Improving Lymph Node Metastasis Classification Using Data Augmentation with Generative Models and Filtering

doi:10.1142/s1793351x2642002x

Improving Lymph Node Metastasis Classification Using Data Augmentation with Generative Models and Filtering

2026-05-01

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DOI：10.1142/s1793351x2642002x

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When a patient is diagnosed with rectal cancer, staging is performed to assess whether the cancer has metastasized to other parts of the body using diagnostic imaging such as CT scans. Recently, there has been a growing interest in applying machine learning for diagnostic support. In our previous studies, we proposed methods to augment limited datasets using generative models; however, concerns were raised that these models might generate unintended images. In this study, we aim to improve the accuracy of lymph node classification. We introduce Denoising Diffusion Probabilistic Models (DDPM) in addition to the Denoising Diffusion Implicit Models (DDIM) employed in prior work. Furthermore, we propose two filtering methods to eliminate unintended images. Additionally, we conducted a comprehensive parameter analysis of the diffusion schedules for both DDIM and DDPM to optimize the generation process.

Keywords:

Rectal cancer

imaging

medical imaging

machine learning

diffusion model

DDPM

DDIM

vision transformer

lymph node classification

Improving Lymph Node Metastasis Classification Using Data Augmentation with Generative Models and Filtering

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