何時、何地與如何：表格自監督學習的自適應分箱

摘要

醫學表格數據在臨床研究中普遍存在，但深度學習在表格上的應用仍未被充分探索，因為可靠的標籤通常需要昂貴的專家裁決，即便結構化的臨床變數常規上能以表格形式取得。自我監督學習可利用這些未標記的表格，而近期基於分箱的前置任務提供了一種有前景的歸納偏置，但現有的目標函數固定於單一全局分位數離散化，並採用與特徵無關的監督方式。我們提出自適應分箱（Adaptive Binning），這是一種用於表格自我監督學習的訓練自適應離散化前置任務，透過逐特徵從粗到細的課程機制，將離散化與學習過程相耦合。受神經網絡的譜偏置及課程學習原理啟發，我們的方法在檢測到學習停滯時逐步細化每個特徵的離散化，並選取具表徵感知的分割點，以同時提升數值空間的集中性與表徵空間的連貫性。一個異質性感知的目標函數統一了類別重建與數值特徵的序數監督。在統一評估協議下，於公開醫學表格數據集上的實驗顯示，無論是線性探測或微調，均能在不依賴資料集特定離散化調整的情況下取得一致的性能提升。我們進一步引入一個附標準化協議的醫學表格自我監督學習基準，以支持在該未充分探索領域中實現可重複的進展。我們的程式碼可於 https://github.com/labhai/Adaptive-Binning 取得。

English

Medical tabular data are ubiquitous in clinical research, but deep learning for tables remains underexplored because reliable labels often require costly expert adjudication, even though structured clinical variables are routinely available in tabular form. Self-supervised learning can leverage these unlabeled tables, and recent binning-based pretexts offer a promising inductive bias, but existing objectives fix a single global quantile discretization and apply feature-agnostic supervision. We propose Adaptive Binning, a training-adaptive discretization pretext for tabular SSL that couples discretization to learning through a feature-wise coarse-to-fine curriculum. Motivated by the spectral bias of neural networks and the principles of curriculum learning, our method progressively refines discretization per feature upon plateau detection and selects representation-aware splits to jointly improve value-space concentration and representation-space coherence. A heterogeneity-aware objective unifies categorical reconstruction with ordinal supervision for numerical features, and experiments on public medical tabular datasets under unified evaluation protocols show consistent gains for linear probing and fine-tuning without dataset-specific discretization tuning. We further introduce a medical tabular SSL benchmark with standardized protocols to support reproducible progress in this underexplored domain. Our code is available at https://github.com/labhai/Adaptive-Binning.