Open Access
2- https://doi.org/10.1109/icip51287.2024.10647834
Codamal: Contrastive Domain Adaptation for Malaria Detection in Low-Cost Microscopes
Codamal introduces an end-to-end contrastive domain adaptation framework that enhances malaria detection in low-cost microscopes by reducing domain shift between high- and low-cost microscope images. It achieves a 16% mAP improvement, 21× faster inference, and halves the model size on the M5 dataset, without additional annotation burdens.
Translate Article 
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-ofthe-art methods in terms of the mean average precision metric (mAP), provides 21× 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/.
- Conference Article
- 18
- 10.1109/cvpr52688.2022.02003
- Jun 1, 2022
Malaria, a fatal but curable disease claims hundreds of thousands of lives every year. Early and correct diagnosis is vital to avoid health complexities, however, it depends upon the availability of costly microscopes and trained experts to analyze blood-smear slides. Deep learning-based methods have the potential to not only decrease the burden of experts but also improve diagnostic accuracy on low-cost microscopes. However, this is hampered by the absence of a reasonable size dataset. One of the most challenging aspects is the reluctance of the experts to annotate the dataset at low magnification on low-cost microscopes. We present a dataset to further the research on malaria microscopy over low-cost microscopes at low magnification. Our large-scale dataset consists of images of blood-smear slides from several malaria-infected patients, collected through micro-scopes at two different cost spectrums and multiple magnifications. Malarial cells are annotated for the localization and life-stage classification task on the images collected through the high-cost microscope at high magnification. We design a mechanism to transfer these annotations from the high-cost microscope at high magnification to the low-cost microscope, at multiple magnifications. Multiple object detectors and domain adaptation methods are presented as the baselines. Furthermore, a partially supervised domain adaptation method is introduced to adapt the object-detector to work on the images collected from the low-cost microscope. The dataset is available here: http://im.itu.edu.pk/m5-malaria-dataset/
- Research Article
- 1
- 10.33140/jsndc.04.03.09
- Dec 30, 2024
- Journal of Sensor Networks and Data Communications
Malaria remains a significant global health burden, particularly in resource-limited regions where timely and accurate diagnosis is critical to effective treatment and control. Deep Learning (DL) has emerged as a transformative tool for automating malaria detection and it offers high accuracy and scalability. However, the effectiveness of these models is constrained by challenges in data quality and model generalization including imbalanced datasets, limited diversity and annotation variability. These issues reduce diagnostic reliability and hinder real-world applicability. This article provides a comprehensive analysis of these challenges and their implications for malaria detection performance. Key findings highlight the impact of data imbalances which can lead to a 20% drop in F1-score and regional biases which significantly hinder model generalization. Proposed solutions, such as GAN-based augmentation, improved accuracy by 15-20% by generating synthetic data to balance classes and enhance dataset diversity. Domain adaptation techniques, including transfer learning, further improved cross-domain robustness by up to 25% in sensitivity. Additionally, the development of diverse global datasets and collaborative data-sharing frameworks is emphasized as a cornerstone for equitable and reliable malaria diagnostics. The role of explainable AI techniques in improving clinical adoption and trustworthiness is also underscored. By addressing these challenges, this work advances the field of AI-driven malaria detection and provides actionable insights for researchers and practitioners. The proposed solutions aim to support the development of accessible and accurate diagnostic tools, particularly for resource-constrained populations
