Online Practical Deep Learning for Radiologists

Sep 3rd, 2023Sep 5th, 2023
https://academy.telemedicineclinic.com/fellowships/3591/online-practical-deep-learning-for-radiologists-fellowship-sep-2023/
In this AI radiology fellowship, you will acquire knowledge about how a convolutional neural network works, being able to experiment in real-time with the main parameters that influence its training.
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Example case from test set with biparametric MRI data, reference segmentation, and output from AI system. The primary MRI assessment resulted in a PI-RADS 5 lesion in the left dorsal part of the peripheral zone and a PI-RADS 4 lesion in the right dorsal part of the peripheral zone (PSA of 11.6 μg/L). Targeted biopsies from both areas showed ISUP-grade 3. All images are from the same slice location, showing a part of both lesions. A T2-weighted (T2W) image. B Diffusion-weighted image with a b-value of 1,500 s/mm2. (C) Apparent diffusion coefficient map. (D) Reference segmentation with white areas representing tumor. (E) Softmax output from AI system. Inset in the lower left corner shows the detection map (cropped around the prostate) with red voxels representing softmax values of 0.99 and yellow voxels representing softmax values of 0.59. (F) T2W image with reference (D) and detection map (inset in E) overlayed, green represents areas in which both the reference and the AI output are positive (true positive voxels for AI), red only positive in reference (false-negative voxels for AI) and blue only segmented by AI (false-positive voxels for AI).
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