CodaMal：用于低成本显微镜疟疾检测的对比域自适应
CodaMal: Contrastive Domain Adaptation for Malaria Detection in Low-Cost Microscopes
AI总结：
提出端到端框架CodaMal，通过域自适应对比损失和精心设计的数据增强，解决低成本显微镜疟疾检测的跨域泛化问题，在M5-dataset上mAP提升16%，推理速度提升21倍且参数量减半。
摘要：
疟疾是全球重大健康问题，其诊断需要能在低成本显微镜（LCM）中有效工作的可扩展解决方案。基于深度学习的方法在显微图像的计算机辅助诊断中取得了成功。然而，这些方法需要显示受疟原虫影响的细胞及其生命阶段的标注图像。与标注高成本显微镜（HCM）图像相比，标注LCM图像显著增加了医学专家的负担。因此，一种实用的解决方案是在HCM图像上进行训练，并在测试时在LCM图像上具有良好的泛化能力。虽然早期的方法采用了多阶段学习过程，但它们没有提供端到端的方法。在这项工作中，我们提出了一个端到端学习框架，名为CodaMal（用于疟疾的对比域自适应）。为了弥合HCM（训练）和LCM（测试）之间的差距，我们提出了一种域自适应对比损失。它通过促进HCM及其对应LCM图像表示之间的相似性来减少域偏移，而不会增加额外的标注负担。此外，训练目标包括带有精心设计的数据增强的目标检测目标，确保准确检测疟原虫。在公开的大规模M5-dataset上，我们提出的方法在平均精度均值指标方面比现有技术方法显著提高了16%，在推理过程中提供了21倍的速度提升，并且仅需要以前方法使用的一半可学习参数。我们的代码已公开：https://daveishan.github.io/codamal-webpage/。
英文摘要：
Malaria is a major health issue worldwide, and its diagnosis requires scalable solutions that can work effectively with low-cost microscopes (LCM). Deep learning-based methods have shown success in computer-aided diagnosis from microscopic images. However, these methods need annotated images that show cells affected by malaria parasites and their life stages. Annotating images from LCM significantly increases the burden on medical experts compared to annotating images from high-cost microscopes (HCM). For this reason, a practical solution would be trained on HCM images which should generalize well on LCM images during testing. While earlier methods adopted a multi-stage learning process, they did not offer an end-to-end approach. In this work, we present an end-to-end learning framework, named CodaMal (COntrastive Domain Adpation for MALaria). In order to bridge the gap between HCM (training) and LCM (testing), we propose a domain adaptive contrastive loss. It reduces the domain shift by promoting similarity between the representations of HCM and its corresponding LCM image, without imposing an additional annotation burden. In addition, the training objective includes object detection objectives with carefully designed augmentations, ensuring the accurate detection of malaria parasites. On the publicly available large-scale M5-dataset, our proposed method shows a significant improvement of 16% over the state-of-the-art methods in terms of the mean average precision metric (mAP), provides 21x speed improvement during inference and requires only half of the learnable parameters used in prior methods. Our code is publicly available: https://daveishan.github.io/codamal-webpage/.
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