AIMC Topic: Magnetic Resonance Imaging

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Neuroimaging and biological markers of different paretic hand outcomes after stroke.

Journal of neuroengineering and rehabilitation
BACKGROUND: Hand dysfunction significantly affects independence after stroke, with outcomes varying across individuals. Exploring biomarkers associated with the paretic hand can improve the prognosis and guide personalized rehabilitation. However, wh...

Deep learning-based classification of parotid gland tumors: integrating dynamic contrast-enhanced MRI for enhanced diagnostic accuracy.

BMC medical imaging
BACKGROUND: To evaluate the performance of deep learning models in classifying parotid gland tumors using T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted MR images, along with DCE data derived from time-intensity curves.

Machine learning-based prognostic prediction for acute ischemic stroke using whole-brain and infarct multi-PLD ASL radiomics.

BMC medical imaging
INTRODUCTION: Accurate early prognostic prediction for acute ischemic stroke (AIS) is essential for guiding personalized treatment. This study aimed to assess the predictive value of radiomics features from whole-brain and infarct cerebral blood flow...

Intralesional and perilesional radiomics strategy based on different machine learning for the prediction of international society of urological pathology grade group in prostate cancer.

BMC medical imaging
OBJECTIVE: To develop and evaluate a intralesional and perilesional radiomics strategy based on different machine learning model to differentiate International Society of Urological Pathology (ISUP) grade > 2 group and ISUP ≤ 2 prostate cancers (PCa)...

Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography.

BMC medical imaging
OBJECTIVE: To develop a multi-modality machine learning-based radiomics model utilizing Magnetic Resonance Imaging (MRI), Ultrasound (US), and Mammography (MMG) for the differentiation of benign and malignant breast nodules.

Medical slice transformer for improved diagnosis and explainability on 3D medical images with DINOv2.

Scientific reports
Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are essential clinical cross-sectional imaging techniques for diagnosing complex conditions. However, large 3D datasets with annotations for deep learning are scarce. While methods like DI...

Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms.

Scientific reports
Brain tumours (BTs) are severe neurological disorders. They affect more than 308,000 people each year worldwide. The mortality rate is over 251,000 deaths annually (IARC, 2020 reports). Detecting BTs is complex because they vary in nature. Early diag...

A novel hybrid vision UNet architecture for brain tumor segmentation and classification.

Scientific reports
This paper focuses on designing and developing novel architectures termed Hybrid Vision UNet-Encoder Decoder (HVU-ED) segmenter and Hybrid Vision UNet-Encoder (HVU-E) classifier for brain tumor segmentation and classification, respectively. The propo...

Influence of cognitive networks and task performance on fMRI-based state classification using DNN models.

Scientific reports
Deep neural networks (DNNs) excel at extracting insights from complex data across various fields, however, their application in cognitive neuroscience remains limited, largely due to the lack of approaches with interpretability. Here, we employ two d...

Universal dimensions of visual representation.

Science advances
Do visual neural networks learn brain-aligned representations because they share architectural constraints and task objectives with biological vision or because they share universal features of natural image processing? We characterized the universal...