Deep Block

Check out the latest newsletter on AI for large area microscopy and earth observation! Explore artificial intelligence technologies in large-area microscopy, including applications in hematology, ceramic engineering. Experience the power of advanced large-scale image analysis software — now available to try for free! #AI #Microscopy #EarthObservation #Hematology #CeramicEngineering #ComputerVision #ComputationalGeometry #DeepTechSolutions #artificialintelligence #deeplearning #aitools #aitool #aiplatform #largeareaprocessing #nocode #imagesegmentation #materialengineering

https://lnkd.in/gWBNXmfP In this article, we will explore the causes of degradation in MLCCs (Multilayer Ceramic Capacitors), the consequences of such degradation, and the factors contributing to the occurrence of defects. Additionally, we will delve into the challenges associated with analyzing MLCC defects and the complexities of maintaining quality in manufacturing processes. Furthermore, we will highlight how emerging technologies can transform the landscape of defect analysis and process research. Specifically, we will examine the potential of Deep Block, a cutting-edge tool available at deepblock.net, and its immense value in automating the detection and analysis of defects, ultimately streamlining the research and production workflows. Modern MLCCs, with their highly miniaturized and densely layered structures, are essential components in numerous electronic devices. However, this complexity introduces significant challenges. The occurrence of defects, such as pores and discontinuities, is increasingly likely as the layers become thinner and the number of stacked layers often exceeds 600 to 700. Identifying and analyzing these defects require substantial expertise, effort, and time, making it a labor-intensive task for engineers and material scientists. To address these issues, automation through AI-powered tools and computational geometry technologies has emerged as a game-changer. Deep Block, for example, provides an intuitive platform to detect, localize, and classify defects within MLCCs, reducing manual intervention and drastically enhancing efficiency. The integration of such tools not only accelerates defect analysis but also supports improved manufacturing yields and product reliability. By the end of this discussion, we aim to provide a comprehensive understanding of MLCC degradation, the hurdles in defect analysis, and the transformative role that Deep Block and similar technologies can play in advancing the field. For more information about Deep Block and its applications, visit www.deepblock.net/contact #mlcc #materialscience #microscopy #materialengineering #electronics #dielectric #failureanalysis #ceramic #capacitor #electronics #computervision #nocode #ai #deeplearning #machinelearning #sintering #sem #failureanalysis #ml #cv #largeareamicroscopy #artificialintelligence #aitool #aitools #aiplatform #micrograph #wemakeaieasy

https://lnkd.in/gJ4fgZ9v Measuring the roughness of 2D polygons is a well-established problem in computational geometry, with diverse applications ranging from material sciences to laboratory medicine. Check out our previous article to learn more about the importance of polygon shape analysis. https://lnkd.in/gArbe4Bg In this article, we will learn the roughness evaluation methods for 2D polygons, leveraging computational geometry principles to offer insights into their practical use. #microscopy #computationalgeometry #deeplearning #computervision #mlcc #ceramic #thinfilm #ai #artificialintelligence #computergraphics #nocode #aitool #aitools #aiplatform #micrograph #electronics #failureanalysis #morphologicalanalysis #hematology #leukemia #oncology #internalmedicine #semanticsegmentation #imagesegmentation #ml #machinelearning

www.deepblock.net, a no-code AI model development platform, is offering a free COCOJSON annotation tool. Unlike traditional tools, this product supports large-scale images, making it ideal for high-resolution microscopy and research applications. For instance, consider an image captured using slide scanner—a single blood smear slide can be as large as 16GB. Rendering such massive images and annotating them with a free online annotation tool has been impossible—until Deep Block made it possible. Building a dataset for supervised machine learning is the first step toward automating your microscopy research or large-area micrograph analysis. However, conventional solutions often fall short, requiring researchers to write custom scripts to split these large images into smaller parts for annotation. With Deepblock.net, there's no need for complex programming. Simply upload your slide image file, annotate directly within the platform, and export the annotations in COCO JSON format, ready to train your AI models. Experience this powerful annotation tool designed for large-scale microscopy images today. Empower your research with ease and precision! Visit deepblock.net now! #microscopy #remotesensing #pathology #cellbiology #artificialintelligence #computervision #digitalpathology #ai #deeplearning #ml #oncology #digitalhealth #digitalhealthcare #medicine #largeareamicroscopy #artificialintelligence #nocode #aitool #aitools #aiplatform #micrograph #machinelearning #wemakeaieasy

https://lnkd.in/gArbe4Bg Discover the critical role of nickel (Ni) ligament structures in enhancing the performance and reliability of multi-layer ceramic capacitors (MLCCs). Our latest article explores how electrode designs affect the quality of MLCC products. By overcoming challenges like roughness, discontinuities, and interfacial liquid layers during manufacturing, MLCCs achieve greater efficiency and durability, even in high-frequency or high-temperature environments. Explore how AI technology and microscopy can be utilized in the MLCC manufacturing process, process evaluation, and defect analysis through deepblock.net. No-code AI tool for automating large-area microscope image analysis - deepblock.net #mlcc #materialscience #microscopy #materialengineering #ceramic #capacitor #electronics #computervision #nocode #ai #deeplearning #machinelearning #sintering #sem #failureanalysis #ml #cv #largeareamicroscopy #artificialintelligence #aitool #aitools #aiplatform #deepblock #micrograph #wemakeaieasy

https://lnkd.in/gxkKBVvn If a blood smear slide shows a high number of smudge cells, it can be a valuable indicator for diagnosing CLL (Chronic Lymphocytic Leukemia). While the presence of smudge cells is not an absolute diagnostic criterion for CLL, a blood smear test is a very affordable, simple, and cost-effective diagnostic tool. In this article, we will explore smudge cells, the reasons they form, and their connection to CLL. Deep Block is an AI software capable of analyzing slide images at high speed. Without any coding, users can create and utilize AI models to identify specific objects of interest. With Deep Block, you can quickly and accurately determine the total number of white blood cells, blasts, and smudge cells present in a slide. No-code Computer Vision for Hematology & Microscopy - deeblock.net #hematology #laboratorymedicine #digitalpathology #ai #deeplearning #microscopy #leukemia #whitebloodcells #ml #cv #dl #all #cll #cml #aml #computervision #oncology #internalmedicine #bloodcancer #clinicalmedicine #clinicalpathology #largeareamicroscopy #artificialintelligence #nocode #aitool #aitools #aiplatform #deepblock #micrograph #machinelearning #wemakeaieasy

Following the creation of our white blood cell segmentation AI model, today we’ll use Deep Block to build an AI model capable of detecting smudge cells as well. Check our previous video : https://lnkd.in/gsuizNbH Check the AI project we used : https://lnkd.in/gDe4FsUs Previously, we developed an AI model to detect white blood cells in stained blood smear slides. For the previous project, we labeled approximately 300 white blood cells. This time, we aim to construct a model that can simultaneously detect both white blood cells and smudge cells. Step 1: Creating a New Class We first created a new class, SmudgeCell. By selecting the drawing mode and the SmudgeCell class, we began labeling smudge cells. Smudge cells, characterized by their damaged appearance, are relatively easy to identify. In about an hour, we labeled around 100 smudge cells. It’s important to note that while our micrograph slide images contain a much larger number of smudge cells, it’s not necessary to label every single one to train the model effectively. Step 2: Training the Model Once labeling was complete, we began model training. As before, the images were divided into 27x48 patches, and the model was trained over 15 epochs. Model training started shortly after, taking about 6 minutes to complete. Step 3: Testing the Model With the trained model ready, we moved on to testing it on a complete slide image to detect both white blood cells and smudge cells. We navigated to the Train tab and transferred the full slide image to the Predict tab for inference. Using a 30% threshold score, we began testing. A lower threshold score was chosen as our model was trained on a limited dataset. This approach ensured that we captured more smudge cells, even if the detection sensitivity wasn’t perfect. Step 4: Results The prediction results were displayed after a short processing time. Despite the size of the massive microscopy images, the model successfully identified numerous white blood cells and smudge cells across the slide. Smudge cells were detected accurately, as shown in the results. www.deepblock.net is highly applicable to large-area microscopy images, including fields like pathology, hematology, laboratory medicine, and oncology. Explore the no-code AI toolbox for microscopy at deepblock.net. #hematology #laboratorymedicine #digitalpathology #ai #deeplearning #microscopy #leukemia #whitebloodcells #ml #cv #dl #all #cll #cml #aml #computervision #oncology #internalmedicine #largeareamicroscopy #artificialintelligence #nocode #aitool #aitools #aiplatform #deepblock #micrograph #machinelearning #wemakeaieasy

Deep Block is an AI-based image analysis platform designed for large-scale, high-resolution microscopy image analysis. Deep Block is particularly optimized for identifying multiple types of objects in large-area micrographs. Key features of Deep Block include: Automated Image Analysis: Deep Block automatically analyzes images, accurately identifying structures such as cells, atoms, and thin films. This allows researchers to process vast amounts of image data efficiently, eliminating the need for manual image analysis. High-Speed Data Processing: Compared to traditional methods, Deep Block operates at much higher speeds, making it ideal for large size micrographs. This is particularly beneficial for large area inspection and analysis, where data volumes can be immense. Diverse Object Detection: Users can train their own AI model in Deep Block to detect and classify critical objects in micrographs, such as cells, tumor cells, and other microstructures. This enables multi-faceted research analysis and can be applied in fields like biology, electronics, and semiconductor manufacturing. By using AI-based image analysis tools like Deep Block, researchers can significantly reduce analysis time, process larger datasets, and boost research efficiency overall. Subscribe to our newsletter for the latest insights into computer vision and large-scale image analysis. Stay updated with industry trends, innovative techniques, and cutting-edge tools that are transforming the way we analyze high-resolution images. #microscopy #hematology #pathology #digitalpathology #computervision #ai #nocode #aitools #aiplatform #deeplearning #machinelearning #ml #cv #neuralnetworks #aitool #machinevision #oncology

Subscribe to Deep Block's latest newsletter to stay informed about cutting-edge advancements in AI technology applied to large-scale microscopy and Earth observation! Gain insights into practical use cases, technical innovations, and detailed applications of AI across these fields. Deep Block's updates offer a thorough look into how these technologies are reshaping science and environmental monitoring. Don’t miss out on expert articles, technology spotlights, and in-depth features on the latest in AI-driven solutions for large-scale imaging and observation! #microscopy #remotesensing #earthobservation #photogrammetry #uav #drone #aerialimaging #aerialphotography #computervision #lifescience #semiconductor #failureanalysis #nocode #deeplearning #machinelearing #ml #artificialintelligence #aitool #aitools #aisoftware #digitalpathology #carboncapture #semiconductormanufacturing #eo #hematology

https://lnkd.in/ghd5-gtT This article provides a comprehensive overview of the diverse types of emission microscopy techniques, principles and methodologies that distinguish each approach It explores the various sensors and detectors employed for image acquisition, highlighting their specific roles and capabilities in capturing detailed emissions from samples. Additionally, the article examines the wide range of applications for each emission microscopy method, illustrating how they are utilized across different fields such as semiconductor analysis, materials science, and electronic device evaluation. #microscopy #failureanalysis #semiconductor #semiconductormanufacturing #semiconductorpackaging #ic #packaging #icpackaging #deeplearning #computervision #emmi #emissionmicroscopy #nocode #aitools #micrograph #inspection #aiplatform #machinelearning #ml #artificialintelligence #machinevision #AI