Diverse Image Generation with Diffusion Models and Cross Class Label Learning for Polyp Classification

• URL: http://arxiv.org/abs/2502.05444v1
• Date: Sat, 08 Feb 2025 04:26:20 GMT
• Title: Diverse Image Generation with Diffusion Models and Cross Class Label Learning for Polyp Classification
• Authors: Vanshali Sharma, Debesh Jha, M. K. Bhuyan, Pradip K. Das, Ulas Bagci,
• Abstract summary: We develop a novel model, PathoPolyp-Diff, that generates text-controlled synthetic images with diverse characteristics. We introduce cross-class label learning to make the model learn features from other classes, reducing the burdensome task of data annotation.
• Score: 4.747649393635696
• License:
• Abstract: Pathologic diagnosis is a critical phase in deciding the optimal treatment procedure for dealing with colorectal cancer (CRC). Colonic polyps, precursors to CRC, can pathologically be classified into two major types: adenomatous and hyperplastic. For precise classification and early diagnosis of such polyps, the medical procedure of colonoscopy has been widely adopted paired with various imaging techniques, including narrow band imaging and white light imaging. However, the existing classification techniques mainly rely on a single imaging modality and show limited performance due to data scarcity. Recently, generative artificial intelligence has been gaining prominence in overcoming such issues. Additionally, various generation-controlling mechanisms using text prompts and images have been introduced to obtain visually appealing and desired outcomes. However, such mechanisms require class labels to make the model respond efficiently to the provided control input. In the colonoscopy domain, such controlling mechanisms are rarely explored; specifically, the text prompt is a completely uninvestigated area. Moreover, the unavailability of expensive class-wise labels for diverse sets of images limits such explorations. Therefore, we develop a novel model, PathoPolyp-Diff, that generates text-controlled synthetic images with diverse characteristics in terms of pathology, imaging modalities, and quality. We introduce cross-class label learning to make the model learn features from other classes, reducing the burdensome task of data annotation. The experimental results report an improvement of up to 7.91% in balanced accuracy using a publicly available dataset. Moreover, cross-class label learning achieves a statistically significant improvement of up to 18.33% in balanced accuracy during video-level analysis. The code is available at https://github.com/Vanshali/PathoPolyp-Diff.

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