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Device Installation Guide Using Augmented Reality

Device Installation Guide Using Augmented Reality

引用

International symposium on Intelligent Systems and Informatics (SISY)

作者： Gergő Oszvald Sándor Szénási John von Neumann Faculty of Informatics Obuda University Budapest Hungary

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

The aim of this paper is to create a software that enables users to setup devices using augmented reality. Such software could serve educational purposes by providing step-by-step guidance, like assembling a computer. Recognizing a device requires an image processing algorithm capable of real-time recognition of a given equipment. Determining the unique parameters of the devices is also necessary for the algorithm to recognize the device model. Subsequently, various states for these models need to be defined, indicating the steps required to move from one state to another. During these steps, it's possible to specify what instructions the user should receive. These instructions could be textual or some form of shape that highlights a specific part of the device or guides from one point to another. To display these shapes, storing instructions is necessary, typically in a database or a markup language. Additionally, for the software to project the shapes accurately, it needs relative coordinates in relation to a given origin point, which could be a specific point on the recognized device.

关键词： Solid modeling Three-dimensional displays image recognition Shape Databases Computational modeling Software algorithms Software Augmented reality Load modeling

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Contrastive Pretraining for Computational Pathology with Visual-Language Models

Contrastive Pretraining for Computational Pathology with Vis...

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IEEE International symposium on Biomedical Imaging

作者： Qifeng Zhou Thao M. Dang Yuzhi Guo Hehuan Ma Wenliang Zhong Saiyang Na Jean Gao Junzhou Huang Department of Computer Science and Engineering The University of Texas at Arlington Arlington USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

In computational pathology, effectively capturing visual-language embeddings from extensive pathology image-text pairs has become increasingly crucial for diverse downstream tasks. Although prior studies have fine-tuned models like CLIP using large pathology image-text datasets, these models encounter limitations due to their separate processing of text and images, restricting their ability to capture essential cross-modal relationships critical in pathology. Recent advancements in large language models (LLMs) have led to the development of vision-language models (VLMs) that demonstrate enhanced multimodal capabilities, including stronger language comprehension and reasoning skills compared to CLIP. However, while VLMs show potential for multimodal embedding, previous efforts have primarily focused on text-based tasks, leaving their application to multimodal pathology data largely unexplored. In this work, we introduce a VLM-based framework designed to integrate and align pathology visual-language embeddings within a single model. We validate our framework's effectiveness through cross-modal retrieval on pathology image-caption datasets and zero-shot patch classification across seven pathology image datasets, demonstrating its superiority over CLIP-based models and underscoring its potential for advancing pathology research.

关键词： Pathology Computational modeling Biological system modeling Large language models Zero shot learning Contrastive learning Cognition Cross modal retrieval Biomedical imaging

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Dineuro: Distilling Knowledge from 2D Natural images via Deformable Tubular Transferring Strategy for 3D Neuron Reconstruction

Dineuro: Distilling Knowledge from 2D Natural Images via Def...

引用

IEEE International symposium on Biomedical Imaging

作者： Yik San Cheng Runkai Zhao Heng Wang Hanchuan Peng Yui Lo Yuqian Chen Lauren J. O'Donnell Weidong Cai School of Computer Science The University of Sydney Sydney Australia SEU-ALLEN Joint Center Institute for Brain and Intelligence Southeast University Nanjing China Harvard Medical School Boston MA USA Brigham and Women's Hospital Boston USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Reconstructing neuron morphology from 3D light microscope imaging data is critical to aid neuroscientists in analyzing brain networks and neuroanatomy. With the boost from deep learning techniques, a variety of learning-based segmentation models have been developed to enhance the signal-to-noise ratio of raw neuron images as a pre-processing step in the reconstruction workflow. However, most existing models directly encode the latent representative features of volumetric neuron data but neglect their intrinsic morphological knowledge. To address this limitation, we design a novel framework, namely DINeuro, that distills the prior knowledge from DINO which pre-trained on extensive 2D natural images to facilitate neuronal morphological learning of our 3D Vision Transformer. To bridge the knowledge gap between the 2D natural image and 3D microscopic morphologic domains, we propose a deformable tubular transferring strategy that adapts the pre-trained 2D natural knowledge to the inherent tubular characteristics of neuronal structure in the latent embedding space. The experimental results on the Janelia dataset of the BigNeuron project demonstrate that our method achieves a segmentation performance improvement of 4.53% in mean Dice and 3.56% in mean 95% Hausdorff distance.

关键词： image segmentation computer vision Three-dimensional displays Microscopy Neurons Morphology Transformers Feature extraction Brain modeling image reconstruction

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image processing-Based Methods to Improve the Robustness of Robotic Gripping

Image Processing-Based Methods to Improve the Robustness of ...

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International symposium on Computational Intelligence and Informatics

作者： Kristóf Takács Renáta Nagyné Elek Tamás Haidegger Antal Bejczy Center for Intelligent Robotics Óbuda Universtiy Budapest Hungary Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary John von Neumann Faculty of Informatics Óbuda University Budapest Hungary University Research and Innovation Center (EKIK) Óbuda University Budapest Hungary Austrian Center for Medical Innovation and Technology Wiener Neustadt Austria

ISBN: (纸本)9798350398830

image processing techniques are having a huge impact on most fields of robotics and industrial automation. Real-time methods are usually employed in complex automation tasks, assisting with decision making or directly guiding robots and machinery, while post-processing is usually used for retrospective assessment of systems and processes. While artificial intelligence-based image processing algorithms (relying usually on neural networks) are more common nowadays, “classical” image processing methods can also be used effectively for most modern applications. This paper focuses on optical flow-based image processing, proving its efficiency by presenting optical flow-based solutions for modern challenges in different fields of robotics, such as robot-assisted surgery and food processing. The application domain introduced in this paper is based on a smart robotic gripper designed to support automated robot cells in the meat industry. The gripper is capable of slip detection and secure gripping of soft, slippery tissues with the help of the implemented real-time algorithm.

关键词： Training Biomedical optical imaging Automation Service robots image processing Surgery Optical computing

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Generating Seamless Virtual Immunohistochemical Whole Slide images with Content and Color Consistency

Generating Seamless Virtual Immunohistochemical Whole Slide ...

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IEEE International symposium on Biomedical Imaging

作者： Sitong Liu Kechun Liu Samuel Margolis Wenjun Wu Stevan R. Knezevich David E. Elder Megan M. Eguchi Joann G. Elmore Linda G. Shapiro University of Washington University of California Los Angeles Pathology Associates Clovis CA USA University of Pennsylvania

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Immunohistochemical (IHC) stains play a vital role in a pathologist's analysis of medical images, providing crucial diagnostic information for various diseases. However, obtaining IHC staining is not always readily available and often costly. Virtual staining from hematoxylin and eosin (H&E)-stained whole slide images (WSIs) produces useful IHC stains without the expensive physical staining process. However, current virtual WSI generation methods based on tile-wise processing often suffer from inconsistencies in content, texture’ and color at tile boundaries. These inconsistencies lead to artifacts that compromise image quality and potentially hinder the adoption of virtual WSI and accurate clinical assessment. To address this limitation, this paper proposes a novel consistent whole slide image (WSI) synthesis network, CC-WSI-Net, which generates seamless and realistic synthetic WSIs from hematoxylin and eosin (H&E)-stained images. Our method addresses the limitations of current virtual WSI generation methods by integrating a content and color consistency supervisor, ensuring consistency across tiles and accurate detection of clinically relevant features. CC-WSI-Net is validated via image quality analysis, detection assessments, and a survey conducted with pathologists. Notably, in 67% of cases, pathologists could not distinguish between traditionally stained slides and the synthetic slides from CC-WSI-Net.

关键词： image quality Surveys Accuracy image color analysis Feature extraction computer aided diagnosis Medical diagnostic imaging Immune system Diseases

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Explainability of deep learning models in medical image classification

Explainability of deep learning models in medical image clas...

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International symposium on Computational Intelligence and Informatics

作者： Michal Kolarik Martin Sarnovsky Jan Paralic Peter Butka Dept. of Cybernetics and Artificial Intelligence FEI Technical university of Košice Košice Slovakia

ISBN: (纸本)9798350398830

The ability to explain the reasons for one’s decisions to others is an important aspect of being human intelligence. We will look at the explainability aspects of the deep learning models, which are most frequently used in medical image processing tasks. The Explainability of machine learning models in medicine is essential for understanding how the particular ML model works and how it solves the problems it was designed for. The work presented in this paper focuses on the classification of lung CT scans for the detection of COVID-19 patients. We used CNN and DenseNet models for the classification and explored the application of selected visual explainability techniques to provide insight into how the model works when processing the images.

关键词： Deep learning COVID-19 Visualization Computed tomography Computational modeling Neural networks Lung

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EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical image Analysis

EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neura...

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IEEE International symposium on Biomedical Imaging

作者： Yi Pan Hanqi Jiang Junhao Chen Yiwei Li Huaqin Zhao Yifan Zhou Peng Shu Zihao Wu Zhengliang Liu Dajiang Zhu Xiang Li Yohannes Abate Tianming Liu School of Computing University of Georgia Athens GA USA Department of Computer Science and Engineering University of Texas at Arlington TX USA Department of Radiology Massachusetts General Hospital and Harvard Medical School MA USA Department of Physics and Astronomy University of Georgia Athens GA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Neuromorphic computing has emerged as a promising energy-efficient alternative to traditional artificial neural networks (ANNs), predominantly utilizing spiking neural networks (SNNs) implemented on neuromorphic hardware. SNNs are more energy-efficient and excel at processing spatio-temporal data than ANNs. Significant advancements have been made in SNN-based convolutional neural networks (CNNs) and Transformer architectures. However, neuromorphic computing for the medical imaging domain remains underexplored. In this study, we introduce EG-SpikeFormer, an SNN architecture tailored for clinical tasks that incorporates eye-gaze data to guide the model's attention to the diagnostically relevant regions in medical images. Our developed approach effectively addresses shortcut learning issues commonly observed in conventional models, especially in scenarios with limited clinical data and high demands for model reliability, generalizability, and transparency. Our EG-SpikeFormer not only demonstrates superior energy efficiency and performance in medical image prediction tasks but also enhances clinical relevance through multi-modal information alignment. By incorporating eye-gaze data, the model improves interpretability and generalization, opening new directions for applying neuromorphic computing in medical image analysis.

关键词： image analysis Neuromorphic engineering Computational modeling Spiking neural networks Medical services Transformers Energy efficiency Data models Convolutional neural networks Medical diagnostic imaging

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Robotic Manipulation of Pathological Slides Powered by Deep Learning and Classical image processing

Robotic Manipulation of Pathological Slides Powered by Deep ...

引用

International symposium on Computational Intelligence and Informatics

作者： Artúr I. Károly Sebestyén Tirczka Tamás Piricz Péter Galambos Antal Bejczy Center for Intelligent Robotics Óbuda University Bécsi u. 96/B. H-1034 Budapest Hungary Doctoral School of Applied Informatics and Applied Mathematics Óbuda Universty

ISBN: (纸本)9798350398830

Digital pathology has many advantages, so the need for digitizing already existing archives naturally arises. However, the fact that there is no standard way of storing pathology archives makes it difficult to provide an automated solution. In this paper, we tackle this problem with a robotic system, which uses a deep convolutional neural network and traditional image processing methods to automatically detect and localize the pathology samples and perform pick and place to organize the samples in a rack that can be directly inserted into the whole slide imaging (WSI) scanner. We were able to achieve a 90% success rate for the pick and place process. This paper introduces the hardware setup and software components that we used for our system and briefly explains the detection procedure.

关键词： Training Pathology image processing Glass Containers Software Convolutional neural networks

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ACLA: An Approximate Carry-Lookahead Adder with Intelligent Carry Judgement and Correction 22

ACLA: An Approximate Carry-Lookahead Adder with Intelligent ...

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22nd International symposium on Quality Electronic Design (ISQED)

作者： Belwal, Shobhit Bhattacharjya, Rajat Goswami, Kaustav Banerjee, Dip Sankar Indian Inst Informat Technol Guwahati Gauhati Assam India Indian Inst Technol Jodhpur Jodhpur Rajasthan India

ISBN: (纸本)9781728176413

Approximate computing in recent times has emerged as a popular alternative to conventional computing techniques. Fault-tolerant applications in the domains of machine learning, signal processing, and computer vision have shown promising results using approximate computing. Approximations on adders and multipliers have been widely proposed in literature and innovations on that front are still a necessity so as to target specific applications. In this paper, an approximate carry-lookahead adder (ACLA) is proposed which makes use of an intelligent approach for judging the carry of subsequent stages. Also, a correction mechanism is proposed so as to hinder substantial accuracy loss. Experimental results show that ACLA is faster than the traditional ripple-carry adder by 70.5% for 32-bit configurations on an average. In terms of accuracy, for 32-bit configurations, ACLA outperforms other state-of-the-art adders such as SARA [1] and BCSA [2] by 51%.

关键词： Approximate adder computational complexity carry judgement error correction image processing

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Transparent Slide Detection and Gripper Design for Slide Transport by Robotic Arm

Transparent Slide Detection and Gripper Design for Slide Tra...

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International symposium on Computational Intelligence and Informatics

作者： Marianna Dimitrova Kucarov Mátyás Takács Béla Molnár Miklos Kozlovszky Doctoral School of Applied Informatics and Applied Mathematics BioTech Óbuda University 3DHistech Ltd. Budapest Hungary 3DHistech Ltd. Óbuda University University Research and Innovation Center (EKIK) Budapest Hungary 3DHistech Ltd. Budapest Hungary John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (纸本)9798350398830

Transparent slide detection, pick and place automation are obtained in order to boost speed, accuracy and smooth workflow between slide coverslipping and digital scanning operation in digital pathology. Fully automatic, precise, robust solution has been developed by robotic arm using computer vision and image processing techniques. Magazine (slide holder) and gripper design, camera selection and mapping to robot base coordinate system are all guided. Python based software algorithms for slide detection and 3D position determination applied in dynamically changeable environment are also explained step by step. Complete automatic process control method is implemented and tested for different types of slide.

关键词： Pathology computer vision Three-dimensional displays Robot kinematics image processing Process control Reliability theory

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