Computationally efficient decoupled momentum optimization algorithm for medical imaging models.

Area of Science:

• Medical Image Analysis
• Machine Learning Optimization

Background:

• Gradient-based optimization is crucial for deep learning models in medical image segmentation.
• Existing optimizers can face challenges with network traffic and computational overhead.

Purpose of the Study:

• To evaluate the Decoupled Momentum Optimizer (DeMo) for medical image segmentation.
• To analyze DeMo's parameter behavior and its extensibility beyond Large Language Models (LLMs).

Main Methods:

• DeMo utilizes a frequency decomposition compression algorithm to reduce gradient redundancy, network traffic, and noise.
• The study analyzes gradient properties like spatial autocorrelation and temporal variance.
• DeMoDropout, a variant, selectively compresses large gradients for improved efficiency.

Main Results:

• DeMo achieved up to 150x traffic reduction and 1.6x speedup in lung CT segmentation.
• Gradient analysis supported conjectures for a subset of parameters, showing higher spatial autocorrelation and lower temporal variance.
• DeMoDropout demonstrated speedups of 1.6x and 6.151x at 1 Gb/s and 100 Mb/s, respectively.

Conclusions:

• DeMo is an effective optimizer for medical image segmentation, offering significant efficiency gains.
• The study validates DeMo's underlying principles for specific parameter groups.
• DeMo and DeMoDropout show promise for accelerating deep learning applications with bandwidth constraints.