American Institute for Radiologic Pathology (AIRP) and Hong Kong College of Radiologists (HKCR) Correlation Course

Jun 29th, 2018Jul 1st, 2018

Hong Kong, 29

HK

http://www.hkcr.org/

Phone:703-390-3991

GU, MSK, and breast imaging topics will be covered in this AIRP RadPath Correlation course.

Test set images with ground truth (teal, red) and model predictions (green, yellow), with true positives (green), false positives (yellow), and false negatives (red). Predictions from the 6x-YOLOv5 ensemble trained on histogram equalized input images with a y = 0.2 avalanche scheme, achieving 0.536 precision, 0.795 recall, and 0.723 F2 score. Image and caption courtesy of Scientific Reports.

AI proposed to help radiologists detect child abuse injuries

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Screenshot (1142)

POCUS used widely in lung ultrasound applications

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CT-based deep learning identifies patients at further lung cancer risk

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AI proposed to help radiologists detect child abuse injuries

A team at Michigan State University has developed an AI model designed to detect rib fractures in children under 3 years old.

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Test set images with ground truth (teal, red) and model predictions (green, yellow), with true positives (green), false positives (yellow), and false negatives (red). Predictions from the 6x-YOLOv5 ensemble trained on histogram equalized input images with a y = 0.2 avalanche scheme, achieving 0.536 precision, 0.795 recall, and 0.723 F2 score. Image and caption courtesy of Scientific Reports.

Tailored lung cancer screening model helps determine nodule follow-up

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A personalized lung cancer screening model used with patients with pulmonary nodules identified on low-dose CT helps clinicians determine how to follow up better than rule-based protocols, researchers have found.

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POCUS used widely in lung ultrasound applications

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Some of the top domains for users of point-of-care ultrasound (POCUS) incorporate the technology for lung ultrasound applications.

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Screenshot (1142)

CT-based deep learning identifies patients at further lung cancer risk

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A CT-based deep learning model can identify individuals with clinical stage IA non-small cell lung cancer (NSCLC) at high risk of further disease, researchers have found.

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ChatGPT-4 matches radiologists in flagging errors on reports

ChatGPT-4 matched radiologists when tasked with detecting errors in imaging reports, with the study suggesting the technology may be useful for improving reporting accuracy.

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Deep-learning model determines breast tumor staging

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A deep-learning model based on mammographic images can determine tumor staging and sentinel lymph node metastasis in breast cancer patients.

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Researchers explain the data preparation and structure of a proposed CNN-based deep learning model using mammography features to determine breast tumor staging. The image is available for use under a Creative Commons license: CC BY-NC-ND 4.0 DEED Attribution-NonCommercial-NoDerivs 4.0 International.

FAPI-PET shows promise in patients with lung cancer

FAPI-PET imaging could emerge as a new tool for assessing patients with single pulmonary tumors, especially in those with negative results on other scans.

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F-18 FDG and Ga-68 FAPI-46 images of a 51-year-old woman with adenocarcinoma with lepidic growth pattern in right upper lobe. (A) Maximum-intensity-projection PET images. The lesion had F-18 FDG uptake below blood pool level but was strongly Ga-68 FAPI-46-positive. CT-guided biopsy led to pathologic diagnosis of adenocarcinoma, and the patient was treated by stereotactic body radiation therapy because of functional inoperability. Image courtesy of the Journal of Nuclear Medicine.

IMV: Nuclear medicine procedure volumes decrease

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Total nuclear medicine patient study volumes decreased between 2021 and 2023, according to a newly published IMV 2024 Nuclear Medicine Market Outlook Report.

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Can brain MRI for emergency imaging of dizziness be more effective?

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Predictors for acute conditions such as stroke could make brain MRI more effective in patients who present in the emergency department with dizziness or vertigo, according to recent research.

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NIH: Occupational radiation dose to RTs needs careful monitoring

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Vision transformer AI model boosts PET/CT imaging of cancer

A vision transformer AI model outperformed convolutional neural networks when classifying tumors on PET/CT images as benign or malignant.

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Predictions and Grad-CAMs of ViT-based models on sample PET/CT, PET, and CT test images from the “positive” class. ViT was fine-tuned using training data for each modality. The bounding boxes depicted in the figure indicate malignant lesions. The top row of the Grad-CAMs shows important areas for “positive” predictions, and the bottom row shows areas for “negative” predictions. Image courtesy of Scientific Reports.

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Kibo Nam, PhD, from Thomas Jefferson University spoke with AuntMinnie.com about the current state of ultrasound in imaging dense breasts.

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