This repository serves as the template for the third project in the Deep Catalyst course, focusing on medical image segmentation. Explore and utilize this template to kickstart your own medical image segmentation projects, leverage best practices, and accelerate your journey into the world of precise medical diagnostics through deep learning.
In this section, you'll find a comprehensive overview of the problem being addressed in the project.
This section explores existing research and solutions related to medical image segmentation.
Here, the proposed approach for solving the problem is detailed. It covers the algorithms, techniques, or deep learning models to be applied, explaining how they address the problem and why they were chosen.
This section delves into the practical aspects of the project's implementation.
Under this subsection, you'll find information about the dataset used for the medical image segmentation task. It includes details about the dataset source, size, composition, preprocessing, and loading applied to it. Dataset
In this subsection, the architecture and specifics of the deep learning model employed for the segmentation task are presented. It describes the model's layers, components, libraries, and any modifications made to it.
This part outlines the configuration settings used for training and evaluation. It includes information on hyperparameters, optimization algorithms, loss function, metric, and any other settings that are crucial to the model's performance.
Here, you'll find instructions and code related to the training of the segmentation model. This section covers the process of training the model on the provided dataset.
In the evaluation section, the methods and metrics used to assess the model's performance are detailed. It explains how the model's segmentation results are quantified and provides insights into the model's effectiveness.