Summary of Biochemical Prostate Cancer Recurrence Prediction: Thinking Fast & Slow, by Suhang You et al.
Biochemical Prostate Cancer Recurrence Prediction: Thinking Fast & Slow
by Suhang You, Sanyukta Adap, Siddhesh Thakur, Bhakti Baheti, Spyridon Bakas
First submitted to arxiv on: 3 Sep 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 The proposed approach leverages multiple instance learning through a two-stage strategy, dubbed “thinking fast & slow”, for time-to-biochemical-recurrence (TTR) prediction in prostate cancer patients after prostatectomy. The first stage identifies relevant whole-slide imaging (WSI) areas, while the second stage uses higher-resolution patches to predict TTR. The approach achieves a mean C-index of 0.733 on internal validation and 0.603 on LEOPARD challenge validation, with post-hoc attention visualization showing that attentive areas contribute to TTR prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to predict how long it takes for prostate cancer to come back after surgery. The method uses special imaging techniques to identify the most important parts of the tissue and then predicts when the cancer will return. The results show that this approach is pretty good at predicting when the cancer will come back, which can help doctors decide what treatment options are best for each patient. |
Keywords
» Artificial intelligence » Attention
