DLAE

DLAE enables development, training, validation, and deployment of convolutional neural networks (CNNs), fully convolutional networks (FCNs), generative adversarial networks (GANs), and bounding box detectors for medical imaging analysis.

Key Features:

• Support for Multiple DL Techniques: Supports convolutional neural networks (CNNs), fully convolutional networks (FCNs), generative adversarial networks (GANs), and bounding box detectors for medical imaging tasks.
• Integration into Clinical Workflows: Provides pathways to integrate trained models into clinical workflows and existing healthcare infrastructures.
• Model Deployment: Enables integration of trained deep learning models into commercial clinical software packages.

Scientific Applications:

• Disease Detection: Applied to disease detection using clinical medical images.
• Diagnosis Support: Used for diagnostic support in clinical imaging contexts.
• Image Enhancement: Employed for image enhancement of clinical images.

Methodology:

Design, training, validation, and deployment of deep learning models, including CNNs, FCNs, GANs, and bounding box detectors, and integration of trained models into commercial clinical software.