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Addressing biases in gastric cancer diagnosis through generative models and vision-based surface tactile sensing

Siddhartha Kapuria,Naruhiko Ikoma,Sandeep P. Chinchali,Farshid Alambeigi

Published 2025 in Medical Imaging

ABSTRACT

Towards developing an informed, intuitive, and generalized artificial intelligence model for endoscopic diagnosis of Advanced Gastric Cancer (AGC) lesions, in this work, we extend the use of generative models to augment limited and biased training datasets. This approach aims to enhance model performance and generalizability, tackling the challenge of acquiring sizable and well-balanced datasets in the clinical domain. We applied this approach to our unique dataset comprised of textural images of additively-manufactured realistic AGC tumor phantoms collected using a robotic data collection system and our unique Vision-based Tactile Sensor - HySenSe. Our approach was particularly suited to this dataset, since HySenSe’s limited sensing area captures only partial images of the much larger AGC lesions, which reduces the amount of information available in a single image. We trained a single Class-Conditioned Latent Diffusion Model (CC-LDM) on an artificially imbalanced dataset of HySenSe images, logging performance using Fr´echet Inception Distance (FID), and exploring different hyperparameter configurations. Using the high-quality synthetic images generated using this model, we performed experiments using our previously developed Dilated ResNet model for AGC tumor classification with the following objectives: (i) study the amount of synthetic data required during training, and (ii) compare different data addition strategies during model training using cross-validation. Note that while the models were trained using both real and synthetic textural images, testing was performed solely on real images. Our experiments showed that the trained classifier demonstrates improved generalizability when trained on a mixture of real and synthetic images, but not all methods of mixing the data are equally effective. Overall, the model shows improved performance even under mixed morphological tumor conditions and partial sensor contact.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    Medical Imaging

  • Publication date

    2025-04-07

  • Fields of study

    Medicine, Computer Science, Engineering

  • Identifiers
    DOI 10.1117/12.3047015
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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