Uncertainty-guided attention learning for malaria parasite detection in thick blood smears.

Abstract

Malaria may seriously threaten an individual's health and wellbeing, and early screening is pivotal for timely treatment and recovery. In malaria screening, thick blood smears are exploited to count the parasites and assess the severity of the disease. Parasites are tiny objects that can be found in high resolution blood smear images, which renders them difficult for detection. Other than using object detection based methods, prior works also applied image classification techniques to this problem. They first extracted image patches from blood smears as parasite candidates and then utilized convolutional neural networks to classify these patches as parasites or non-parasites. However, these approaches overlook the fact that the blood smear images may contain noises, errors, and background artifacts, which introduces uncertainty and makes the model predictions less stable. In this work, we propose an uncertainty-guided attention learning based network for malaria parasite detection from thick blood smears, which incorporates pixel attention mechanism to identify more fine-grained and pixel-wise informative features, to improve the classification capability of our model. We further put uncertainty estimation on channels of the feature map to guide pixel attention learning, such that the features from channels with higher uncertainty are considered unreliable and are thus restrictively exploited by pixel attention learning. To estimate channel-wise uncertainty, we introduce the Bayesian channel attention, which reformulates the traditional channel attention under the Bayesian framework. As a result, it denotes channel uncertainties with estimated variances that guide the pixel attention learning. We compared to several state-of-the-art baselines on two public datasets using parasite-level and patient-level evaluations. The proposed method demonstrates superior performance with respect to most metrics on two datasets, especially achieving highest average precision (AP) scores in both parasite and patient-level scenarios.