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
- Scaling Large Vision-Language Models for Enhanced Multimodal Comprehension In Biomedical Image Analysis [0.1984949535188529]
Vision language models (VLMs) address this by incorporating a pretrained vision backbone for processing images and a cross-modal projector.
We developed intelligent assistants finetuned from LLaVA models to enhance multimodal understanding in low-dose radiation therapy.
arXiv Detail & Related papers (2025-01-26T02:48:01Z) - Probing the limitations of multimodal language models for chemistry and materials research [3.422786943576035]
We introduce MaCBench, a benchmark for evaluating how vision-language models handle real-world chemistry and materials science tasks.
We find that while these systems show promising capabilities in basic perception tasks, they exhibit fundamental limitations in spatial reasoning, cross-modal information synthesis, and logical inference.
Our insights have important implications beyond chemistry and materials science, suggesting that developing reliable multimodal AI scientific assistants may require advances in curating suitable training data and approaches to training those models.
arXiv Detail & Related papers (2024-11-25T21:51:45Z) - MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific Understanding [59.41495657570397]
We present a comprehensive dataset compiled from Nature Communications articles covering 72 scientific fields.
We evaluated 19 proprietary and open-source models on two benchmark tasks, figure captioning and multiple-choice, and conducted human expert annotation.
Fine-tuning Qwen2-VL-7B with our task-specific data achieved better performance than GPT-4o and even human experts in multiple-choice evaluations.
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) - 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.