UNET 3+ Implementation in TensorFlow | UNET 3+ with Deep Supervision & Classification Guided Module
📺 In this video, we're going to learn how to implement UNet3+, an architecture for medical image segmentation.
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
00:15 - What is UNET 3+?
02:36 - Importing libraries
02:49 - Convolution block
03:37 - Encoder block
04:34 - Defining UNET 3+ function
04:46 - Input layer
05:13 - Encoder network
07:59 - Bottleneck layer
08:41 - Decoder 4 implementation
14:52 - Decoder 3 implementation
18:06 - Decoder 2 implementation
20:13 - Decoder 1 implementation
23:02 - UNET 3+ with Deep Supervision
26:54 - UNET 3+ with Deep Supervision and Classification-Guided Module
35:06 - Ending
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