Multimodal Deep Learning for Scientific Imaging Interpretation

• URL: http://arxiv.org/abs/2309.12460v2
• Date: Mon, 25 Sep 2023 23:11:34 GMT
• Title: Multimodal Deep Learning for Scientific Imaging Interpretation
• Authors: Abdulelah S. Alshehri, Franklin L. Lee, Shihu Wang
• Abstract summary: This study presents a novel methodology to linguistically emulate and evaluate human-like interactions with Scanning Electron Microscopy (SEM) images. Our approach distills insights from both textual and visual data harvested from peer-reviewed articles. Our model (GlassLLaVA) excels in crafting accurate interpretations, identifying key features, and detecting defects in previously unseen SEM images.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the domain of scientific imaging, interpreting visual data often demands an intricate combination of human expertise and deep comprehension of the subject materials. This study presents a novel methodology to linguistically emulate and subsequently evaluate human-like interactions with Scanning Electron Microscopy (SEM) images, specifically of glass materials. Leveraging a multimodal deep learning framework, our approach distills insights from both textual and visual data harvested from peer-reviewed articles, further augmented by the capabilities of GPT-4 for refined data synthesis and evaluation. Despite inherent challenges--such as nuanced interpretations and the limited availability of specialized datasets--our model (GlassLLaVA) excels in crafting accurate interpretations, identifying key features, and detecting defects in previously unseen SEM images. Moreover, we introduce versatile evaluation metrics, suitable for an array of scientific imaging applications, which allows for benchmarking against research-grounded answers. Benefiting from the robustness of contemporary Large Language Models, our model adeptly aligns with insights from research papers. This advancement not only underscores considerable progress in bridging the gap between human and machine interpretation in scientific imaging, but also hints at expansive avenues for future research and broader application.

Related papers

• ARPA: A Novel Hybrid Model for Advancing Visual Word Disambiguation Using Large Language Models and Transformers [1.6541870997607049]
  We present ARPA, an architecture that fuses the unparalleled contextual understanding of large language models with the advanced feature extraction capabilities of transformers. ARPA's introduction marks a significant milestone in visual word disambiguation, offering a compelling solution. We invite researchers and practitioners to explore the capabilities of our model, envisioning a future where such hybrid models drive unprecedented advancements in artificial intelligence.
  arXiv  Detail & Related papers  (2024-08-12T10:15:13Z)
• MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific Understanding [59.41495657570397]
  This dataset includes figures such as schematic diagrams, simulated images, macroscopic/microscopic photos, and experimental visualizations. We developed benchmarks for scientific figure captioning and multiple-choice questions, evaluating six proprietary and over ten open-source models. The dataset and benchmarks will be released to support further research.
  arXiv  Detail & Related papers  (2024-07-06T00:40:53Z)
• SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval [64.03631654052445]
  Current benchmarks for evaluating MMIR performance in image-text pairing within the scientific domain show a notable gap. We develop a specialised scientific MMIR benchmark by leveraging open-access paper collections. This benchmark comprises 530K meticulously curated image-text pairs, extracted from figures and tables with detailed captions in scientific documents.
  arXiv  Detail & Related papers  (2024-01-24T14:23:12Z)
• StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data [129.92449761766025]
  We propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models. Our research includes comprehensive experiments conducted on various datasets.
  arXiv  Detail & Related papers  (2023-08-20T12:43:52Z)
• Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data" [0.0]
  In-situ TEM experiments can provide important insights into how dislocations behave and move. The analysis of individual video frames can provide useful insights but is limited by the capabilities of automated identification. In this work, a parametric model for generating synthetic training data for segmentation of dislocations is developed.
  arXiv  Detail & Related papers  (2023-07-12T17:37:46Z)
• Domain Generalization for Mammographic Image Analysis with Contrastive Learning [62.25104935889111]
  The training of an efficacious deep learning model requires large data with diverse styles and qualities. A novel contrastive learning is developed to equip the deep learning models with better style generalization capability. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets.
  arXiv  Detail & Related papers  (2023-04-20T11:40:21Z)
• The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations [0.0]
  Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models. We present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization.
  arXiv  Detail & Related papers  (2022-12-22T14:29:43Z)
• Semantic segmentation of multispectral photoacoustic images using deep learning [53.65837038435433]
  Photoacoustic imaging has the potential to revolutionise healthcare. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. We present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images.
  arXiv  Detail & Related papers  (2021-05-20T09:33:55Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.