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.

Malaria is an infectious disease claiming more than half a million lives each year. Because traditional diagnosis takes expertise and the workload is high, an international team of researchers investigated if diagnosis using a new system combining an automatic scanning microscope and AI is feasible in clinical settings. They found that the system identified malaria parasites almost as accurately as experts staffing microscopes used in standard diagnostic procedures. This may help reduce the burden on microscopists and increase the feasible patient load. Each year, more than 200 million people fall sick with malaria and more than half a million of these infections lead to death. The World Health Organization recommends parasite-based diagnosis before starting treatment for the disease caused by Plasmodium parasites.

We introduce a hierarchical Bayesian model for the discovery of putative regulators from gene expression data only. The hierarchy incorporates the knowledge that there are just a few regulators that by themselves only regulate a handful of genes. This is implemented through a so-called spike-and-slab prior, a mixture of Gaussians with different widths, with mixing weights from a hierarchical Bernoulli model. For efficient inference we implemented expectation propagation. Running the model on a malaria parasite data set, we found four genes with significant homology to transcription factors in an amoebe, one RNA regulator and three genes of unknown function (out of the top ten genes considered).

Tens of thousands of times a year, a technician places a drop of blood on a slide and peers at it under a microscope, searching for malaria parasites. Making a definitive diagnosis requires the technician to look at up to 300 different fields of view over roughly half an hour. This process is repeated over and over, day after day, on every continent except Antarctica. It's tedious work, but it saves lives. Malaria parasites infect over 200 million people and kill 400,000 every year, mostly children in Africa. Trained and experienced malaria microscopists are rare, however.

We introduce a hierarchical Bayesian model for the discovery of putative regulators from gene expression data only. The hierarchy incorporates the knowledge that there are just a few regulators that by themselves only regulate a handful of genes. This is implemented through a so-called spike-and-slab prior, a mixture of Gaussians with different widths, with mixing weights from a hierarchical Bernoulli model. For efficient inference we implemented expectation propagation. Running the model on a malaria parasite data set, we found four genes with significant homology to transcription factors in an amoebe, one RNA regulator and three genes of unknown function (out of the top ten genes considered).

Engineers at Duke University have developed a microscope that adapts its lighting angles, colors and patterns while teaching itself the optimal settings needed to complete a given diagnostic task. In the initial proof-of-concept study, the microscope simultaneously developed a lighting pattern and classification system that allowed it to quickly identify red blood cells infected by the malaria parasite more accurately than trained physicians and other machine learning approaches. The results appear online on November 19 in the journal Biomedical Optics Express. "A standard microscope illuminates a sample with the same amount of light coming from all directions, and that lighting has been optimized for human eyes over hundreds of years," said Roarke Horstmeyer, assistant professor of biomedical engineering at Duke. "But computers can see things humans can't," Hortmeyer said. "So not only have we redesigned the hardware to provide a diverse range of lighting options, we've allowed the microscope to optimize the illumination for itself."

Malaria is a female anopheles mosquito-bite inflicted life-threatening disease which is considered endemic in many parts of the world. This article focuses on improving malaria detection from patches segmented from microscopic images of red blood cell smears by introducing a deep convolutional neural network. Compared to the traditional methods that use tedious hand engineering feature extraction, the proposed method uses deep learning in an end-to-end arrangement that performs both feature extraction and classification directly from the raw segmented patches of the red blood smears. The dataset used in this study was taken from National Institute of Health named NIH Malaria Dataset. The evaluation metric accuracy and loss along with 5-fold cross validation was used to compare and select the best performing architecture. To maximize the performance, existing standard pre-processing techniques from the literature has also been experimented. In addition, several other complex architectures have been implemented and tested to pick the best performing model. A holdout test has also been conducted to verify how well the proposed model generalizes on unseen data. Our best model achieves an accuracy of almost 97.77%.

IMAGE: InSilico study reveals how E64 approaches, binds to, and inhibits falcipain-2 of Plasmodium falciparum that causes malaria in humans. Monday, November 12, 2018, Taipei, Taiwan, Republic of China - Insilico Taiwan, a Taipei-based subsidiary of Insilico Medicine, developing the end-to-end drug discovery pipeline utilizing the next generation artificial intelligence, announces the publication of a new research paper titled "In Silico Study Reveals How E64 Approaches, Binds to, and Inhibits Falcipain-2 of Plasmodium falciparum that Causes Malaria in Humans" in Scientific Reports - a scientific journal published by the Nature Publishing Group. Malaria is one of the world's oldest infectious diseases that still causes a lot of health problems in many tropical countries. Plasmodium falciparum, the most dangerous human malaria parasite, is believed to cause hundreds of millions of illnesses and about half a million deaths a year. Inhibitors of FP2 block haemoglobin destruction and parasite development, suggesting that FP2 inhibition is a promising target for antimalarial therapy.

A laboratory robot powered by artificial intelligence (AI) has discovered that a compound commonly found in toothpaste could be used to combat drug-resistant malaria parasites. Triclosan could be deployed against strains of plasmodium malaria parasites that have evolved resistance to the widely used drug pyrimethamine, according to the University of Cambridge.

A laboratory robot powered by artificial intelligence (AI) has discovered that a compound commonly found in toothpaste could be used to combat drug-resistant malaria parasites. Triclosan could be deployed against strains of plasmodium malaria parasites that have evolved resistance to the widely used drug pyrimethamine, according to the University of Cambridge. Pyrimethamine works by inhibiting a particular enzyme called DHFR and scientists have known for some time that triclosan can be employed to target another enzyme, ENR. The fast-moving AI routines of the robot "Eve", however, which formulate, test and re-evaluate hypotheses in quick succession, discovered that the common toothpaste chemical also attacks DHFR – even in parasites resistant to pyrimethamine. It has led researchers to hope that triclosan could be developed for use in a two-pronged attack on plasmodium in the liver and in the blood.