---
title: "基于注意力机制的多实例学习：将癌症登记院的病历标签转化为高质量训练数据"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.03481v1"
permalink: "https://feed.astralor.com/items/itm_6ed08d3fdfbcb474cc13d122"
published: "2026-07-03T16:48:06.000Z"
score: 77
channel: "tech"
tags: ["Attention-Based Multiple Instance Learning","BC Cancer Registry","弱监督学习","深度学习","病理报告自动编码"]
---

# 基于注意力机制的多实例学习：将癌症登记院的病历标签转化为高质量训练数据

## AI 点评

> 这篇论文让我眼前一亮，它巧妙地避开了医疗AI常见的“数据标注贵”陷阱，转而利用医院已经有的粗标签。对于任何想用AI降低运营成本但又缺乏标注资源的机构来说，这个框架简直是个宝藏。尤其值得病理科、癌症登记中心以及做弱监督学习的研究者关注。

## 速览

癌症登记现代化中，病理报告编码的人力密集任务可通过深度学习自动化，但缺乏报告级人工标注数据。加拿大BC癌症登记院利用患者级操作标签，采用注意力机制的多实例学习（ABMIL）恢复标签与报告的关联，从嘈杂标签中蒸馏出高质量训练集，使分类器宏F1达0.83，优于基线方法。该框架无需额外标注或大规模算力，为癌症登记工作流自动化提供了实用路径。

## 关键点

- 提出ABMIL方法，利用癌症登记院已有的患者级操作标签，无需报告级人工标注。
- 通过注意力机制自动选择最具信息的病理报告，蒸馏出高质量训练数据集。
- 在BC癌症登记院的肿瘤分类任务中，宏F1达0.83，超越多种基线。
- 框架计算成本低，易于推广，减轻病理编码人力负担。

## 全文

Modernizing cancer registries with deep learning is opening new opportunities to automate labor-intensive tasks such as the coding of pathology reports. However, progress is constrained by the scarcity of report-level human-annotated training data. Cancer registries generate substantial volumes of expert-assigned labels as a routine product of their operations, but these exist at the patient level and are not linked to the individual pathology reports that informed them, limiting their direct use for training models. We develop an efficient framework for training deep learning classifiers by leveraging these operationally-generated labels without requiring per-report human annotation, demonstrated for tumor group classification at the BC Cancer Registry. We use Attention-Based Multiple Instance Learning (ABMIL) to recover the lost link between patient-level labels and the reports that informed them, leveraging the attention the model places on each report to distil a large, noisily-labeled corpus into a compact, high-quality per-report training dataset. A classifier fine-tuned on a distilled dataset achieved a macro F1 of 0.83, outperforming established baselines across most tumor groups. By turning routine operational labels into high-quality training data without additional annotation or large-scale computing infrastructure, ABMIL offers a practical and accessible route to automating cancer registry workflows.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_6ed08d3fdfbcb474cc13d122