1 research outputs found
ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysis
Micropaleontology in geosciences focuses on studying the evolution of
microfossils (e.g., foraminifera) through geological records to reconstruct
past environmental and climatic conditions. This field heavily relies on visual
recognition of microfossil features, making it suitable for computer vision
technology, specifically deep convolutional neural networks (CNNs), to automate
and optimize microfossil identification and classification. However, the
application of deep learning in micropaleontology is hindered by limited
availability of high-quality, high-resolution labeled fossil images and the
significant manual labeling effort required by experts. To address these
challenges, we propose a novel deep learning workflow combining hierarchical
vision transformers with style-based generative adversarial network algorithms
to efficiently acquire and synthetically generate realistic high-resolution
labeled datasets of micropaleontology in large volumes. Our study shows that
this workflow can generate high-resolution images with a high signal-to-noise
ratio (39.1 dB) and realistic synthetic images with a Frechet inception
distance similarity score of 14.88. Additionally, our workflow provides a large
volume of self-labeled datasets for model benchmarking and various downstream
visual tasks, including fossil classification and segmentation. For the first
time, we performed few-shot semantic segmentation of different foraminifera
chambers on both generated and synthetic images with high accuracy. This novel
meta-learning approach is only possible with the availability of
high-resolution, high-volume labeled datasets. Our deep learning-based workflow
shows promise in advancing and optimizing micropaleontological research and
other visual-dependent geological analyses