Multimodal Deep Learning for Scientific Imaging Interpretation

• DOI: 10.48550/arxiv.2309.12460
• Publication Date: 2023-01-01
• Authors: Abdulelah S. Alsh...; Franklin L. Lee; Shihu Wang
• Keywords: Benchmarking; Expansive; Robustness

In the domain of scientific imaging, interpreting visual data often demands an intricate combination human expertise and deep comprehension subject materials. This study presents a novel methodology to linguistically emulate subsequently evaluate human-like interactions with Scanning Electron Microscopy (SEM) images, specifically glass Leveraging multimodal learning framework, our approach distills insights from both textual harvested peer-reviewed articles, further augmented by capabilities GPT-4 for refined synthesis evaluation. Despite inherent challenges--such as nuanced interpretations limited availability specialized datasets--our model (GlassLLaVA) excels in crafting accurate interpretations, identifying key features, detecting defects previously unseen SEM images. Moreover, we introduce versatile evaluation metrics, suitable array imaging applications, which allows benchmarking against research-grounded answers. Benefiting robustness contemporary Large Language Models, adeptly aligns research papers. advancement not only underscores considerable progress bridging gap between machine interpretation but also hints at expansive avenues future broader application.