Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …
imaging. However, these approaches primarily focus on supervised learning, assuming that …
Medical image segmentation with domain adaptation: a survey
Deep learning (DL) has shown remarkable success in various medical imaging data
analysis applications. However, it remains challenging for DL models to achieve good …
analysis applications. However, it remains challenging for DL models to achieve good …
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of medical image
analysis. However, despite these achievements, the further enhancement of deep learning …
analysis. However, despite these achievements, the further enhancement of deep learning …
One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs
J Jiang, H Veeraraghavan - IEEE transactions on medical …, 2022 - ieeexplore.ieee.org
Image-guided adaptive lung radiotherapy requires accurate tumor and organs segmentation
from during treatment cone-beam CT (CBCT) images. Thoracic CBCTs are hard to segment …
from during treatment cone-beam CT (CBCT) images. Thoracic CBCTs are hard to segment …
Exposing semantic segmentation failures via maximum discrepancy competition
Semantic segmentation is an extensively studied task in computer vision, with numerous
methods proposed every year. Thanks to the advent of deep learning in semantic …
methods proposed every year. Thanks to the advent of deep learning in semantic …
Ensemble learning and tensor regularization for cone‐beam computed tomography‐based pelvic organ segmentation
Purpose Cone‐beam computed tomography (CBCT) is a widely accessible low‐dose
imaging approach compatible with on‐table patient anatomy observation for radiotherapy …
imaging approach compatible with on‐table patient anatomy observation for radiotherapy …
Soft-cp: A credible and effective data augmentation for semantic segmentation of medical lesions
P Dai, L Dong, R Zhang, H Zhu, J Wu… - arXiv preprint arXiv …, 2022 - arxiv.org
The medical datasets are usually faced with the problem of scarcity and data imbalance.
Moreover, annotating large datasets for semantic segmentation of medical lesions is domain …
Moreover, annotating large datasets for semantic segmentation of medical lesions is domain …
A proof-of-concept study of artificial intelligence–assisted contour editing
Purpose To present a concept called artificial intelligence–assisted contour editing (AIACE)
and demonstrate its feasibility. Materials and Methods The conceptual workflow of AIACE is …
and demonstrate its feasibility. Materials and Methods The conceptual workflow of AIACE is …
[PDF][PDF] 用于肿瘤调强放射治疗影像分析与转换的深度学习方法
刘国才， 顾冬冬， 刘骁， 刘劲光， 刘焰飞… - 中国生物医学工程 …, 2022 - cjbme.csbme.org
癌症已成为严重威胁人类健康的主要公共卫生问题ꎮ 60%~ 70% 的癌症患者需要进行放射治疗
ꎮ 调强放疗是当前主要的临床放疗技术ꎮ 对近几年基于深度学习的影像分析与转换方法在肿瘤 …
ꎮ 调强放疗是当前主要的临床放疗技术ꎮ 对近几年基于深度学习的影像分析与转换方法在肿瘤 …
Dosimetric assessment of patient dose calculation on a deep learning‐based synthesized computed tomography image for adaptive radiotherapy
Purpose Dose computation using cone beam computed tomography (CBCT) images is
inaccurate for the purpose of adaptive treatment planning. The main goal of this study is to …
inaccurate for the purpose of adaptive treatment planning. The main goal of this study is to …