Studying The Effect of MIL Pooling Filters on MIL Tasks

从点估计到分布：用于早产预测的多实例学习中的GMM池化

From Point Estimates to Distributions: GMM Pooling for MIL in Preterm Birth Prediction

提出高斯混合模型池化方法，将患者的多张超声图像特征分布建模为固定长度表示，提升早产预测性能。

从相关性到因果性：基于最大池化的多实例学习导致更稳健的整张滑动图像分类

From Correlation to Causation: Max-Pooling-Based Multi-Instance Learning Leads to More Robust Whole Slide Image Classification

本文提出FocusMIL，通过结合最大池化与变分信息瓶颈，改进多实例学习以更稳健地处理整张滑动图像分类任务。

Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling

Supervised learning tasks such as cancer survival prediction from gigapixel whole slide images (WSIs) are a critical challenge in computational pathology that requires modeling complex features of the tumor microenvironment. These learning tasks are often solved with deep multi-instance learning (MIL) models that do not explicitly capture intratumoral heterogeneity. We develop a novel variance pooling architecture that enables a MIL model to incorporate intratumoral heterogeneity into its predictions. Two interpretability tools based on representative patches are illustrated to probe the biolo…

Attention Awareness Multiple Instance Neural Network

Multiple instance learning is qualified for many pattern recognition tasks with weakly annotated data. The combination of artificial neural network and multiple instance learning offers an end-to-end solution and has been widely utilized. However, challenges remain in two-folds. Firstly, current MIL pooling operators are usually pre-defined and lack flexibility to mine key instances. Secondly, in current solutions, the bag-level representation can be inaccurate or inaccessible. To this end, we propose an attention awareness multiple instance neural network framework in this paper. It consists …

Token Pooling in Vision Transformers

Despite the recent success in many applications, the high computational requirements of vision transformers limit their use in resource-constrained settings. While many existing methods improve the quadratic complexity of attention, in most vision transformers, self-attention is not the major computation bottleneck, e.g., more than 80% of the computation is spent on fully-connected layers. To improve the computational complexity of all layers, we propose a novel token downsampling method, called Token Pooling, efficiently exploiting redundancies in the images and intermediate token representat…

Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning

Convolutional neural networks (CNN) have achieved state of the art performance on both classification and segmentation tasks. Applying CNNs to microscopy images is challenging due to the lack of datasets labeled at the single cell level. We extend the application of CNNs to microscopy image classification and segmentation using multiple instance learning (MIL). We present the adaptive Noisy-AND MIL pooling function, a new MIL operator that is robust to outliers. Combining CNNs with MIL enables training CNNs using full resolution microscopy images with global labels. We base our approach on the…