AIMC Topic: Image Processing, Computer-Assisted

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Denoising of high-resolution 3D UTE-MR angiogram data using lightweight and efficient convolutional neural networks.

Magnetic resonance imaging
High-resolution magnetic resonance angiography (∼ 50 μm MRA) data plays a critical role in the accurate diagnosis of various vascular disorders. However, it is very challenging to acquire, and it is susceptible to artifacts and noise which limits its...

Harnessing deep learning for wheat variety classification: a convolutional neural network and transfer learning approach.

Journal of the science of food and agriculture
BACKGROUND: Computer vision and the use of image-based solutions are gaining traction as non-destructive food assessment methods because of the low costs of computational equipment. Research conducted on the development of wheat classification models...

Modeling multi-scale uncertainty with evidence integration for reliable polyp segmentation.

Neural networks : the official journal of the International Neural Network Society
Polyp segmentation is critical in medical image analysis. Traditional methods, while capable of producing precise outputs in well-defined regions, often struggle with blurry or ambiguous areas in medical images, which can lead to errors in clinical d...

Generalizable AI approach for detecting projection type and left-right reversal in chest X-rays.

Radiological physics and technology
The verification of chest X-ray images involves several checkpoints, including orientation and reversal. To address the challenges of manual verification, this study developed an artificial intelligence (AI)-based system using a deep convolutional ne...

S2LIC: Learned image compression with the SwinV2 block, Adaptive Channel-wise and Global-inter attention Context.

Neural networks : the official journal of the International Neural Network Society
Recently, deep learning technology has been successfully applied in the field of image compression, leading to superior rate-distortion performance. It is crucial to design an effective and efficient entropy model to estimate the probability distribu...

Memory Transmission Based Referring Video Object Segmentation.

Neural networks : the official journal of the International Neural Network Society
Referring Video Object Segmentation (RVOS) addresses the task of segmenting target objects described by textual descriptions from videos. In order to ensure the consistency of objects segmented from video frames, inter-frame modeling is adopted to ca...

Federated Learning for Renal Tumor Segmentation and Classification on Multi-Center MRI Dataset.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning (DL) models for accurate renal tumor characterization may benefit from multi-center datasets for improved generalizability; however, data-sharing constraints necessitate privacy-preserving solutions like federated learning (...

Escarcitys: A framework for enhancing medical image classification performance in scarcity of trainable samples scenarios.

Neural networks : the official journal of the International Neural Network Society
In the field of healthcare, the acquisition and annotation of medical images present significant challenges, resulting in a scarcity of trainable samples. This data limitation hinders the performance of deep learning models, creating bottlenecks in c...

SPAST: Arbitrary style transfer with style priors via pre-trained large-scale model.

Neural networks : the official journal of the International Neural Network Society
Given an arbitrary content and style image, arbitrary style transfer aims to render a new stylized image which preserves the content image's structure and possesses the style image's style. Existing arbitrary style transfer methods are based on eithe...