Detection of Parasitic Eggs from Microscopy Images and the emergence of a new dataset
This work addresses the problem of reducing workload and improving objectivity for medical experts in parasitology, but it is incremental as it adapts existing deep learning methods to a new domain.
The paper tackled the problem of automatically detecting parasitic eggs in microscopy images to improve diagnostic efficiency, achieving encouraging results with a framework combining GANs for image enhancement and Faster-RCNN for detection, though detection remains challenging for some egg types, and a new public dataset was created.
Automatic detection of parasitic eggs in microscopy images has the potential to increase the efficiency of human experts whilst also providing an objective assessment. The time saved by such a process would both help ensure a prompt treatment to patients, and off-load excessive work from experts' shoulders. Advances in deep learning inspired us to exploit successful architectures for detection, adapting them to tackle a different domain. We propose a framework that exploits two such state-of-the-art models. Specifically, we demonstrate results produced by both a Generative Adversarial Network (GAN) and Faster-RCNN, for image enhancement and object detection respectively, on microscopy images of varying quality. The use of these techniques yields encouraging results, though further improvements are still needed for certain egg types whose detection still proves challenging. As a result, a new dataset has been created and made publicly available, providing an even wider range of classes and variability.