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Can LLMs help improve oncologic imaging interpretation?

AuntMinnie

Radiologists highly prefer patient clinical histories generated by large language models (LLMs) for oncologic imaging requisitions to those typically produced by referring physicians,according to research published February 4 in Radiology.

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AI proposed to help radiologists detect child abuse injuries

AuntMinnie

Detecting rib fractures in pediatric x-rays is challenging, as they can be obliquely oriented to the imaging detector or obscured by other structures, the authors explained. Test set images with ground truth (teal, red) and model predictions (green, yellow), with true positives (green), false positives (yellow), and false negatives (red).

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AI boosts radiologist perception training

AuntMinnie

They provided a step-by-step description of how to use it in an article published October 24 in the British Journal of Radiology. For the novice, beginning interpretation of cross-sectional imaging can be a daunting task,” he noted. In total, the data sets included 513 normal images and 154 with small lung nodules.

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Ultralearning Radiology

Ben White

Depending on your imaging protocols, perhaps that means you will open up the accompanying CT of the cervical spine, because the thinner slices on that exam may make a fracture more apparent even though it’s the brain that I’m reading right now. You can then individually save generated flashcards into an Anki deck.

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JNM Publishes Procedure Standard/practice Guideline for FES PET Imaging of Breast Cancer

Imaging Technology

milla1cf Tue, 01/16/2024 - 14:37 January 16, 2024 — The Society of Nuclear Medicine and Molecular Imaging ( SNMMI ) and the European Association of Nuclear Medicine (EANM) have issued a new procedure standard/practice guideline for estrogen receptor imaging of breast cancer patients using FES PET.

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Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging

European Society of Radiology: AI

This study evaluates deep learning (DL) algorithms that are playing an increasingly important role in automatic medical image analysis. Image preprocessing, such as windowing, plays a large role in improving deep learning model performance. Visual saliency maps can facilitate explainable artificial intelligence systems.

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[Podcast] – Shaping Results through Imaging: Addressing Variability in Oncology Trials 

Median

Project Manager II at Median Technologies, the discussion was about how variability in imaging interpretation can lead to discrepancies in oncology clinical trial results. This is an excerpt from our podcast “Shaping Results through Imaging: Addressing Variability in Oncology Trials”. Click here!

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