Recent progress in transformer-based medical image analysis
The transformer is primarily used in the field of natural language processing. Recently, it has
been adopted and shows promise in the computer vision (CV) field. Medical image analysis …
been adopted and shows promise in the computer vision (CV) field. Medical image analysis …
Improving diagnosis and prognosis of lung cancer using vision transformers: a sco** review
Background Vision transformer-based methods are advancing the field of medical artificial
intelligence and cancer imaging, including lung cancer applications. Recently, many …
intelligence and cancer imaging, including lung cancer applications. Recently, many …
Introduction of artificial Intelligence
Artificial intelligence (AI) is referred to as the intelligence developed by machines with
mathematical modeling. In particular, AI is manifested by machine's ability to effectively …
mathematical modeling. In particular, AI is manifested by machine's ability to effectively …
Design of precision medicine web-service platform towards health care digital twin
Recently, there has been a growing interest in researching and develo** personalized
medical AI services. The previous AI medical systems rarely provided model output …
medical AI services. The previous AI medical systems rarely provided model output …
Weighted Deformable Network for Efficient Segmentation of Lung Tumors in CT
The computerized delineation and prognosis of lung cancer is typically based on Computed
Tomography (CT) image analysis, whereby the region of interest (ROI) is accurately …
Tomography (CT) image analysis, whereby the region of interest (ROI) is accurately …
FedDUS: Lung tumor segmentation on CT images through federated semi-supervised with dynamic update strategy
D Wang, C Han, Z Zhang, T Zhai, H Lin, B Yang… - Computer Methods and …, 2024 - Elsevier
Abstract Background and Objective Lung tumor annotation is a key upstream task for further
diagnosis and prognosis. Although deep learning techniques have promoted automation of …
diagnosis and prognosis. Although deep learning techniques have promoted automation of …
Enhancing Lung Cancer Survival Prediction: 3D CNN Analysis of CT Images Using Novel GTV1-SliceNum Feature and PEN-BCE Loss Function
Lung cancer is a prevalent malignancy associated with a high mortality rate, with a 5-year
relative survival rate of 23%. Traditional survival analysis methods, reliant on clinician …
relative survival rate of 23%. Traditional survival analysis methods, reliant on clinician …
Survival Prediction in Lung Cancer through Multi-Modal Representation Learning
Survival prediction is a crucial task associated with cancer diagnosis and treatment
planning. This paper presents a novel approach to survival prediction by harnessing …
planning. This paper presents a novel approach to survival prediction by harnessing …
Survival Analysis in Lung Cancer: A Comparative Study of Different Approaches Using NSCLC-Radiomics (Lung1) Data
Lung cancer is known as one of the most prevalent and dangerous types of cancer, and its
survival rate is lower compared to many other types of cancer. Survival analyses are vital to …
survival rate is lower compared to many other types of cancer. Survival analyses are vital to …
Attention-Based Multimodal Bilinear Feature Fusion for Lung Cancer Survival Analysis
Survival analysis (SA) is an essential task that aims to predict survival status and duration,
determine individual and precise treatment strategies, and assess disease intensity and …
determine individual and precise treatment strategies, and assess disease intensity and …