Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection

TL;DR

This study applies multiple instance learning with deep CNNs to detect large hemorrhages in 3D CT head images, enabling weakly supervised training without slice annotations and achieving high accuracy with sufficient data.

Contribution

It introduces a MIL framework for 3D CT hemorrhage detection that requires only volume-level labels, reducing annotation effort and facilitating clinical report integration.

Findings

Achieved 98.10% true positive rate in hemorrhage detection.

Needed at least 400 volumes for accurate generalization.

Provided open-source code for MIL in CT neuroimaging.

Abstract

Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to contain multiple instances. MIL is gaining traction for learning from weak labels but has not been widely applied to 3D medical imaging. MIL is well-suited to clinical CT acquisitions since (1) the highly anisotropic voxels hinder application of traditional 3D networks and (2) patch-based networks have limited ability to learn whole volume labels. In this work, we apply MIL with a deep convolutional neural network to identify whether clinical CT head image volumes possess one or more large hemorrhages (> 20cm$^3$), resulting in a learned 2D model without the need for 2D slice annotations. Individual image volumes are considered separate bags, and the slices in each volume are instances. Such a framework sets the stage for incorporating information obtained in clinical reports to help train a 2D segmentation approach. Within this context, we evaluate the data requirements to enable generalization of MIL by varying the amount of training data. Our results show that a training size of at least 400 patient image volumes was needed to achieve accurate per-slice hemorrhage detection. Over a five-fold cross-validation, the leading model, which made use of the maximum number of training volumes, had an average true positive rate of 98.10%, an average true negative rate of 99.36%, and an average precision of 0.9698. The models have been made available along with source code to enabled continued exploration and adaption of MIL in CT neuroimaging.