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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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STARC-9: A Large-scale Dataset for Multi-Class Tissue Classification for CRC Histopathology.

Barathi Subramanian¹, Rathinaraja Jeyaraj¹, Mitchell Nevin Peterson²

¹Department of Pathology, Stanford University, USA.

Advances in Neural Information Processing Systems

|May 6, 2026

Summary

This summary is machine-generated.

Researchers developed STARC-9, a large colorectal cancer (CRC) dataset, to improve machine learning models. This dataset enhances model generalizability for better cancer diagnosis and treatment planning.

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Area of Science:

Digital Pathology
Machine Learning in Oncology
Computational Biology

Background:

Accurate colorectal cancer (CRC) tissue classification is crucial for machine learning-driven diagnosis and treatment.
Existing CRC datasets lack morphologic diversity, class balance, and image quality, hindering model performance and generalizability.

Purpose of the Study:

Introduce STARC-9, a large-scale dataset for multi-class CRC tissue classification.
Address limitations of current datasets by providing diverse, high-quality histopathologic image tiles.
Validate the utility of STARC-9 for improving downstream machine learning models.

Main Methods:

Developed DeepCluster++, a novel framework for semi-automated dataset curation.
Utilized autoencoder-based feature extraction and K-means clustering for tile grouping.
Employed equal-frequency binning and expert pathologist verification to ensure tile diversity and accuracy.

Main Results:

Created STARC-9 with 630,000 histopathologic tiles across nine CRC tissue classes.
Demonstrated superior generalizability of machine learning models trained on STARC-9 compared to those trained on existing datasets.
Validated STARC-9's effectiveness on multi-class CRC tissue classification and segmentation tasks.

Conclusions:

STARC-9 significantly enhances the performance and generalizability of machine learning models for CRC tissue classification.
The DeepCluster++ framework offers a flexible approach for constructing high-quality datasets from whole-slide images (WSI) across various applications.
STARC-9 facilitates advancements in AI-powered cancer diagnostics and personalized medicine.