Gabriel Cristóbal

Cyanobacteria play a fundamental role in aquatic ecosystems, contributing to global biogeochemical cycles and serving as indicators of environmental change. Their classification is critical for monitoring water quality, detecting harmful algal blooms and understanding ecosystem dynamics. However, accurate identification remains a major challenge due to their vast taxonomic diversity and significant morphological similarities. Visual inspection alone is often insufficient, highlighting the need for computational approaches to enhance classification accuracy. In this study, we present a multimodal deep learning model that combines convolutional neural networks (CNNs) for image-based feature extraction with bidirectional transformers for text embedding. These complementary features are fused via concatenation to improve species-level classification. To our knowledge, this is the first application of a multimodal neural architecture integrating CNNs and bidirectional transformers for cyanobacteria classification. We evaluated five CNN backbones of varying depth, resulting in eight model configurations. Performance is benchmarked against unimodal CNN models that rely solely on image data. The model is trained and validated on a dataset of 1660 microscopic images and corresponding textual descriptions, covering nine cyanobacterial genera across three taxonomic orders. Results demonstrate the potential of multimodal deep learning to improve classification performance, supporting the development of scalable and accurate identification tools in microbiology and environmental monitoring.

Environmental microscopy is crucial for analyzing microorganisms, but traditional optical microscopes are often expensive, bulky, and impractical for field use. AI-driven image recognition, powered by deep learning models like YOLO, enhances microscopy analysis but typically requires high computational resources. To address these challenges, we present two cost-effective pipelines integrating AI with low-cost microscopes and edge computing. Both approaches use the OpenFlexure Microscope and Raspberry Pi devices. The first performs real-time inference with a Raspberry Pi 5 and Hailo-8L accelerator, while the second captures images with a Raspberry Pi 4, transferring them to a GPU-equipped desktop for processing. Using YOLOv8, we evaluate their ability to detect phytoplankton species, including cyanobacteria and diatoms. Results show that edge computing enables accurate, efficient, and low-power microscopy analysis, demonstrating its potential for real-time environmental monitoring in resource-limited settings.

MICROSCOM is an Erasmus Mundus design measure project awarded by the European Commission to establish a two-year International Master’s programme in Advanced Microscopy with Artificial Intelligence (MICROSCOM), what is called Erasmus Mundus Joint Master (EMJM). The EMJM MICROSCOM programme will be the first of its kind to comprehensively address all aspects of the computational microscopy field, including a thorough understanding of microscopy principles, methods, and techniques, as well as software and hardware development and integration. It will also cover the incorporation of artificial intelligence (AI) techniques and edge computing, along with practical projects focused on real-world problems. The EMJM will be administered by a consortium of Higher Education Institutions (HEIs) from Ireland, Spain and the United Kingdom, in conjunction with international partners from academia and industry who possess specialized expertise to oversee MSc thesis.

Market-available automated microscopy systems are often unaffordable for research institutions, particularly in economically disadvantaged countries, limiting their access to advanced technologies. This work addresses this challenge by developing a cost-effective virtual microscopy and telemicroscopy system, aimed at creating a remote-controlled microscopy setup for analyzing digital samples with performance comparable to high-end equipment but at a reduced cost. The system includes a web platform for telemicroscopy, enabling remote control of the robotic stage and real-time viewing of the microscope camera. Additionally, a decision support system has been implemented, integrating AI-based models for identifying and classifying objects of interest in three histopathology use cases for breast cancer analysis: i) identification of tumor cells in H&E samples; ii) grading of Her2 samples; and iii) evaluation of Ki67 samples. These models enhance diagnostic capabilities, increasing productivity for experts and reducing manual workload. Sample virtualization and automatic processing simplify tasks for professionals, allowing remote participation in concurrent work sessions and streamlining processes for digital samples.

Obtaining high-quality images for training AI models in the field of plankton identification, particularly cyanobacteria, is a challenging and time-critical task that necessitates the expertise of biologists. Data augmentation techniques, including conventional methods and GANs, can improve model performance, but GANs typically require large training datasets to produce high-quality results. To tackle this issue, we employed the StyleGAN2ADA model on a dataset of 9 cyanobacteria genera plus non-cyanobacterial microalgae. We evaluated the generated images using both qualitative and quantitative metrics. Qualitative assessments involved a psychophysical test conducted by three expert biologists to identify shape and texture deviations or chromatic aberration that might impede visual classification. Additionally, three non-reference image quality metrics based on perceptual features were used for quantitative assessment. Images meeting quality standards will be incorporated into classification models to assess the performance improvement compared to the original dataset. This comprehensive evaluation process ensured the suitability of generated images for enhancing model performance.

A novel ImageJ plugin is designed to extend the depth-of-field (DoF) by seamlessly fusing a series of multi-focus images, allowing for in-depth analysis. Moreover, it has been tested on multi-exposure image stacks, demonstrating its adeptness in preserving intricate details within both poorly and brightly illuminated regions of 3-D specimens. The significance of this capability becomes particularly apparent when dealing with images that exhibit a limited DoF and varying exposure settings under low signal-to-noise ratio conditions. The plugin’s effectiveness has been thoroughly validated through the processing and analysis of numerous image stacks featuring diverse diatom and cyanobacteria species. The proposed methodology incorporates a two-scale decomposition (TSD) scheme, complemented by the refinement of weight maps using edge-preserving filtering (EPF). This dual approach ensures the preservation of fine details in the fused image while simultaneously minimizing noise. Such innovations make this plugin a valuable tool for researchers and analysts working with complex image datasets.

This article presents a review after an exhaustive search that yielded 23 works carried out in the last decade for the availability of optical microscopes with open hardware as a low‐cost alternative to commercial systems. These works were developed with the aim of covering needs within several areas such as: Bio Sciences research in institutions with limited resources, diagnosis of diseases and health screenings in large populations in developing countries, and training in educational contexts with a need for high availability of equipment and low replacement cost. The analysis of the selected works allows us to classify the analyzed solutions into two main categories, for which their essential characteristics are enumerated: portable field microscopes and multipurpose automated microscopes. Moreover, this work includes a discussion on the degree of maturity of the solutions in terms of the adoption of practices aligned with the development of Open Science.Research Highlights: Concise review on low‐cost microscopes for developing Open Science, exposing the role of smartphone‐based microscopy. The work classifies microscopes in two main categories: (1) portable field microscopes, and (2) multipurpose automated microscopes.