InstantDL

InstantDL implements convolutional neural networks (CNNs) for semantic segmentation, instance segmentation, pixel-wise regression, and classification of biomedical images to quantify molecular and cellular structures.

Key Features:

• Task Coverage: Supports semantic segmentation, instance segmentation, pixel-wise regression, and classification of biomedical images.
• Convolutional Neural Networks (CNNs): Implements CNN-based architectures for image segmentation, regression, and classification tasks.
• Automation and Standardization: Provides automated and standardized workflows to reduce manual parameter tuning and ensure consistent processing.
• Uncertainty Assessment: Computes uncertainty estimates for model predictions to inform reliability of results.
• Performance Benchmarking: Benchmarked on seven publicly available datasets and reported competitive performance without parameter adjustments.

Scientific Applications:

• Biomedical image analysis: Enables segmentation, instance detection, regression, and classification across diverse biomedical imaging datasets.
• Molecular and cellular quantification: Supports quantitative analysis of molecular and cellular processes from microscopy images.

Methodology:

Computational methods explicitly include convolutional neural networks (CNNs), automated and standardized workflows, uncertainty estimation of predictions, and benchmarking on publicly available datasets.