Multiple instance learning (MIL) is a robust paradigm for whole-slide
pathological image (WSI) analysis, processing gigapixel-resolution images with
slide-level labels. As pioneering efforts, attention-based MIL (ABMIL) and its
variants are increasingly becoming popular due to the characteristics of
simultaneously handling clinical diagnosis and tumor localization. However, the
attention mechanism exhibits limitations in discriminating between instances,
which often misclassifies tissues and potentially impairs MIL performance. This
paper proposes an Attribute-Driven MIL (AttriMIL) framework to address these
issues. Concretely, we dissect the calculation process of ABMIL and present an
attribute scoring mechanism that measures the contribution of each instance to
bag prediction effectively, quantifying instance attributes. Based on attribute
quantification, we develop a spatial attribute constraint and an attribute
ranking constraint to model instance correlations within and across slides,
respectively. These constraints encourage the network to capture the spatial
correlation and semantic similarity of instances, improving the ability of
AttriMIL to distinguish tissue types and identify challenging instances.
Additionally, AttriMIL employs a histopathology adaptive backbone that
maximizes the pre-trained model's feature extraction capability for collecting
pathological features. Extensive experiments on three public benchmarks
demonstrate that our AttriMIL outperforms existing state-of-the-art frameworks
across multiple evaluation metrics. The implementation code is available at
this https URL

提出了一个属性驱动 MIL 框架来解决注意力机制在区分实例上的限制，并通过空间属性约束和属性排序约束改进了模型的识别能力，进而在多个评估指标上优于现有的最先进框架。

重新思考基于注意力的多实例学习在全切片病理图像分类中的应用：一个实例属性的观点

Rethinking Attention-Based Multiple Instance Learning for Whole-Slide  Pathological Image Classification: An Instance Attribute Viewpoint

Pathological image analysis is an important process for detecting
abnormalities such as cancer from cell images. However, since the image size is
generally very large, the cost of providing detailed annotations is high, which
makes it difficult to apply machine learning techniques. One way to improve the
performance of identifying abnormalities while keeping the annotation cost low
is to use only labels for each slide, or to use information from another
dataset that has already been labeled. However, such weak supervisory
information often does not provide sufficient performance. In this paper, we
proposed a new task setting to improve the classification performance of the
target dataset without increasing annotation costs. And to solve this problem,
we propose a pipeline that uses multiple instance learning (MIL) and domain
adaptation (DA) methods. Furthermore, in order to combine the supervisory
information of both methods effectively, we propose a method to create
pseudo-labels with high confidence. We conducted experiments on the
pathological image dataset we created for this study and showed that the
proposed method significantly improves the classification performance compared
to existing methods.

为了在不增加注释成本的情况下提高目标数据集的分类性能，我们提出了一种使用多实例学习和域自适应方法的流程，并提出了一种高置信度的伪标签方法来有效地结合两种方法的监督信息。我们使用为此研究创建的病理图像数据集进行了实验，并证明了所提出的方法相对于现有方法显着提高了分类性能。

面向病理图像例级预测的领域自适应多示例学习

Domain Adaptive Multiple Instance Learning for Instance-level Prediction  of Pathological Images

When applying multi-instance learning (MIL) to make predictions for bags of
instances, the prediction accuracy of an instance often depends on not only the
instance itself but also its context in the corresponding bag. From the
viewpoint of causal inference, such bag contextual prior works as a confounder
and may result in model robustness and interpretability issues. Focusing on
this problem, we propose a novel interventional multi-instance learning (IMIL)
framework to achieve deconfounded instance-level prediction. Unlike traditional
likelihood-based strategies, we design an Expectation-Maximization (EM)
algorithm based on causal intervention, providing a robust instance selection
in the training phase and suppressing the bias caused by the bag contextual
prior. Experiments on pathological image analysis demonstrate that our IMIL
method substantially reduces false positives and outperforms state-of-the-art
MIL methods.

该研究提出了一种新颖的干预型多实例学习（IMIL）框架，并通过因果干预设计期望最大化（EM）算法来实现训练过程中的强大实例选择以及抑制袋子上下文先验引起的偏差。 实验表明，IMIL 方法可以显著减少误报，并优于现有 MIL 方法。