Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation
For researchers in materials science using deep learning for TEM defect analysis, this work provides a method to improve model performance when labeled data is scarce, though gains are small and split-dependent.
The paper introduces a mask-conditioned latent diffusion model for generating synthetic TEM defect images to augment small experimental datasets. Augmentation with synthetic data yielded up to a 0.02 gain in the harmonic mean of detection and classification F1 scores for a Mask R-CNN model.
Analyzing microstructural defects in transmission electron microscopy (TEM) images, particularly in irradiated metal alloys, is often limited by the availability of high-quality, labeled data. To address this, we introduce a generative data augmentation approach using a mask-conditioned latent diffusion model (LDM) for synthesizing realistic TEM images with controllable, automatically labeled multi-class defect masks. Without requiring manual annotations for generation, our method enables the creation of synthetic image-mask pairs by sampling distributions learned from experimental masks. These generated data were used to augment small experimental datasets of varying sizes (10, 50, and 100 labeled experimental images) to train a Mask Regional Convolutional Neural Network (R-CNN) model for defect detection and classification. Our results show that generative augmentation yields small overall model performance improvements, with up to a 0.02 gain in the harmonic mean of detection and classification F1 scores. However, we also find that the relative contributions to detection and classification improvement depend on the specific train/test data split. These findings highlight the potential of targeted generative models to enhance deep learning performance in data-scarce microscopy-based image quantification tasks.