Medical Image Analysis In Python
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Medical Image Analysis In Python

About the Book

What awaits you in this book?

You will be introduced to medical imaging research. Understand the DICOM and NIfTI-1 Data Format. Learn to analyze meta tags, convert and anonymize data.

Learn what Windowing and Hounsfield units are and how to apply them.

Understand the task of image segmentation. Take a look at third party libraries for medical image segmentation.

Get to know fastai.medical.imaging.

The course is accompanied by several test tasks and a chat in the telegram for discussion.

What exactly will not be in this book?

This course will not include Deep Learning. We will be focusing specifically on the medical imaging format.

About the Author

Alimbekov Renat
Alimbekov Renat

Hello everyone, my name is Renat Alimbekov. I work as a Big Data Analyst at Beeline Kazakhstan.

Previously I worked as a Deep Learning Scientist at an AI medical startup. I have done research and work with medical data and images. In addition, I work as a mentor at Yandex.Practicum. I help students to master the Data Scientist profession.

I have my own blog and telegram channel where I write guides and practical advice on Data Science and Machine Learning.

I take part in competitions on the kaggle site (profile). I am a repeated high ranking competitor, including in competitions with medical data: SIIM-ACR Pneumothorax Segmentation, RSNA Intracranial Hemorrhage Detection, Prostate cANcer graDe Assessment (PANDA) Challenge, RANZCR CLiP - Catheter and Line Position Challenge

I also rank 17th and 95th in the CheXpert Stanford ML Group competition.

You can support me to create new courses and other content on Patreon.

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