CanvOI, an Oncology Intelligence Foundation Model: Scaling FLOPS Differently

TL;DR

CanvOI is a novel digital pathology foundation model that employs larger tile sizes and smaller patch sizes to improve cancer diagnosis accuracy, especially with limited data, achieving state-of-the-art results.

Contribution

This work introduces a unique approach by modifying input image characteristics to enhance model performance in digital oncology, differing from traditional scaling methods.

Findings

Achieved 1.5-7.4% higher AUC than existing models.

Significantly outperformed other models with only 10% of the training data.

Demonstrated potential to improve clinical outcomes in cancer diagnosis.

Abstract

The rapidly evolving field of digital oncopathology faces significant challenges, including the need to address diverse and complex clinical questions, often involving rare conditions, with limited availability of labeled data. These limitations hinder the development of robust AI-driven tools in the biomedical space, where accuracy in probabilistic determinations is of utmost importance. To address this, digital pathology foundation models have begun to emerge, typically developed with the size and diversity of the pre-training dataset and model parameters in mind. Here, we present CanvOI, a ViT-g/10-based foundation model designed to enhance the capabilities of digital pathology by addressing these challenges through a different approach. Considering the unique nature of oncologic histopathological images and the requirements from the embeddings to provide meaningful representations for Multiple Instance Learning (MIL) downstream models, we chose to modify the input image characteristics. By introducing larger tile sizes (380 x 380 pixels) and smaller patch sizes (10 x 10 pixels), we were able to optimize the model's performance, pushing computational resources in a new direction and achieving state-of-the-art performance on cancer-related benchmarks. CanvOI demonstrated a 1.5-7.4% improvement in averaged AUC compared to other leading foundation models built for digital pathology. Moreover, our results demonstrate that CanvOI significantly outperformed the other models, with the performance gap widening substantially when trained on just 10% of the initial cohort. This work highlights an alternative approach that, if integrated with traditional development approaches, has the potential to advance Oncology Intelligence (OI), overcome some of the current barriers and ultimately improve the clinical outcome of cancer patients.