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February 14, 2026Journal of Japan Society for Fuzzy Theory and Intelligent InformaticsOpen Access

Development of a Transfer Learning-Based Interactive Tool for Pathological Image Tissue Segmentation

Takeshi Terazawa Hoshiro SATO Toshiya Arakawa

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Key Points

The tool achieves enhanced efficiency, maintaining accuracy comparable to manual methods despite substantial workload reduction.
It leverages transfer learning and random forest algorithms to classify tissue image components such as cell nuclei and collagen.
The evaluation utilized metrics including the Intersection over Union (IoU) and the number of annotation operations performed during testing.
Future enhancements may focus on incorporating additional shape features to improve accuracy and efficiency in identifying target tissues.

Implication

Interactive tool demonstrates improved efficiency in tissue segmentation, suggesting further enhancements are needed.

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Cite This Study

Terazawa et al. (2026) studied this question.

synapsesocial.com/papers/69a76121c6e9836116a2ec5e https://doi.org/10.3156/jsoft.38.1_511

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