Automatic plankton quantification using deep features

The study of marine plankton data is vital to monitor the health of the world’s oceans. In recent decades, automatic plankton recognition systems have proved useful to address the vast amount of data collected by specially engineered in situ digital imaging systems. At the beginning, these systems were developed and put into operation using traditional automatic classification techniques, which were fed with handdesigned local image descriptors (such as Fourier features), obtaining quite successful results. In the past few years, there have been many advances in the computer vision community with the rebirth of neural networks. In this paper, we leverage how descriptors computed using Convolutional Neural Networks (CNNs) trained with out-of-domain data are useful to replace hand-designed descriptors in the task of estimating the prevalence of each plankton class in a water sample. To achieve this goal, we have designed a broad set of experiments that show how effective these deep features are when working in combination with state-of-the-art quantification algorithms

Deep learning-based diatom taxonomy on virtual slides

Kloster M, Langenkämper D, Zurowietz M, Beszteri B, Nattkemper TW. Deep learning-based diatom taxonomy on virtual slides. Scientific Reports. 2020;10(1): 14416

A survey on generative adversarial networks for imbalance problems in computer vision tasks

Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Neural Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets. In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms

Characterization of Acantharea-Phaeocystis photosymbioses: distribution, abundance, specificity, maintenance and host-control

Microbial eukaryotes (protists) are important contributors to marine biogeochemistry and play essential roles as both producers and consumers in marine ecosystems. Among protists, mixotrophs—those that use both heterotrophy and autotrophy to meet their energy requirements—are especially important to primary production in low-nutrient regions. Acantharian protists (clades E & F) accomplish mixotrophy by hosting ​Phaeocystis spp.​ as algal endosymbionts and are extremely abundant in subtropical low-nutrient regions where they form productivity hotspots. Despite their ecological importance, acantharians remain understudied due to their structural fragility and inability to survive in culture. In order to overcome these challenges and illuminate key aspects of acantharian biology and ecology—including distribution, abundance, and specificity and specialization of symbioses—single-cell RNA sequencing methods were developed for acantharians and used alongside environmental metabarcode sequencing and high-throughput, in-situ imaging. Major findings from this thesis were that i) acantharian cell (> 250 µm) concentrations decrease with depth, which correlates to patterns in relative sequence abundances for acantharian clades with known morphologies but not for those lacking known morphology, and that ii) while individual acantharians simultaneously harbor multiple symbiont species, intra-host symbiont communities do not match environmental communities, providing evidence for multiple uptake events but against continuous symbiont turnover, and that iii) photosynthesis genes are upregulated in symbiotic Phaeocystis​, reflecting enhanced productivity in symbiosis, but DNA replication and cell-cycle genes are downregulated, demonstrating that hosts suppress symbiont cell division. Moreover, storage carbohydrate and lipid biosynthesis and metabolism genes are downregulated in symbiotic ​Phaeocystis​, suggesting fixed carbon is relinquished to acantharian hosts. Gene expression patterns indicate that symbiotic ​Phaeocystis​ is not nutrient limited and likely benefits from host-supplied ammonium and urea, thus providing evidence for nutrient transfer between hosts and symbionts. Interestingly, genes associated with protein kinase signaling pathways that promote cell proliferation are downregulated in symbiotic ​Phaeocystis​. Deactivation of these genes may prevent symbionts from overgrowing hosts and therefore represents a key component of maintaining the symbiosis. This research contributes new insights into the ecologically relevant photosymbioses between Acantharea and ​Phaeocystis​ and illustrates the benefits of combining single-cell sequencing and imaging technologies to illuminate important microbial relationships in marine ecosystems.Okinawa Institute of Science and Technology Graduate Universit