Overview This repository contains an implementation of an AutoEncoder with UNET architecture for detecting medical defects in scans. The combination of AutoEncoders and UNET provides a powerful solution for identifying anomalies in medical images, making it a valuable tool for healthcare professionals. Features AutoEncoder Architecture: Leverage the power of unsupervised learning with AutoEncoders to efficiently encode and decode medical images while capturing essential features.
UNET Architecture: Employ the UNET architecture to create a robust and precise segmentation model. UNET's skip connections help in capturing both global and local features, enhancing the accuracy of defect localization.
Medical Image Preprocessing: Implement specialized preprocessing techniques tailored for medical images to enhance the model's ability to identify subtle defects.
Training Pipeline: A comprehensive training pipeline with configurable hyperparameters, allowing users to adapt the model to different datasets and tasks.
