Black Belt Mammographer

Jun 10th, 2016
Victoria, TX
US
https://store.aheconline.com/index.php?route=product/product&path=59&product_id=2337
Phone:800-239-1361
This continuing education course is designed for mammographers who wish to expand their knowledge on multiple topics (nine, to be exact) in one fast-paced eight-hour day.
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Radiologist Computer Screen X Ray
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(A) Free-response receiver operating characteristic (FROC) curve for DBT-DINO and DINOv2 (Meta AI) on the detection task. DINOv2 achieved higher sensitivity than DBT-DINO across most false-positive thresholds. (B) Detection rates of DBT-DINO and DINOv2 for lesions that were ultimately found to be benign (n = 70) and lesions that were found to be cancerous (n = 66). Error bars represent 95% CIs estimated via patient-level bootstrapping with 1000 repetitions. Both models showed similar detection rates for cancerous lesions, whereas DINOv2 detected more benign lesions than DBT-DINO. (C) Example bounding box predictions for DBT-DINO and DINOv2 on the detection test set. Each image is a single two-dimensional section from a three-dimensional digital breast tomosynthesis volume; the first column shows a mediolateral oblique view, and the second and third columns show craniocaudal views. Both models struggled to detect very small lesions, such as the 3-mm-diameter lesion shown in the first column. FP = false positive.
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Ct Contrast
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Imaging turnaround times continued to climb in 2024
By Erik L. Ridley
The largest increases were found in ultrasound and x-ray.
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Radiologist Computer Screen X Ray
DBT-based foundational model classifies breast density
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DBT-DINO showed strong performance in classifying breast density from DBT images.
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(A) Free-response receiver operating characteristic (FROC) curve for DBT-DINO and DINOv2 (Meta AI) on the detection task. DINOv2 achieved higher sensitivity than DBT-DINO across most false-positive thresholds. (B) Detection rates of DBT-DINO and DINOv2 for lesions that were ultimately found to be benign (n = 70) and lesions that were found to be cancerous (n = 66). Error bars represent 95% CIs estimated via patient-level bootstrapping with 1000 repetitions. Both models showed similar detection rates for cancerous lesions, whereas DINOv2 detected more benign lesions than DBT-DINO. (C) Example bounding box predictions for DBT-DINO and DINOv2 on the detection test set. Each image is a single two-dimensional section from a three-dimensional digital breast tomosynthesis volume; the first column shows a mediolateral oblique view, and the second and third columns show craniocaudal views. Both models struggled to detect very small lesions, such as the 3-mm-diameter lesion shown in the first column. FP = false positive.
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Representative axial fused F-18 FDG-PET/MRI images and corresponding anatomic MRI images from a patient imaged at baseline and after initiation of androgen deprivation therapy are shown. Each row displays a distinct vascular territory. White arrows indicate focal areas of increased tracer uptake within the arterial wall, where spherical volumes of interest (VOIs) were placed for maximum standardized uptake value (SUVmax) measurement. Additional VOIs were placed in the superior vena cava and inferior vena cava for blood-pool activity sampling. SUVmax values from the arterial VOIs were extracted on the corresponding PET images and normalized to the blood-pool mean standardized uptake value to derive the maximum target-to-background ratio.
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