UNET 3+ Implementation in PyTorch | UNET 3+ with Deep Supervision & Classification Guided Module
📺 In this video, we're going to learn how to implement UNet3+ using the PyTorch framework.
What is UNET 3+:
UNet 3+ is an advanced medical image segmentation architecture, building upon UNet++. It features full-scale skip connections, deep supervision, and a classification-guided module for improved accuracy and reduced parameters. The model efficiently captures fine-grained details and coarse-grained semantics in medical images, addressing issues like over-segmentation and false positives.
🔧 Code: https://github.com/nikhilroxtomar/UNE...
🕒 Timeline:
00:00 - Introduction
00:11 - What is UNET 3+?
02:46 - Importing libraries
02:51 - Convolution block
04:15 - Encoder block
05:11 - Defining UNET 3+ Model
05:36 - Encoder network
10:57 - Decoder 4 implementation
16:46 - Decoder 3 implementation
19:32 - Decoder 2 implementation
21:16 - Decoder 1 implementation
22:31 - UNET 3+ Complete Implementation
24:14 - UNET 3+ with Deep Supervision
28:55 - UNET 3+ with Deep Supervision and Classification-Guided Module
37:15 - Ending
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