To develop a robust and interpretable model for colorectal cancer classification that specifically targets weakly labeled data.
Key Findings:
Achieved an AUC of 0.895 on the TCGA-CRC dataset, significantly outperforming ResNet (0.777), EfficientNet (0.791), and ShuffleNet (0.784), indicating superior accuracy and robustness.
Demonstrated improved classification performance in weakly supervised settings, highlighting the model's potential for real-world clinical applications.
Interpretation:
The proposed model enhances accuracy and robustness in colorectal cancer detection, making it suitable for clinical applications.
Limitations:
The model's performance may vary with different datasets and types of cancer, suggesting the need for further validation across diverse clinical settings.
Computational demands may limit scalability in certain clinical settings, indicating a need for optimization in future iterations.
Conclusion:
This work presents a promising tool for automated colorectal cancer detection, with significant potential for clinical deployment and improved patient outcomes.
