cs640-ubcocean-competition's Introduction

CS640 Course Project

Installation Instructions

For installation instructions, refer to Installation Guide

Introduction

Our project involved tackling the UBC OCEAN competition, where we were provided with 70% of the data to build a model. Later, our code was evaluated on a hidden test set comprising 30% of the available data.

Steps

Create Graph from image

Image Source: Graph Transformer(Yi et al.)

Creating graph based models on this data.

For more detailed description checkout Project Summary

Model architecture

Project Presentation

Project Report

References

Lu, M., Williamson, D., Chen, T., Chen, R., Barbieri, M., & Mahmood, F. (2021). Data-efficient and weakly supervised computational pathology on whole-slide images. Nature Biomedical Engineering, 5, 1-16. DOI
Wang, X., Yang, S., Zhang, J., Wang, M., Zhang, J., Yang, W., Huang, J., & Han, X. (2022). Transformer-based unsupervised contrastive learning for histopathological image classification. Medical Image Analysis, 81, 102559. DOI
Zheng, Y., Gindra, R., Green, E., Burks, E., Betke, M., Beane, J., & Kolachalama, V. (2022). A Graph-Transformer for Whole Slide Image Classification. IEEE Transactions on Medical Imaging, PP. DOI
Bradski, G. (2000). The OpenCV Library. Dr. Dobb's Journal of Software Tools. Link
Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man Cybern. Link
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv
Ilse, M., Tomczak, J. M., & Welling, M. (2018). Attention-based Deep Multiple Instance Learning. arXiv

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