Strategic Objectives
• Master the mechanics of 2D and 3D spatial filtering tailored for anatomy.
• Optimize depth and receptive fields for high-resolution medical scans.
• Understand the engineering behind feature extraction in volumetric data.
• Build robust architectures that minimize noise while maximizing sensitivity.
The Core Challenge
General-purpose neural networks often fail to capture the nuanced spatial hierarchies required for critical clinical diagnosis.
01
Foundations of Convolution
02
The Architecture of Vision
03
Kernels and Filter Design
04
Linearity and Beyond
05
Spatial Downsampling
06
The U-Net Revolution
07
Residual Connections
08
Volumetric Processing
09
Voxel-Based Architectures
10
Dilated Convolutions
11
Batch Normalization
12
Attention Mechanisms
13
Inception Modules
14
Transfer Learning in Radiology
15
Separable Convolutions
16
Generative Layers
17
Feature Maps and Visualization
18
Hyperparameter Engineering
19
Loss Functions for Structure
20
Hardware Acceleration
21