AIMC Topic: Image Processing, Computer-Assisted

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S-Net: A novel shallow network for enhanced detail retention in medical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In recent years, deep U-shaped network architectures have been widely applied to medical image segmentation tasks, achieving notable successes. However, the inherent limitation of this architecture is that multiple down-samp...

Advancing hierarchical neural networks with scale-aware pyramidal feature learning for medical image dense prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Hierarchical neural networks are pivotal in medical imaging for multi-scale representation, aiding in tasks such as object detection and segmentation. However, their effectiveness is often limited by the loss of intra-scale ...

Scatter and beam hardening effect corrections in pelvic region cone beam CT images using a convolutional neural network.

Radiological physics and technology
The aim of this study is to remove scattered photons and beam hardening effect in cone beam CT (CBCT) images and make an image available for treatment planning. To remove scattered photons and beam hardening effect, a convolutional neural network (CN...

Weakly supervised nuclei segmentation based on pseudo label correction and uncertainty denoising.

Artificial intelligence in medicine
Nuclei segmentation plays a vital role in computer-aided histopathology image analysis. Numerous fully supervised learning approaches exhibit amazing performance relying on pathological image with precisely annotations. Whereas, it is difficult and t...

IdenBAT: Disentangled representation learning for identity-preserved brain age transformation.

Artificial intelligence in medicine
Brain age transformation aims to convert reference brain images into synthesized images that accurately reflect the age-specific features of a target age group. The primary objective of this task is to modify only the age-related attributes of the re...

Combating Medical Label Noise through more precise partition-correction and progressive hard-enhanced learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Computer-aided diagnosis systems based on deep neural networks heavily rely on datasets with high-quality labels. However, manual annotation for lesion diagnosis relies on image features, often requiring professional experie...

Breaking through scattering: The H-Net CNN model for image retrieval.

Computer methods and programs in biomedicine
BACKGROUND: In scattering media, traditional optical imaging techniques often find it significantly challenging to accurately reconstruct images owing to rapid light scattering. Thus, to address this problem, we propose a convolutional neural network...

A Trusted Medical Image Zero-Watermarking Scheme Based on DCNN and Hyperchaotic System.

IEEE journal of biomedical and health informatics
The zero-watermarking methods provide a means of lossless, which was adopted to protect medical image copyright requiring high integrity. However, most existing studies have only focused on robustness and there has been little discussion about the an...

MedKAFormer: When Kolmogorov-Arnold Theorem Meets Vision Transformer for Medical Image Representation.

IEEE journal of biomedical and health informatics
Vision Transformers (ViTs) suffer from high parameter complexity because they rely on Multi-layer Perceptrons (MLPs) for nonlinear representation. This issue is particularly challenging in medical image analysis, where labeled data is limited, leadin...

Ultra-Sparse-View Cone-Beam CT Reconstruction-Based Strictly Structure-Preserved Deep Neural Network in Image-Guided Radiation Therapy.

IEEE transactions on medical imaging
Radiation therapy is regarded as the mainstay treatment for cancer in clinic. Kilovoltage cone-beam CT (CBCT) images have been acquired for most treatment sites as the clinical routine for image-guided radiation therapy (IGRT). However, repeated CBCT...