AIMC Topic: Image Interpretation, Computer-Assisted
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Multi channel fusion diffusion models for brain tumor MRI data augmentation.
Scientific reports
Jul 2, 2025
The early diagnosis of brain tumors is crucial for patient prognosis, and medical imaging techniques such as MRI and CT scans are essential tools for diagnosing brain tumors. However, high-quality medical image data for brain tumors is often scarce a...
Hybrid model integration with explainable AI for brain tumor diagnosis: a unified approach to MRI analysis and prediction.
Scientific reports
Jul 1, 2025
Effective treatment for brain tumors relies on accurate detection because this is a crucial health condition. Medical imaging plays a pivotal role in improving tumor detection and diagnosis in the early stage. This study presents two approaches to th...
Transformer attention fusion for fine grained medical image classification.
Scientific reports
Jul 1, 2025
Fine-grained visual classification is fundamental for medical image applications because it detects minor lesions. Diabetic retinopathy (DR) is a preventable cause of blindness, which requires exact and timely diagnosis to prevent vision damage. The ...
Deep learning quantifies pathologists' visual patterns for whole slide image diagnosis.
Nature communications
Jul 1, 2025
Based on the expertise of pathologists, the pixelwise manual annotation has provided substantial support for training deep learning models of whole slide images (WSI)-assisted diagnostic. However, the collection of pixelwise annotation demands massiv...
Hybrid transfer learning and self-attention framework for robust MRI-based brain tumor classification.
Scientific reports
Jul 1, 2025
Brain tumors are a significant contributor to cancer-related deaths worldwide. Accurate and prompt detection is crucial to reduce mortality rates and improve patient survival prospects. Magnetic Resonance Imaging (MRI) is crucial for diagnosis, but m...
Generative AI for weakly supervised segmentation and downstream classification of brain tumors on MR images.
Scientific reports
Jul 1, 2025
Segmenting abnormalities is a leading problem in medical imaging. Using machine learning for segmentation generally requires manually annotated segmentations, demanding extensive time and resources from radiologists. We propose a weakly supervised ap...
Automatic melanoma detection using an optimized five-stream convolutional neural network.
Scientific reports
Jul 1, 2025
Melanoma is among the deadliest forms of malignant skin cancer, with the number of cases increasing dramatically worldwide. Its early and accurate diagnosis is crucial for effective treatment. However, automatic melanoma detection has several signifi...
Deep learning detects retropharyngeal edema on MRI in patients with acute neck infections.
European radiology experimental
Jun 19, 2025
BACKGROUND: In acute neck infections, magnetic resonance imaging (MRI) shows retropharyngeal edema (RPE), which is a prognostic imaging biomarker for a severe course of illness. This study aimed to develop a deep learning-based algorithm for the auto...
Exploratory multi-cohort, multi-reader study on the clinical utility of a deep learning model for transforming cryosectioned to formalin-fixed, paraffin-embedded (FFPE) images in breast lesion diagnosis.
Breast cancer research : BCR
Jun 17, 2025
BACKGROUND: Cryosectioned tissues often exhibit artifacts that compromise pathologists' diagnostic accuracy during intraoperative assessments. These inconsistencies, compounded by variations in frozen section (FS) production across laboratories, high...
Whole-lesion-aware network based on freehand ultrasound video for breast cancer assessment: a prospective multicenter study.
Cancer imaging : the official publication of the International Cancer Imaging Society
Jun 16, 2025
BACKGROUND: The clinical application of artificial intelligence (AI) models based on breast ultrasound static images has been hindered in real-world workflows due to operator-dependence of standardized image acquisition and incomplete view of breast ...
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