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Annual IEEE Symposium on Foundations of computer science

作者： Debarati Das Jacob Gilbert MohammadTaghi Hajiaghayi Tomasz Kociumaka Barna Saha Hamed Saleh Dept. of Computer Science & Engineering Pennsylvania State University State College PA United States Dept. of Computer Science University of Maryland College Park MD United States Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Dept. of Computer Science & Engineering Halıcıoğlu Data Science Institute University of California San Diego San Diego CA United States

ISBN: (数字)9781665455190

ISBN: (纸本)9781665455206

Computing the edit distance of two strings is one of the most basic problems in computer science and combinatorial optimization. Tree edit distance is a natural generalization of edit distance in which the task is to compute a measure of dissimilarity between two (unweighted) rooted trees with node labels. Perhaps the most notable recent application of tree edit distance is in NoSQL big databases, such as MongoDB, where each row of the database is a JSON document represented as a labeled rooted tree and finding dissimilarity between two rows is a basic operation. Until recently, the fastest algorithm for tree edit distance ran in cubic time (Demaine, Mozes, Rossman, Weimann; TALG’10); however, Mao (FOCS’21) broke the cubic barrier for the tree edit distance problem using fast matrix *** a parameter k as an upper bound on the distance, an $\mathcal{O}(n+k^{2})$-time algorithm for edit distance has been known since the 1980s due to works of Myers (Algorithmica’86) and Landau and Vishkin (JCSS’88). The existence of an $\tilde{\mathcal{O}}(n+poly(k))$-time algorithm for tree edit distance has been posed as open question, e.g., by Akmal and Jin (ICALP’21), who give a stateof-the-art $O(nk^{2})$-time algorithm. In this paper, we answer this question positively.

关键词： computer science Upper bound Costs Databases Task analysis Optimization

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UDA-CT: A General Framework for CT Image Standardization

UDA-CT: A General Framework for CT Image Standardization

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Md Selim Jie Zhang Baowei Fei Matthew Lewis Guo-Qiang Zhang Jin Chen Dept. of Computer Science University of Kentucky Lexington KY Dept. of Radiology University of Kentucky Lexington KY Dept. of Bioengineering University of Texas at Dallas Dallas TX Dept. of Radiology UT Southwestern Medical Center Dallas TX Dept. of Neurology University of Texas Health Science Center at Houston Houston TX Inst. for Biomedical Informatics University of Kentucky Lexington KY

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Large-scale CT image studies often suffer from a lack of homogeneity regarding radiomic characteristics due to the images acquired with scanners from different vendors or with different reconstruction algorithms. We propose a deep learning-based framework called UDA-CT to tackle the homogeneity issue by leveraging both paired and unpaired images. Using UDA-CT, the CT images can be standardized both from different acquisition protocols of the same scanner and CT images acquired using a similar protocol but scanners from different vendors. UDA-CT incorporates recent advances in deep learning including domain adaptation and adversarial augmentation. It includes a unique design for model training batch which integrates nonstandard images and their adversarial variations to enhance model generalizability. The experimental results show that UDA-CT significantly improves the performance of the cross-scanner image standardization by utilizing both paired and unpaired data.

关键词： Training Deep learning Adaptation models Protocols Computed tomography Biological system modeling Computational modeling

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Delay Analysis in Closed-Loop EEG Phase-Triggered Transcranial Magnetic Stimulation

Delay Analysis in Closed-Loop EEG Phase-Triggered Transcrani...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yu-Cheng Chang Pin-Hsuan Chao Yan-Ming Kuan Chiu-Jung Huang Li-Fen Chen Wei-Chung Mao Tung-Ping Su Sin-Horng Chen Chun-Shu Wei Dept. of Electronics and Electrical Engineering NYCU Hsinchu Taiwan Inst. of Brain Science NYCU Hsinchu Taiwan Inst. of Biomedical Engineering (BME) NYCU Hsinchu Taiwan Dept. of Computer Science (CS) NYCU Hsinchu Taiwan Dept. of Psychiatry Cheng Hsin General Hospital Taipei Taiwan Dept. of CS and the Inst. of BME NYCU Hsinchu Taiwan

The recent development of closed-loop EEG phase-triggered transcranial magnetic stimulation (TMS) has advanced potential applications of adaptive neuromodulation based on the current brain state. Closed-loop TMS involves instantaneous acquisition of the EEG rhythm, timing prediction of the target phase, and triggering of TMS. However, the accuracy of EEG phase prediction algorithms is largely influenced by the system’s transport delay, and their relationship is rarely considered in related work. This paper proposes a delay analysis that considers the delay of the closed-loop EEG phase-triggered TMS system as a primary factor in the validation of phase prediction algorithms. An in-silico validation using real EEG data was performed to compare the performance of commonly used algorithms. The experimental results indicate a significant influence of the total delay on the algorithm performance, and the performance ranking among algorithms varies at different levels of delay. We conclude that the delay analysis framework should be widely adopted in the design and validation of phase prediction algorithms for closed-loop EEG phase-triggered TMS systems.

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EffiSegNet: Gastrointestinal Polyp Segmentation through a Pre-Trained EfficientNet-based Network with a Simplified Decoder

EffiSegNet: Gastrointestinal Polyp Segmentation through a Pr...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Ioannis A. Vezakis Konstantinos Georgas Dimitrios Fotiadis George K. Matsopoulos TECREANDO B.V. Amsterdam The Netherlands Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Materials Science and Engineering University of Ioannina Ioannina Greece

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This work introduces EffiSegNet, a novel segmentation framework leveraging transfer learning with a pre-trained Convolutional Neural Network (CNN) classifier as its backbone. Deviating from traditional architectures with a symmetric U-shape, EffiSegNet simplifies the decoder and utilizes full-scale feature fusion to minimize computational cost and the number of parameters. We evaluated our model on the gastrointestinal polyp segmentation task using the publicly available Kvasir-SEG dataset, achieving state-of-the-art results. Specifically, the EffiSegNet-B4 network variant achieved an F 1 score of 0.9552, mean Dice (mDice) 0.9483, mean Intersection over Union (mIoU) 0.9056, Precision 0.9679, and Recall 0.9429 with a pre-trained backbone – to the best of our knowledge, the highest reported scores in the literature for this dataset. Additional training from scratch also demonstrated exceptional performance compared to previous work, achieving an F 1 score of 0.9286, mDice 0.9207, mIoU 0.8668, Precision 0.9311 and Recall 0.9262. These results underscore the importance of a well-designed encoder in image segmentation networks and the effectiveness of transfer learning approaches.

关键词： Training Knowledge engineering Image segmentation Transfer learning computer architecture Medical services Gastrointestinal tract Decoding Convolutional neural networks biomedical imaging

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A Land Evaluation Support System using Aerial Images and Land Data for Land Use Classification

A Land Evaluation Support System using Aerial Images and Lan...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Yunhao Tu Mayu Urata Mamoru Endo Takami Yasuda Hirokazu Shimazaki Tomoyuki Kimura Dept. of Computer Science Chubu University Kasugai Japan Graduate School of Informatics Nagoya University Nagoya Japan System Development Department Japan Appraisal System INC Nagoya Japan

Fixed asset tax, which is determined based on land use, is an important and valuable revenue source for local governments in Japan. Local government staff spend a lot of time and effort in conducting field surveys to confirm land use. Therefore, we have been developing a support system for automatic land use determination in local governments using aerial images and land data. This paper improves on our previous study in three ways. Firstly, we use a transformer-based deep learning model to improve the performance of the proposed system. Secondly, we added more classification tasks and introduced the classification of solar panels to apply to the work in local governments. Thirdly. we verified the effect of time lag in data collection by comparing the differences in the data used. This study aims to improve the efficiency and accuracy of land evaluation work, as well as to reduce the burden on staff by developing the land evaluation support system. The results confirm that the system is effective in supporting land evaluation work in local governments.

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EffiSegNet: Gastrointestinal Polyp Segmentation through a Pre-Trained EfficientNet-based Network with a Simplified Decoder

arXiv

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arXiv 2024年

作者： Vezakis, Ioannis A. Georgas, Konstantinos Fotiadis, Dimitrios Matsopoulos, George K. TECREANDO B.V. Amsterdam Netherlands Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Materials Science and Engineering University of Ioannina Ioannina Greece

This work introduces EffiSegNet, a novel segmentation framework leveraging transfer learning with a pre-trained Convolutional Neural Network (CNN) classifier as its backbone. Deviating from traditional architectures with a symmetric U-shape, EffiSegNet simplifies the decoder and utilizes full-scale feature fusion to minimize computational cost and the number of parameters. We evaluated our model on the gastrointestinal polyp segmentation task using the publicly available Kvasir-SEG dataset, achieving state-of-the-art results. Specifically, the EffiSegNet-B4 network variant achieved an F1 score of 0.9552, mean Dice (mDice) 0.9483, mean Intersection over Union (mIoU) 0.9056, Precision 0.9679, and Recall 0.9429 with a pre-trained backbone – to the best of our knowledge, the highest reported scores in the literature for this dataset. Additional training from scratch also demonstrated exceptional performance compared to previous work, achieving an F1 score of 0.9286, mDice 0.9207, mIoU 0.8668, Precision 0.9311 and Recall 0.9262. These results underscore the importance of a well-designed encoder in image segmentation networks and the effectiveness of transfer learning approaches. © 2024, CC BY.

关键词： Semantic Segmentation

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Mitigating the Risk of Health Inequity Exacerbated by Large Language Models

arXiv

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arXiv 2024年

作者： Ji, Yuelyue Ma, Wenhe Sivarajkumar, Sonish Zhang, Hang Sadhu, Eugene Mathew Li, Zhuochun Wu, Xizhi Visweswaran, Shyam Wang, Yanshan Dept. of Information Science University of Pittsburgh PittsburghPA United States Dept. of Health Information Management University of Pittsburgh PittsburghPA United States Intelligent Systems Program University of Pittsburgh PittsburghPA United States Dept. of Biomedical Informatics University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States University of Pittsburgh Medical Center PittsburghPA United States

Recent advancements in large language models (LLMs) have demonstrated their potential in numerous medical applications, particularly in automating clinical trial matching for translational research and enhancing medical question-answering for clinical decision support. However, our study shows that incorporating non-decisive socio-demographic factors—such as race, sex, income level, LGBT+ status, homelessness, illiteracy, disability, and unemployment—into the input of LLMs can lead to incorrect and harmful outputs for these populations. These discrepancies risk exacerbating existing health disparities if LLMs are widely adopted in healthcare. To address this issue, we introduce EquityGuard, a novel framework designed to detect and mitigate the risk of health inequities in LLM-based medical applications. Our evaluation demonstrates its efficacy in promoting equitable outcomes across diverse populations. © 2024, CC BY.

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Comparison of 2D SURE and 3D CT imaging of cortical vessels in a rat kidney

Comparison of 2D SURE and 3D CT imaging of cortical vessels ...

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IEEE Symposium (IUS) Ultrasonics

作者： Lauge Naur Hansen Hans Martin Kjer Mostafa Amin-Naji Nathalie Sarup Panduro Charlotte Mehlin Sørensen Carsten Gundlach Anders Bjorholm Dahl Jørgen Arendt Jensen Dept. of Health Technology Technical University of Denmark Kgs. Lyngby Denmark Dept. of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark Dept. of Biomedical Sciences University of Copenhagen Copenhagen Denmark Dept. of Physics Technical University of Denmark Kgs. Lyngby Denmark

SUper-Resolution ultrasound imaging using the Erythrocytes (SURE) can visualize blood vessels at about 25 to 50 µm resolution, but validation is required to assess how accurately the vasculature and its morphology are represented. Previous work compared ultrasound imaging to maximum intensity projections (MIP) of micro-CT volumes at an insufficient voxel size of 22.6 µm. Here, a 5 µm voxel size micro-CT volume of a cortical region in an excised rat kidney was acquired to test the hypothesis that blood vessels down to 50 µm in diameter are detected with SURE imaging. For the micro-CT volume, the blood vessels were segmented by an intensity threshold, and local thickness estimates were computed using expanding spheres within the segmentation mask. An affine registration between the SURE image and the micro-CT volume was manually defined, and a MIP of the micro-CT volume across 2 mm from the ultrasound imaging plane was computed for vessels with diameter estimates greater than 50 µm. The SURE image depicts 12 cortical radial vessels with high intensities and 9 dimmer cortical radial vessels. Notably, the 12 high intensity vessels are also depicted in the micro-CT projection, but 5 cortical radial vessels and 2 arcuate blood vessels are only visible in the SURE image. Of the faintly depicted cortical radial vessels, 5 vessels are not readily matched to vessels in the micro-CT projection. On the other hand, the micro-CT projection contains 6 cortical radial vessel segments and 2 arcuate blood vessels not depicted in the SURE image. These discrepancies in the comparison may arise from the challenging registration of the ultrasound focus beam and the micro-CT volume that is complicated further by the different states of the imaged tissue. Despite these challenges, SURE depicts most vessels at least down to 50 µm and even resolves parallel vessels not visible in the micro-CT projection, thus highlighting its clinical potential.

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface

Optical Evanescent Field for Direct Observation of Sub-100nm...

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Robotics, Engineering, science, and Technology (RESTCON), International Conference on

作者： Panart Khajornrungruang Thitipat Permpatdechakul Keisuke Suzuki Aran Blattler Yutaka Terayama Daiki Goto Pipat Phaisalpanumas Natthaphon Bun-Athuek Amornphun Phunopas Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Mechanical Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Mechanical Information Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Mechanical Science and Technology Course Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Industiral Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Mechanical Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes.

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Using Unlabeled Data in Self-Supervised Training Improves Automatic Vertebral Body Fracture Assessment

Using Unlabeled Data in Self-Supervised Training Improves Au...

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IEEE International Symposium on biomedical Imaging

作者： Julian Lukas Laue Christian Adolph Eren Bora Yilmaz Carsten Meyer Department of Computer Science Ostfalia University of Applied Sciences Wolfenbüttel Germany L3S Research Center Leibniz University Hannover Hannover Germany Deptartment of Computer Science Faculty of Engineering Kiel University Germany Dept. of Radiology and Neuroradiology Section Biomedical Imaging UKSH Kiel Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Vertebral body (VB) fractures can have severe implications for the patient’s well-being, but often remain undetected. Automatic vertebral fracture assessment is challenging due to severe class imbalance in fracture grades, scarcity of annotated data, and often subtle visual differences between the grades. In this paper, we show that leveraging unlabeled data, collected from eight diverse, publicly available datasets in a self-supervised way via the BYOL framework, can noticeably improve performance. Our pre-trained models outperformed the non-pre-trained baselines in 17 out of 18 comparisons, 11 of which were statistically significant, when evaluated on the widely-used VerSe dataset, our in-house dataset, and combinations thereof. Furthermore, our models reached an average AUROC value of 99.6% on the VerSe test corpus, the highest recorded in the literature so far.

关键词： Training Visualization Sensitivity Biological system modeling Context modeling biomedical imaging

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