A Simplified Approach to Deep Learning for Image Segmentation

TL;DR

This paper presents a simplified, practical approach to training deep neural networks for image segmentation, focusing on data management and augmentation techniques suitable for limited or homogeneous datasets.

Contribution

It introduces a streamlined methodology for training deep learning models in image segmentation, emphasizing data handling and augmentation strategies.

Findings

Effective training techniques for pixel-wise classification

Data augmentation improves model performance on limited datasets

Guidelines for managing insufficiently annotated image data

Abstract

Leaping into the rapidly developing world of deep learning is an exciting and sometimes confusing adventure. All of the advice and tutorials available can be hard to organize and work through, especially when training specific models on specific datasets, different from those originally used to train the network. In this short guide, we aim to walk the reader through the techniques that we have used to successfully train two deep neural networks for pixel-wise classification, including some data management and augmentation approaches for working with image data that may be insufficiently annotated or relatively homogenous.