Explainable machine learning for diffraction patterns.

Area of Science:

• Crystallography
• Artificial Intelligence
• Data Science

Background:

• Serial crystallography experiments generate vast datasets, necessitating efficient data reduction.
• Accurate classification of 'hit' and 'miss' data is crucial for downstream analysis in X-ray free-electron laser experiments.
• Current convolutional neural networks (CNNs) for data classification lack transparency, operating as 'black boxes'.

Purpose of the Study:

• To qualitatively investigate the internal workings of CNNs used for serial crystallography data classification.
• To develop visualization methods that highlight image features critical for CNN predictions.
• To enhance the interpretability of deep learning models in structural biology data analysis.

Main Methods:

• Application of image classification techniques using convolutional neural networks (CNNs).
• Development and implementation of visualization methods to interpret CNN predictions.
• Qualitative analysis of feature contributions to 'hit' and 'miss' classifications in serial crystallography datasets.

Main Results:

• Successful classification of serial crystallography data into 'hit' and 'miss' categories using CNNs.
• Visualization techniques successfully identified image regions influencing classification outcomes.
• Demonstration of feature importance for specific predictions within the CNN architecture.

Conclusions:

• This study provides qualitative insights into CNNs for serial crystallography data analysis.
• Visualizing feature contributions demystifies the 'black box' nature of these deep learning models.
• Interpretability of CNNs can improve confidence and guide further development in high-throughput structural biology.