Active Prompt Tuning Enables Gpt-40 To Do Efficient Classification Of Microscopy Images Article Swipe
Abhiram Kandiyana
,
Peter R. Mouton
,
Yaroslav Kolinko
,
Lawrence Hall
,
Dmitry B. Goldgof
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.02639
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.02639
Traditional deep learning-based methods for classifying cellular features in microscopy images require time- and labor-intensive processes for training models. Among the current limitations are major time commitments from domain experts for accurate ground truth preparation; and the need for a large amount of input image data. We previously proposed a solution that overcomes these challenges using OpenAI's GPT-4(V) model on a pilot dataset (Iba-1 immuno-stained tissue sections from 11 mouse brains). Results on the pilot dataset were equivalent in accuracy and with a substantial improvement in throughput efficiency compared to the baseline using a traditional Convolutional Neural Net (CNN)-based approach. The present study builds upon this framework using a second unique and substantially larger dataset of microscopy images. Our current approach uses a newer and faster model, GPT-4o, along with improved prompts. It was evaluated on a microscopy image dataset captured at low (10x) magnification from cresyl-violet-stained sections through the cerebellum of a total of 18 mouse brains (9 Lurcher mice, 9 wild-type controls). We used our approach to classify these images either as a control group or Lurcher mutant. Using 6 mice in the prompt set the results were correct classification for 11 out of the 12 mice (92%) with 96% higher efficiency, reduced image requirements, and lower demands on time and effort of domain experts compared to the baseline method (snapshot ensemble of CNN models). These results confirm that our approach is effective across multiple datasets from different brain regions and magnifications, with minimal overhead.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.02639
- https://arxiv.org/pdf/2411.02639
- OA Status
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https://doi.org/10.48550/arxiv.2411.02639Digital Object Identifier
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Active Prompt Tuning Enables Gpt-40 To Do Efficient Classification Of Microscopy ImagesWork title
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preprintOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-11-04Full publication date if available
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Abhiram Kandiyana, Peter R. Mouton, Yaroslav Kolinko, Lawrence Hall, Dmitry B. GoldgofList of authors in order
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https://arxiv.org/abs/2411.02639Publisher landing page
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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Microscopy, Computer science, Artificial intelligence, Computer vision, Materials science, Optics, PhysicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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