Biochemical Prostate Cancer Recurrence Prediction: Thinking Fast & Slow
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Abstract (arXiv)
Time to biochemical recurrence in prostate cancer is essential for prognostic monitoring of the progression of patients after prostatectomy, which assesses the efficacy of the surgery. In this work, we proposed to leverage multiple instance learning through a two-stage ``thinking fast & slow'' strategy for the time to recurrence (TTR) prediction. The first (``thinking fast'') stage finds the most relevant WSI area for biochemical recurrence and the second (``thinking slow'') stage leverages higher resolution patches to predict TTR. Our approach reveals a mean C-index (Ci) of 0.733 (θ=0.059) on our internal validation and Ci=0.603 on the LEOPARD challenge validation set. Post hoc attention visualization shows that the most attentive area contributes to the TTR prediction.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 21 references that are in the map. First posted on arXiv 3 Sep 2024.
Builds on
7 of 21 references.
References · 7
- 2018Attention-based Deep Multiple Instance Learningcited by 1,077
- 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 738
- 2020Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networkscited by 178
- 2016Multiple Instance Learning: A Survey of Problem Characteristics and Applicationscited by 163
- 2023Attention-Challenging Multiple Instance Learning for Whole Slide Image Classificationcited by 61
- 2022Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potentialcited by 54
- 2024Multi-head Attention-based Deep Multiple Instance Learningcited by 6
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Citing papers per year: 2025: 2.
2 papers.