Biochemical Prostate Cancer Recurrence Prediction: Thinking Fast & Slow

2024-09-03 · Suhang You, Sanyukta Adap, Siddhesh Thakur, Bhakti Baheti, Spyridon Bakas

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 ($\theta=0.059$) 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.

📄 PDF Abstract BibTeX arXiv:2409.02284

Code (1)

yousuhang/IU-ComPath-LeoPard 공식 구현 pytorch

Tasks

Multiple Instance Learning Prediction

Methods 이 논문이 사용한 방법론

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Attention 설명 없음

HOC 설명 없음

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Multiple Instance Learning