AIMC Topic: Diagnostic Imaging

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Development and External Validation of an Artificial Intelligence Model for Identifying Radiology Reports Containing Recommendations for Additional Imaging.

AJR. American journal of roentgenology
Reported rates of recommendations for additional imaging (RAIs) in radiology reports are low. Bidirectional encoder representations from transformers (BERT), a deep learning model pretrained to understand language context and ambiguity, has potentia...

Morphological feature recognition of different differentiation stages of induced ADSCs based on deep learning.

Computers in biology and medicine
In order to accurately identify the morphological features of different differentiation stages of induced Adipose Derived Stem Cells (ADSCs) and judge the differentiation types of induced ADSCs, a morphological feature recognition method of different...

Foundation models for generalist medical artificial intelligence.

Nature
The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities in medicine. We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI)....

PocketNet: A Smaller Neural Network for Medical Image Analysis.

IEEE transactions on medical imaging
Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models. To address such issues, we propose the PocketNet paradigm to reduce the size of deep learning models by throttling th...

Deep learning-enabled segmentation of ambiguous bioimages with deepflash2.

Nature communications
Bioimages frequently exhibit low signal-to-noise ratios due to experimental conditions, specimen characteristics, and imaging trade-offs. Reliable segmentation of such ambiguous images is difficult and laborious. Here we introduce deepflash2, a deep ...

A primer on artificial intelligence in pancreatic imaging.

Diagnostic and interventional imaging
Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data contained in medical images. Deep learning and radiomics are the two main AI methods currently being applied within radiology. Deep learning uses a layered s...

Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: application to surgical imaging.

International journal of computer assisted radiology and surgery
PURPOSE: Hyperspectral imaging has the potential to improve intraoperative decision making if tissue characterisation is performed in real-time and with high-resolution. Hyperspectral snapshot mosaic sensors offer a promising approach due to their fa...