Chief Science Officer at DataPrime, Inc.
Senior Data Engineer, Publicis Sapient
University of Economics and Technology, Instructor
Big Data & Analytics architect, Amazon
In this deep learning project, you will learn to implement Unet++ models for medical image segmentation to detect and classify colorectal polyps.
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Business Objective
Machine learning and deep learning technologies are increasing at a fast pace with respect to the domain of healthcare and medical sciences. These technologies sometimes even out perform medical doctors by producing results that might not be easily notable to a human eye. Polyp recognition and segmentation is one such technology which helps doctors identify polyps from colonoscopic images.
Data Overview
CVC-Clinic database consists of frames extracted from colonoscopy videos. The dataset contains several examples of polyp frames & corresponding ground truth for them.
The Ground Truth image consists of a mask corresponding to the region covered by the polyp in the image. The data is available in both .png and .tiff formats
Aim
To segment the polyps from colonoscopy images
Tech Stack
Language used : Python
Deep learning library used : Pytorch
Computer vision library used : OpenCV
Other python libraries : Scikit-learn, pandas, numpy, albumentations etc.
Approach
Understanding the essence of the dataset
Understanding the metrics that are going to be used for evaluating the predictions
Understanding Unet architecture and why is it preferred widely in building deep learning models with respect to medical sciences.
Understanding Unet++ and how is it different from Unet
Setting up a working environment for the project
Creating new data by making modifications on the existing data
Building Unet ++ model using pytorch
Training the model. ( A GPU might be required since model training takes a really long time in CPUs)
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