Xiong Hao, Wang Zhiyong, Sharan Roneel V, Berkovsky Shlomo
Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, 2109, NSW, Australia.
School of Computer Science, The University of Sydney, Sydney, 2006, NSW, Australia.
Neural Netw. 2025 Nov;191:107833. doi: 10.1016/j.neunet.2025.107833. Epub 2025 Jul 8.
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.
疟疾可能会严重威胁个人的健康和福祉，早期筛查对于及时治疗和康复至关重要。在疟疾筛查中，厚血涂片被用于计数寄生虫并评估疾病的严重程度。寄生虫是微小的物体，可在高分辨率的血涂片图像中找到，这使得它们难以检测。除了使用基于目标检测的方法外，先前的工作也将图像分类技术应用于这个问题。他们首先从血涂片中提取图像块作为寄生虫候选物，然后利用卷积神经网络将这些块分类为寄生虫或非寄生虫。然而，这些方法忽略了血涂片图像可能包含噪声、误差和背景伪影这一事实，这引入了不确定性并使模型预测变得不稳定。在这项工作中，我们提出了一种基于不确定性引导注意力学习的网络，用于从厚血涂片中检测疟疾寄生虫，该网络结合了像素注意力机制来识别更细粒度和逐像素的信息特征，以提高我们模型的分类能力。我们进一步在特征图的通道上进行不确定性估计，以指导像素注意力学习，使得来自具有较高不确定性通道的特征被认为不可靠，因此在像素注意力学习中受到限制利用。为了估计通道级的不确定性，我们引入了贝叶斯通道注意力，它在贝叶斯框架下重新构建了传统的通道注意力。结果，它用估计的方差表示通道不确定性，以指导像素注意力学习。我们在两个公共数据集上使用寄生虫级和患者级评估与几个最先进的基线进行了比较。所提出的方法在两个数据集上的大多数指标方面都表现出卓越的性能，特别是在寄生虫和患者级场景中均取得了最高的平均精度（AP）分数。
