Generative adversarial networks in medical image segmentation: A review
S Xun, D Li, H Zhu, M Chen, J Wang, J Li… - Computers in biology …, 2022 - Elsevier
Abstract Purpose Since Generative Adversarial Network (GAN) was introduced into the field
of deep learning in 2014, it has received extensive attention from academia and industry …
of deep learning in 2014, it has received extensive attention from academia and industry …
Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey
Recent advancements with deep generative models have proven significant potential in the
task of image synthesis, detection, segmentation, and classification. Segmenting the medical …
task of image synthesis, detection, segmentation, and classification. Segmenting the medical …
Dodnet: Learning to segment multi-organ and tumors from multiple partially labeled datasets
Due to the intensive cost of labor and expertise in annotating 3D medical images at a voxel
level, most benchmark datasets are equipped with the annotations of only one type of …
level, most benchmark datasets are equipped with the annotations of only one type of …
Kidney tumor semantic segmentation using deep learning: A survey of state-of-the-art
A Abdelrahman, S Viriri - Journal of imaging, 2022 - mdpi.com
Cure rates for kidney cancer vary according to stage and grade; hence, accurate diagnostic
procedures for early detection and diagnosis are crucial. Some difficulties with manual …
procedures for early detection and diagnosis are crucial. Some difficulties with manual …
Deep learning techniques in liver tumour diagnosis using CT and MR imaging-A systematic review
B Lakshmipriya, B Pottakkat, G Ramkumar - Artificial Intelligence in …, 2023 - Elsevier
Deep learning has become a thriving force in the computer aided diagnosis of liver cancer,
as it solves extremely complicated challenges with high accuracy over time and facilitates …
as it solves extremely complicated challenges with high accuracy over time and facilitates …
Divergentnets: Medical image segmentation by network ensemble
Detection of colon polyps has become a trending topic in the intersecting fields of machine
learning and gastrointestinal endoscopy. The focus has mainly been on per-frame …
learning and gastrointestinal endoscopy. The focus has mainly been on per-frame …
Computer vision approach for liver tumor classification using CT dataset
M Hussain, N Saher, S Qadri - Applied Artificial Intelligence, 2022 - Taylor & Francis
The liver tumor is one of the most foremost critical causes of death in the world. Nowadays,
Medical Imaging (MI) is one of the prominent Computer Vision fields (CV), which helps …
Medical Imaging (MI) is one of the prominent Computer Vision fields (CV), which helps …
Modality-specific segmentation network for lung tumor segmentation in PET-CT images
Lung tumor segmentation in PET-CT images plays an important role to assist physicians in
clinical application to accurately diagnose and treat lung cancer. However, it is still a …
clinical application to accurately diagnose and treat lung cancer. However, it is still a …
Automatic segmentation of lung tumors on CT images based on a 2D & 3D hybrid convolutional neural network
W Gan, H Wang, H Gu, Y Duan, Y Shao… - The British Journal of …, 2021 - academic.oup.com
Objective: A stable and accurate automatic tumor delineation method has been developed
to facilitate the intelligent design of lung cancer radiotherapy process. The purpose of this …
to facilitate the intelligent design of lung cancer radiotherapy process. The purpose of this …
Lung cancer tumor region segmentation using recurrent 3d-denseunet
The performance of a computer-aided automated diagnosis system of lung cancer from
Computed Tomography (CT) volumetric images greatly depends on the accurate detection …
Computed Tomography (CT) volumetric images greatly depends on the accurate detection …