Breast Tomosynthesis Workshop

Jun 15th, 2018

Melbourne, 07

AU

https://lightboxradiology.com/course-details.aspx?c=1105

Phone:+61754492717

Provides an intensive, hands-on experience of tomosynthesis. Throughout the program, participants will develop their interpretive skills through extensive case reviews (50 cases) on dedicated workstations. Sponsored by Hologic.

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.

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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.

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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.

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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.

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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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