemotherapy
by: Zhimin Liu, et al.
Published: (2025)
by: Zhimin Liu, et al.
Published: (2025)
Long‐term outcomes and risk profile of cT3N0 breast cancer treated with neoadjuvant chemotherapy and curative surgery
by: Young Seob Shin, et al.
Published: (2024)
by: Young Seob Shin, et al.
Published: (2024)
Clinical target volume (CTV) automatic delineation using deep learning network for cervical cancer radiotherapy: A study with external validation
by: Zhe Wu, et al.
Published: (2024)
by: Zhe Wu, et al.
Published: (2024)
The contrast‐free diffusion MRI multiple index for the early prediction of pathological response to neoadjuvant chemotherapy in breast cancer
by: Lina Zhang, et al.
Published: (2024)
by: Lina Zhang, et al.
Published: (2024)
Monitoring of neoadjuvant chemotherapy through time domain diffuse optics: Breast tissue composition changes and collagen discriminative potential
by: Mule, Nikhitha, et al.
Published: (2024)
by: Mule, Nikhitha, et al.
Published: (2024)
Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer
by: Amin Talebi, et al.
Published: (2024)
by: Amin Talebi, et al.
Published: (2024)
The prevalence and clinical significance of residual occult breast cancer after neoadjuvant chemotherapy: reassessing surgical pathology in cases initially described as pathological complete response
by: Di Ai, et al.
Published: (2025)
by: Di Ai, et al.
Published: (2025)
Clinicopathologic and molecular correlates to neoadjuvant chemotherapy‐induced pathologic response in breast angiosarcoma
by: Hsin‐Yi Chang, et al.
Published: (2024)
by: Hsin‐Yi Chang, et al.
Published: (2024)
Transformer‐based deep learning for predicting brain tumor recurrence using magnetic resonance imaging
by: Qiuyu Zhou, et al.
Published: (2025)
by: Qiuyu Zhou, et al.
Published: (2025)
Enhancing automated right‐sided early‐stage breast cancer treatments via deep learning model adaptation without additional training
by: Michele Zeverino, et al.
Published: (2025)
by: Michele Zeverino, et al.
Published: (2025)
A 3D deep learning model based on MRI for predicting lymphovascular invasion in rectal cancer
by: Tangjuan Wang, et al.
Published: (2025)
by: Tangjuan Wang, et al.
Published: (2025)
Support vector machine‐based MRI radiomics predict response to neoadjuvant therapy and progression‐free‐survival of Colorectal Liver Metastases patients
by: Zhuo‐fu Li, et al.
Published: (2026)
by: Zhuo‐fu Li, et al.
Published: (2026)
A comparative analysis of deep learning architectures with data augmentation and multichannel input for locoregional breast cancer radiotherapy
by: Rosalie Klarenberg, et al.
Published: (2025)
by: Rosalie Klarenberg, et al.
Published: (2025)
Nomogram for predicting early shoulder joint dysfunction after breast cancer surgery
by: Jing Yu, et al.
Published: (2025)
by: Jing Yu, et al.
Published: (2025)
A review of deep learning approaches for multimodal image segmentation of liver cancer
by: Chaopeng Wu, et al.
Published: (2024)
by: Chaopeng Wu, et al.
Published: (2024)
Phantom evaluation of spectral performance in photon‐counting CT for breast cancer imaging
by: Liqiang Ren, et al.
Published: (2026)
by: Liqiang Ren, et al.
Published: (2026)
Technical note: Characterization, validation, and spectral optimization of a dedicated breast CT system for contrast‐enhanced imaging
by: Juan J. Pautasso, et al.
Published: (2024)
by: Juan J. Pautasso, et al.
Published: (2024)
Narrative review of neoadjuvant therapy in patients with locally advanced colon cancer
by: Jen‐Pin Chuang, et al.
Published: (2024)
by: Jen‐Pin Chuang, et al.
Published: (2024)
Data harvesting vs data farming: A study of the importance of variation vs sample size in deep learning-based auto-segmentation for breast cancer patients
by: Buhl, ES, et al.
Published: (2024)
by: Buhl, ES, et al.
Published: (2024)
Molecular and genomic advances in breast cancer: A comprehensive review of predictive and therapeutic innovations
by: Samina Malik, et al.
Published: (2026)
by: Samina Malik, et al.
Published: (2026)
Towards order of magnitude X-ray dose reduction in breast cancer imaging using phase contrast and deep denoising
by: Pakzad, Ashkan, et al.
Published: (2025)
by: Pakzad, Ashkan, et al.
Published: (2025)
Comparing DWI image quality of deep-learning-reconstructed EPI with RESOLVE in breast lesions at 3.0T: a pilot study
by: Tsarouchi, Marialena I., et al.
Published: (2024)
by: Tsarouchi, Marialena I., et al.
Published: (2024)
An automated patient‐specific segment reduction‐based beam angle optimization technique for deep learning auto‐planning for early breast cancer
by: Michele Zeverino, et al.
Published: (2025)
by: Michele Zeverino, et al.
Published: (2025)
Robust synchrotron-based deep learning algorithm for intracochlear segmentation in clinical scans: development and international validation
by: Micuda, Ashley, et al.
Published: (2026)
by: Micuda, Ashley, et al.
Published: (2026)
Radiation therapy response prediction for head and neck cancer using multimodal imaging and multiview dynamic graph autoencoder feature selection
by: Amir Moslemi, et al.
Published: (2025)
by: Amir Moslemi, et al.
Published: (2025)
Photoacoustic image reconstruction via deep learning
by: Antholzer, Stephan, et al.
Published: (2019)
by: Antholzer, Stephan, et al.
Published: (2019)
Surface guided ring gantry radiotherapy in deep inspiration breath hold for breast cancer patients
by: Mustafa Kadhim, et al.
Published: (2024)
by: Mustafa Kadhim, et al.
Published: (2024)
Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning
by: Yang, Xilin, et al.
Published: (2024)
by: Yang, Xilin, et al.
Published: (2024)
Factors associated with the onset and survival of subsequent primary breast cancer in female non‐metastatic breast cancer survivors
by: Shunshun Liang, et al.
Published: (2024)
by: Shunshun Liang, et al.
Published: (2024)
Enhancing patient‐specific quality assurance for VMAT for breast cancer treatment: A machine learning approach for gamma passing rate (GPR) prediction
by: Francis C. Djoumessi Zamo, et al.
Published: (2025)
by: Francis C. Djoumessi Zamo, et al.
Published: (2025)
Deep learning in image segmentation for cancer
by: Robba Rai
Published: (2024)
by: Robba Rai
Published: (2024)
Parameter map guided explainable segmentation framework for breast cancer using amide proton transfer weighted imaging
by: Qiuhui Yang, et al.
Published: (2024)
by: Qiuhui Yang, et al.
Published: (2024)
Improving textural realism in breast phantom images
by: Luana de M. Omena, et al.
Published: (2026)
by: Luana de M. Omena, et al.
Published: (2026)
Similar Items
-
Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy
by: Malhaire, Caroline, et al.
Published: (2025) -
Breast Cancer: Habitat imaging based on intravoxel incoherent motion for predicting pathologic complete response to neoadjuvant chemotherapy
by: Hui Zhang, et al.
Published: (2025) -
A two-stage dual-task learning strategy for early prediction of pathological complete response to neoadjuvant chemotherapy for breast cancer using dynamic contrast-enhanced magnetic resonance images
by: Jing, Bowen, et al.
Published: (2025) -
K‐means clustering‐based analysis of quantitative ultrafast DCE‐MRI for predicting breast cancer response to neoadjuvant chemotherapy
by: Zhen Ren, et al.
Published: (2025) -
An ultrasound‐based nomogram for predicting axillary node pathologic complete response after neoadjuvant chemotherapy in breast cancer: Modeling and external validation
by: Qijun Zheng, et al.
Published: (2024)