The rapidly emerging field of computational pathology has the potential to
enable objective diagnosis, therapeutic response prediction and identification
of new morphological features of clinical relevance. However, deep
learning-based computational pathology approaches either require manual
annotation of gigapixel whole slide images (WSIs) in fully-supervised settings
or thousands of WSIs with slide-level labels in a weakly-supervised setting.
Moreover, whole slide level computational pathology methods also suffer from
domain adaptation and interpretability issues. These challenges have prevented
the broad adaptation of computational pathology for clinical and research
purposes. Here we present CLAM - Clustering-constrained attention multiple
instance learning, an easy-to-use, high-throughput, and interpretable WSI-level
processing and learning method that only requires slide-level labels while
being data efficient, adaptable and capable of handling multi-class subtyping
problems. CLAM is a deep-learning-based weakly-supervised method that uses
attention-based learning to automatically identify sub-regions of high
diagnostic value in order to accurately classify the whole slide, while also
utilizing instance-level clustering over the representative regions identified
to constrain and refine the feature space. In three separate analyses, we
demonstrate the data efficiency and adaptability of CLAM and its superior
performance over standard weakly-supervised classification. We demonstrate that
CLAM models are interpretable and can be used to identify well-known and new
morphological features. We further show that models trained using CLAM are
adaptable to independent test cohorts, cell phone microscopy images, and
biopsies. CLAM is a general-purpose and adaptable method that can be used for a
variety of different computational pathology tasks in both clinical and
research settings.

CLAM 是一种易于使用、高吞吐量和可解释的基于深度学习的弱监督学习方法，它可以在只需要幻灯片级别标签的情况下处理大规模组织切片图像，并且可以用于多种不同的计算病理学任务。

全切片影像上数据有效和弱监督计算病理学

Data Efficient and Weakly Supervised Computational Pathology on Whole  Slide Images

Attention-based learning for fine-grained image recognition remains a
challenging task, where most of the existing methods treat each object part in
isolation, while neglecting the correlations among them. In addition, the
multi-stage or multi-scale mechanisms involved make the existing methods less
efficient and hard to be trained end-to-end. In this paper, we propose a novel
attention-based convolutional neural network (CNN) which regulates multiple
object parts among different input images. Our method first learns multiple
attention region features of each input image through the one-squeeze
multi-excitation (OSME) module, and then apply the multi-attention multi-class
constraint (MAMC) in a metric learning framework. For each anchor feature, the
MAMC functions by pulling same-attention same-class features closer, while
pushing different-attention or different-class features away. Our method can be
easily trained end-to-end, and is highly efficient which requires only one
training stage. Moreover, we introduce Dogs-in-the-Wild, a comprehensive dog
species dataset that surpasses similar existing datasets by category coverage,
data volume and annotation quality. This dataset will be released upon
acceptance to facilitate the research of fine-grained image recognition.
Extensive experiments are conducted to show the substantial improvements of our
method on four benchmark datasets.

本文提出了一种基于注意力机制的卷积神经网络方法，使用一握多激励模块学习每个输入图像的多个注意区域特征，并在度量学习框架中应用多注意力多类约束，通过推拉相同 / 不同注意力类别的特征以实现多个目标区域的协调，并引入 Dogs-in-the-Wild 数据集以促进细粒度图像识别研究。