AIMC Topic: Image Interpretation, Computer-Assisted

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DIPathMamba: A domain-incremental weakly supervised state space model for pathology image segmentation.

Medical image analysis Apr 9, 2025
Accurate segmentation of pathology images plays a crucial role in digital pathology workflow. However, two significant issues exist with the present pathology image segmentation methods: (i) Most fully supervised models rely on dense pixel-level anno...
Image Processing, Computer-Assisted Supervised Machine Learning Image Interpretation, Computer-Assisted Humans Algorithms
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Unsupervised brain MRI tumour segmentation via two-stage image synthesis.

Medical image analysis Apr 5, 2025
Deep learning shows promise in automated brain tumour segmentation, but it depends on costly expert annotations. Recent advances in unsupervised learning offer an alternative by using synthetic data for training. However, the discrepancy between real...
Unsupervised Machine Learning Brain Neoplasms Image Interpretation, Computer-Assisted Humans Deep Learning Algorithms Magnetic Resonance Imaging
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FreqYOLO: A uterine disease detection network based on local and global frequency feature learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Apr 4, 2025
Leiomyomas (LM) and adenomyosis (AM) are common gynecological diseases with high incidence rates and an increasing trend of affecting younger women. Accurate detection and differentiation of LM and AM in ultrasound images are crucial for selecting ap...
Humans Ultrasonography Diagnosis, Differential Uterine Neoplasms Machine Learning Algorithms Female Adenomyosis Image Interpretation, Computer-Assisted Leiomyoma
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MedScale-Former: Self-guided multiscale transformer for medical image segmentation.

Medical image analysis Apr 4, 2025
Accurate medical image segmentation is crucial for enabling automated clinical decision procedures. However, existing supervised deep learning methods for medical image segmentation face significant challenges due to their reliance on extensive label...
Image Interpretation, Computer-Assisted Multiple Myeloma Image Processing, Computer-Assisted Supervised Machine Learning Humans Algorithms Deep Learning
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Retinal OCT image segmentation with deep learning: A review of advances, datasets, and evaluation metrics.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Apr 4, 2025
Optical coherence tomography (OCT) is a widely used imaging technology in ophthalmic clinical practice, providing non-invasive access to high-resolution retinal images. Segmentation of anatomical structures and pathological lesions in retinal OCT ima...
Humans Retina Image Interpretation, Computer-Assisted Retinal Diseases Deep Learning Image Processing, Computer-Assisted Tomography, Optical Coherence
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A semantic segmentation model for automatic precise identification of pituitary microadenomas with preoperative MRI.

Neuroradiology Apr 4, 2025
PURPOSE: Magnetic resonance imaging (MRI) is an essential technique for diagnosing pituitary adenomas; however, it is also challenging for neurosurgeons to use it to precisely identify some types of microadenomas. A novel neural network model was dev...
Middle Aged Contrast Media Magnetic Resonance Imaging Adult Male Humans Semantics Pituitary Neoplasms Neural Networks, Computer Preoperative Care Adenoma Female Image Interpretation, Computer-Assisted Aged
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Towards clinical implementation of automated segmentation of vestibular schwannomas: a reliability study comparing AI and human performance.

Neuroradiology Apr 4, 2025
PURPOSE: To evaluate the clinimetric reliability of automated vestibular schwannoma (VS) segmentations by a comparison with human inter-observer variability on T1-weighted contrast-enhanced MRI scans.
Adult Male Middle Aged Contrast Media Magnetic Resonance Imaging Tumor Burden Female Reproducibility of Results Aged Artificial Intelligence Humans Retrospective Studies Neuroma, Acoustic Image Interpretation, Computer-Assisted Observer Variation
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Multi-Scale Dynamic Sparse Token Multi-Instance Learning for Pathology Image Classification.

IEEE journal of biomedical and health informatics Apr 4, 2025
In many challenging breast cancer pathology images, the proportion of truly informative tumor regions is extremely limited. The disparity between the essential information required for clinical diagnosis (Tumor area less than 10$\%$) and the vast amo...
Algorithms Humans Breast Neoplasms Databases, Factual Machine Learning Image Interpretation, Computer-Assisted Female
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LGG-NeXt: A Next Generation CNN and Transformer Hybrid Model for the Diagnosis of Alzheimer's Disease Using 2D Structural MRI.

IEEE journal of biomedical and health informatics Apr 4, 2025
Incurable Alzheimer's disease (AD) plagues many elderly people and families. It is important to accurately diagnose and predict it at an early stage. However, the existing methods have shortcomings, such as inability to learn local and global informa...
Alzheimer Disease Image Interpretation, Computer-Assisted Brain Aged Magnetic Resonance Imaging Neural Networks, Computer Algorithms Humans
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Self-Supervised Multi-Scale Multi-Modal Graph Pool Transformer for Sellar Region Tumor Diagnosis.

IEEE journal of biomedical and health informatics Apr 4, 2025
The sellar region tumor is a brain tumor that only exists in the brain sellar, which affects the central nervous system. The early diagnosis of the sellar region tumor subtypes helps clinicians better understand the best treatment and recovery of pat...
Image Interpretation, Computer-Assisted Sella Turcica Pituitary Neoplasms Supervised Machine Learning Humans Algorithms Brain Neoplasms Magnetic Resonance Imaging
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