AIMC Topic: Image Interpretation, Computer-Assisted

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Human-in-the-loop machine learning-based quantitative assessment of hemifacial spasm based on volumetric interpolated breath-hold examination MR.

The British journal of radiology Apr 1, 2025
OBJECTIVES: The purpose of this study was to assess the severity of hemifacial spasm (HFS) through quantitative measures that associated it with neurovascular contact (NVC).
Machine Learning Female Aged Adult Humans Breath Holding Hemifacial Spasm Imaging, Three-Dimensional Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Male Middle Aged Severity of Illness Index
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MRI-based Deep Learning Algorithm for Assisting Clinically Significant Prostate Cancer Detection: A Bicenter Prospective Study.

Radiology Mar 1, 2025
Background Although artificial intelligence is actively being developed for prostate MRI, few studies have prospectively validated these tools. Purpose To compare the diagnostic performance of a commercial deep learning algorithm (DLA) and radiologis...
Image-Guided Biopsy Prostatic Neoplasms Prostate Image Interpretation, Computer-Assisted Middle Aged Deep Learning Magnetic Resonance Imaging Humans Algorithms Sensitivity and Specificity Male Prospective Studies Aged
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Physics-Informed Autoencoder for Prostate Tissue Microstructure Profiling with Hybrid Multidimensional MRI.

Radiology. Artificial intelligence Mar 1, 2025
Purpose To evaluate the performance of Physics-Informed Autoencoder (PIA), a self-supervised deep learning model, in measuring tissue-based biomarkers for prostate cancer (PCa) using hybrid multidimensional MRI. Materials and Methods This retrospecti...
Prostatic Neoplasms Prostate Image Interpretation, Computer-Assisted Middle Aged Deep Learning Magnetic Resonance Imaging Signal-To-Noise Ratio Autoencoder Humans Neural Networks, Computer Algorithms Retrospective Studies Male
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Uncertainty CNNs: A path to enhanced medical image classification performance.

Mathematical biosciences and engineering : MBE Feb 20, 2025
The automated detection of tumors using medical imaging data has garnered significant attention over the past decade due to the critical need for early and accurate diagnoses. This interest is fueled by advancements in computationally efficient model...
Heart Failure Humans Image Interpretation, Computer-Assisted Uncertainty Neural Networks, Computer Image Processing, Computer-Assisted Algorithms Magnetic Resonance Imaging Lung Neoplasms Tomography, X-Ray Computed Diagnostic Imaging Neoplasms Deep Learning
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Extended Technical and Clinical Validation of Deep Learning-Based Brainstem Segmentation for Application in Neurodegenerative Diseases.

Human brain mapping Feb 15, 2025
Disorders of the central nervous system, including neurodegenerative diseases, frequently affect the brainstem and can present with focal atrophy. This study aimed to (1) optimize deep learning-based brainstem segmentation for a wide range of patholo...
Aged Cerebral Small Vessel Diseases Adult Humans Male Neuroimaging Female Brain Stem Middle Aged Reproducibility of Results Multiple System Atrophy Deep Learning Neurodegenerative Diseases Magnetic Resonance Imaging Multiple Sclerosis Image Interpretation, Computer-Assisted
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Thinking like a pathologist: Morphologic approach to hepatobiliary tumors by ChatGPT.

American journal of clinical pathology Jan 28, 2025
OBJECTIVES: This research aimed to evaluate the effectiveness of ChatGPT in accurately diagnosing hepatobiliary tumors using histopathologic images.
Image Interpretation, Computer-Assisted Humans Bile Duct Neoplasms Artificial Intelligence Carcinoma, Hepatocellular Liver Neoplasms Cholangiocarcinoma Generative Artificial Intelligence Pathologists
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DEFIF-Net: A lightweight dual-encoding feature interaction fusion network for medical image segmentation.

PloS one Jan 1, 2025
Medical image segmentation plays a crucial role in computer-aided diagnosis. By segmenting pathological tissues in medical images, doctors can observe anatomical structures more clearly, thereby achieving more accurate disease diagnoses. However, exi...
Neural Networks, Computer Algorithms Image Processing, Computer-Assisted Image Interpretation, Computer-Assisted Humans
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Breast cancer pathology image recognition based on convolutional neural network.

PloS one Jan 1, 2025
This study presents a convolutional neural network (CNN)-based method for the classification and recognition of breast cancer pathology images. It aims to solve the problems existing in traditional pathological tissue analysis methods, such as time-c...
Female Breast Neoplasms Convolutional Neural Networks Humans Neural Networks, Computer Image Processing, Computer-Assisted Algorithms Image Interpretation, Computer-Assisted
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OS-DETR: End-to-end brain tumor detection framework based on orthogonal channel shuffle networks.

PloS one Jan 1, 2025
OrthoNets use the Gram-Schmidt process to achieve orthogonality among filters but do not impose constraints on the internal orthogonality of individual filters. To reduce the risk of overfitting, especially in scenarios with limited data such as medi...
Humans Brain Neoplasms Image Interpretation, Computer-Assisted Neural Networks, Computer Algorithms
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Attention-aware Deep Learning Models for Dermoscopic Image Classification for Skin Disease Diagnosis.

Current medical imaging Jan 1, 2025
BACKGROUND: The skin, being the largest organ in the human body, plays a vital protective role. Skin lesions are changes in the appearance of the skin, such as bumps, sores, lumps, patches, and discoloration. If not identified and treated promptly, s...
Deep Learning Humans Skin Diseases Neural Networks, Computer Image Interpretation, Computer-Assisted Dermoscopy
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