Preprocessing 3D Volumes for Tumor Segmentation using PyTorch and MONAI | Part 1/2
In this video, you will learn how to preprocess 3D volumes (Nifti files) for tumor segmentation tasks, as well as how to use them for organ segmentation and classification tasks. Monai is an opensource framework based on PyTorch that I used during my internship and found very useful, so I wanted to share my experience with you. 🆕 Learn how to effectively manage and process DICOM files in Python with our comprehensive course, designed to equip you with the skills and knowledge you need to succeed. https://www.learn.pycad.co/course/dic... • This is the second part, enjoy !    • Preprocessing 3D Volumes for Tumor Segment...  • Code and explanation in this link: https://bit.ly/3jAtzXV • Subscribe to my newsletter here: https://bit.ly/3FCMVEW • Here is the link to the documentation: https://docs.monai.io/en/latest/ • Create nifti volumes from group of dicom files:    • How to Convert Series of Dicom Files into ...  • Convert dicom files into JPG or PNG:    • Convert Dicom Images Into JPG or PNG with ...  • Convert JPG or PNG images into dicom files: https://pycad.co/convert-jpg-or-png-i... • Please visit my website for blogs about my work: www.pycad.co • Visit my store for the effects I use in my videos: https://pycad.gumroad.com/ • My bio links here: https://withkoji.com/@pycad Time lapes: • 00:00 : Introduction • 01:27 : Tools we are using • 05:50 : Importing the data • 27:05 : Defining the functions for the transformations • 41:00 : The dataloader #pycad #python #computervision #pytorch #monai
Preprocessing 3D Volumes for Tumor Segmentation using PyTorch and MONAI | Part 2/2
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