AIMC Topic: Image Interpretation, Computer-Assisted
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Development and Validation of a Deep Learning System to Differentiate HER2-Zero, HER2-Low, and HER2-Positive Breast Cancer Based on Dynamic Contrast-Enhanced MRI.
Journal of magnetic resonance imaging : JMRI
Dec 6, 2024
BACKGROUND: Previous studies explored MRI-based radiomic features for differentiating between human epidermal growth factor receptor 2 (HER2)-zero, HER2-low, and HER2-positive breast cancer, but deep learning's effectiveness is uncertain.
Automated Classification of Body MRI Sequences Using Convolutional Neural Networks.
Academic radiology
Dec 6, 2024
RATIONALE AND OBJECTIVES: Multi-parametric MRI (mpMRI) studies of the body are routinely acquired in clinical practice. However, a standardized naming convention for MRI protocols and series does not exist currently. Conflicts in the series descripti...
Current status and future directions of explainable artificial intelligence in medical imaging.
European journal of radiology
Dec 6, 2024
The inherent "black box" nature of AI algorithms presents a substantial barrier to the widespread adoption of the technology in clinical settings, leading to a lack of trust among users. This review begins by examining the foundational stages involve...
Adaptive Annotation Correlation Based Multi-Annotation Learning for Calibrated Medical Image Segmentation.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Medical image segmentation is a fundamental task in many clinical applications, yet current automated segmentation methods rely heavily on manual annotations, which are inherently subjective and prone to annotation bias. Recently, modeling annotator ...
DualStreamFoveaNet: A Dual Stream Fusion Architecture With Anatomical Awareness for Robust Fovea Localization.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Accurate fovea localization is essential for analyzing retinal diseases to prevent irreversible vision loss. While current deep learning-based methods outperform traditional ones, they still face challenges such as the lack of local anatomical landma...
Weakly Supervised Classification for Nasopharyngeal Carcinoma With Transformer in Whole Slide Images.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Pathological examination of nasopharyngeal carcinoma (NPC) is an indispensable factor for diagnosis, guiding clinical treatment and judging prognosis. Traditional and fully supervised NPC diagnosis algorithms require manual delineation of regions of ...
tDKI-Net: A Joint q-t Space Learning Network for Diffusion-Time-Dependent Kurtosis Imaging.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Time-dependent diffusion magnetic resonance imaging (TDDMRI) is useful for the non-invasive characterization of tissue microstructure. These models require densely sampled q-t space data for microstructural fitting, leading to very time-consuming acq...
Development and Validation of Multiparametric MRI-based Interpretable Deep Learning Radiomics Fusion Model for Predicting Lymph Node Metastasis and Prognosis in Rectal Cancer: A Two-center Study.
Academic radiology
Dec 4, 2024
RATIONALE AND OBJECTIVES: To develop interpretable machine learning models that utilize deep learning (DL) and radiomics based on multiparametric Magnetic resonance imaging (MRI) to predict preoperative lymph node (LN) metastasis in rectal cancer.
Leveraging a Vision Transformer Model to Improve Diagnostic Accuracy of Cardiac Amyloidosis With Cardiac Magnetic Resonance.
JACC. Cardiovascular imaging
Dec 4, 2024
BACKGROUND: Cardiac magnetic resonance (CMR) imaging is an important diagnostic tool for diagnosis of cardiac amyloidosis (CA). However, discrimination of CA from other etiologies of myocardial disease can be challenging.
Humans
Decision Support Techniques
Retrospective Studies
Female
Reproducibility of Results
Predictive Value of Tests
Myocardium
Image Interpretation, Computer-Assisted
Cardiomyopathies
Magnetic Resonance Imaging, Cine
Cardiomyopathy, Hypertrophic
Amyloidosis
Deep Learning
Contrast Media
Diagnosis, Differential
Aged
Male
Middle Aged
UDA-GS: A cross-center multimodal unsupervised domain adaptation framework for Glioma segmentation.
Computers in biology and medicine
Dec 4, 2024
Gliomas are the most common and malignant form of primary brain tumors. Accurate segmentation and measurement from MRI are crucial for diagnosis and treatment. Due to the infiltrative growth pattern of gliomas, their labeling is very difficult. In tu...
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