Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions
Motivation Medical image analysis involves a series of tasks used to assist physicians in
qualitative and quantitative analyses of lesions or anatomical structures which can …
qualitative and quantitative analyses of lesions or anatomical structures which can …
2D medical image synthesis using transformer-based denoising diffusion probabilistic model
Objective. Artificial intelligence (AI) methods have gained popularity in medical imaging
research. The size and scope of the training image datasets needed for successful AI model …
research. The size and scope of the training image datasets needed for successful AI model …
A recent survey of vision transformers for medical image segmentation
Medical image segmentation plays a crucial role in various healthcare applications,
enabling accurate diagnosis, treatment planning, and disease monitoring. Traditionally …
enabling accurate diagnosis, treatment planning, and disease monitoring. Traditionally …
Polyp-sam: Transfer sam for polyp segmentation
Automatic segmentation of colon polyps can significantly reduce the misdiagnosis of colon
cancer and improve physician annotation efficiency. While many methods have been …
cancer and improve physician annotation efficiency. While many methods have been …
Abdomen CT multi‐organ segmentation using token‐based MLP‐Mixer
Background Manual contouring is very labor‐intensive, time‐consuming, and subject to intra‐
and inter‐observer variability. An automated deep learning approach to fast and accurate …
and inter‐observer variability. An automated deep learning approach to fast and accurate …
Breastsam: A study of segment anything model for breast tumor detection in ultrasound images
Breast cancer is one of the most common cancers among women worldwide, with early
detection significantly increasing survival rates. Ultrasound imaging is a critical diagnostic …
detection significantly increasing survival rates. Ultrasound imaging is a critical diagnostic …
Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation
X Liu, L Qu, Z Xie, J Zhao, Y Shi, Z Song - arXiv preprint arXiv:2303.00232, 2023 - arxiv.org
Accurate segmentation of multiple organs of the head, neck, chest, and abdomen from
medical images is an essential step in computer-aided diagnosis, surgical navigation, and …
medical images is an essential step in computer-aided diagnosis, surgical navigation, and …
An optimized framework for cone‐beam computed tomography‐based online evaluation for proton therapy
CW Chang, R Nilsson, S Andersson… - Medical …, 2023 - Wiley Online Library
Background Clinical evidence has demonstrated that proton therapy can achieve
comparable tumor control probabilities compared to conventional photon therapy but with …
comparable tumor control probabilities compared to conventional photon therapy but with …
MDDU-Net: A multi-scale dense connectivity hybrid dilated convolutional U-Net for segmentation in prostate ultrasound images
L Wang, Y Cui, Y Zhang, C Guo - Expert Systems with Applications, 2025 - Elsevier
Ultrasound is one of the most commonly used imaging tools in prostate biopsy and
brachytherapy. However, due to issues such as blurred image boundaries, similar intensity …
brachytherapy. However, due to issues such as blurred image boundaries, similar intensity …
End-to-end brain tumor detection using a graph-feature-based classifier
Brain tumors are caused by abnormal cell growth and can cause pain and reduced survival
rates. The early detection of brain tumors is pivotal in improving outcomes. Recently …
rates. The early detection of brain tumors is pivotal in improving outcomes. Recently …