Applyingdata augmentation techniques(C) is the most likely action to improve the model’s

generalization on unseen medical imaging data. Let’s dive into why:

What is generalization?: Generalization is a model’s ability to perform well on new, unseen data,

avoiding overfitting to the training set. Overfitting occurs when a model memorizes training data

(e.g., specific image patterns) rather than learning robust features (e.g., anomaly shapes).

Role of data augmentation: Augmentation artificially expands the training dataset by applying

transformations (e.g., rotations, flips, brightness changes) to medical images, simulating real-world

variability (e.g., different lighting, angles in scans). This forces the model to learn invariant features,

improving its performance on diverse test data. For example, rotating an X-ray image ensures the

model recognizes anomalies regardless of orientation.

Implementation: NVIDIA’s DALI or cuAugment can GPU-accelerate augmentation,integrating

seamlessly with training pipelines on NVIDIA infrastructure. Techniques like random crops or noise

injection are particularly effective for medical imaging.

Evidence: The symptom—high training accuracy, low test accuracy—indicates overfitting, a common

issue in deep learning, especially with limited or uniform datasets like medical images.

Augmentation is a standard remedy.

Why not the other options?

A (Fewer epochs): Reduces training time, potentially underfitting, not addressing overfitting.

B (Larger batch size): Improves training stability but doesn’t inherently enhance generalization; it

may even mask overfitting by smoothing gradients.

D (More complex model): Increases capacity, worsening overfitting if data variety isn’t addressed.

NVIDIA’s healthcare AI resources endorse augmentation for robust models (C).

Reference:NVIDIA Healthcare AI Guide; DALI Augmentation documentation on nvidia.com.