Summary of Self-contrastive Weakly Supervised Learning Framework For Prognostic Prediction Using Whole Slide Images, by Saul Fuster et al.
Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images
by Saul Fuster, Farbod Khoraminia, Julio Silva-Rodríguez, Umay Kiraz, Geert J. L. H. van Leenders, Trygve Eftestøl, Valery Naranjo, Emiel A. M. Janssen, Tahlita C. M. Zuiverloon, Kjersti Engan
First submitted to arxiv on: 24 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A pioneering study in deep learning tackles the challenge of automated prognostic prediction from histopathological images, leveraging a novel three-part framework comprising convolutional networks, contrastive learning, and nested multiple instance learning. The framework is applied to bladder cancer use cases, achieving AUCs of 0.721 and 0.678 for recurrence and treatment outcome prediction respectively. This research explores the significance of various regions of interest within histopathological slides and exploits diverse learning scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study uses artificial intelligence to predict what will happen in the future based on pictures of tissue samples from cancer patients. Right now, doctors look at these pictures to figure out how well a patient will do, but this is a hard job and can be wrong sometimes. The researchers created a special computer program that looks at the pictures and tries to guess what will happen. They tested their program on fake pictures and it did okay. Then they used real pictures of bladder cancer and their program did pretty well predicting what would happen. |
Keywords
» Artificial intelligence » Deep learning
