  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 up
during inference, and requires only half learnable parameters than the prior
methods. Our code is publicly available.
