MLCancerDetection

Project for TÜ Machine Learning course.

Authors

• Karl-Joan Alesma
• Karl Oskar Kuuse
• Randal Annus

Description

This repository contains code for the kaggle competition UBC Ovarian Cancer Subtype Classification and Outlier Detection (UBC-OCEAN).

The training directory contains notebooks used for training models. The submissions contains notebooks that were used as submissions to the competition.

The submissions used for the final leaderboard score contain FINAL in the notebook name.

We used a tiled dataset and image tiling code [1] to train models. We trained models based on architectures laid out in several papers [2][3][4][5][6][7][8].

Citations

1. J. Borovec (2023). Tiled Images dataset (512x512) scale 0.25 all-in-one. Accessed 16.12.2023 https://www.kaggle.com/competitions/UBC-OCEAN/discussion/451908
2. J. Linmans, S. Elfwing, J. Laak, G. Litjens. (2023). Predictive uncertainty estimation for out-of-distribution detection in digital pathology, Medical Image Analysis, Volume 83, https://doi.org/10.1016/j.media.2022.102655.
3. B. Li, Y. Li, K. W. Eliceiri (2021). Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14318--14328.
4. Z. Shao, H. Bian, Y. Chen Y. Wang, J. Zhang, X. Ji and others (2021). Transmil: Transformer based correlated multiple instance learning for whole slide image classification. Advances in Neural Information Processing Systems, volume 34, pages 2136--2147.
5. M. H. Daneshvar, H. Sarmadi, K.-V. Yuen (2023). A locally unsupervised hybrid learning method for removing environmental effects under different measurement periods. Measurement, Volume 208, page 112465. https://doi.org/10.1016/j.measurement.2023.112465.
6. M. Ilse, J. Tomczak, M. Welling (2018). Attention-based Deep Multiple Instance Learning. Proceedings of the 35th International Conference on Machine Learning, Volume 80, Pages 2127--2136. http://proceedings.mlr.press/v80/ilse18a/ilse18a.pdf. https://proceedings.mlr.press/v80/ilse18a.html.
7. J. Zhang, X. Zhang, K. Ma, R. Gupta, J. Saltz, M. Vakalopoulou, D. Samaras (2022). Gigapixel Whole-Slide Images Classification Using Locally Supervised Learning. International Conference on Medical Image Computing and Computer-Assisted Intervention, Pages 192--201. Springer.
8. K. J. Alesma (2023). Gigapixel Whole-Slide Images Classification using Locally Supervised Learning. Accessed 17.12.2023. https://github.com/karl-joan/local_learning_wsi.