恶性间皮瘤（Malignant Mesothelioma）是一种难以诊断且高度致命的癌症，通常与石棉接触有关。它可以广泛分为三个亚型：上皮型、肉瘤型和具有前两个亚型重要成分的混合型双相亚型。早期诊断和亚型鉴定可以提供治疗信息并有助于改善患者预后。然而，恶性间皮瘤的亚型划分，尤其是从常规组织学切片中识别过渡特征，存在高度的观察者差异性。在本研究中，我们提出了一种端到端的多示例学习（Multiple Instance Learning, MIL）方法来进行恶性间皮瘤亚型划分。该方法采用自适应基于示例的采样方案，在训练深度卷积神经网络时可以学习更广泛的相关实例，相比使用最大值或前N个最值的MIL方法。我们还研究了增强实例表示方法，包括从细胞分割中获得的细胞形态学特征的聚合。所提出的MIL方法使得可以识别出恶性间皮细胞亚型的特定组织区域，从而可以根据肉瘤型和上皮型区域的优势程度来持续刻画样本，从而避免了当前使用的亚型分类方法的任意性和高度主观性。实例评分还可以用于研究肿瘤的异质性并识别与不同亚型相关的模式。我们在一个由234个组织微阵列核心数据组成的数据集上评估了所提出的方法，并获得了该任务的0.89±0.05的AUROC。该数据集和开发的方法已经可供社区使用，网址为：https://github.com/measty/PINS。版权所有 © 2023 作者。由Elsevier B.V.出版，保留所有权利。

Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of 0.89±0.05 for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS.Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.