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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Z. Fang Z. Lu H. Liu Y. Liu G. S.P. Mok Department of Electrical and Computer Engineering Faculty of Science and Technology Biomedical Imaging Laboratory (BIG) University of Macau Macau China Department of Nuclear Medicine The Affiliated Hospital Southwest Medical University Luzhou China Center for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau Macau China

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

FDG PET is a useful tool for diagnosis, staging and treatment monitoring on Head and Neck (H&N) tumors. Segmentation is usually performed for the tumors while the deep learning technique is emerging as an efficient yet precise alternate to manual segmentation. The performance of the conventional CNN-based segmentation networks is limited by insufficient training data. The Segment Anything Model (SAM) has been released by Meta, initiating a paradigm shift to a “smart” interactive segmentation that provides competitive performance when properly prompted. In this study, we proposed a SAM+nnUNet for H&N tumor segmentation on FDG PET to enhance the original SAM, in which the prompting process can be time-consuming and require a high degree of expertise. The HECKTOR2022 PET/CT dataset was used, with 420 patient images for training, 50 cases for validation, and 54 cases for testing. The training dataset of PET was used to fine-tune the mask decoder of pre-trained SAM, while both PET and CT were used to train an nnUNet. The trained nnUNet was first used to perform a preliminary segmentation of the tumors on the testing dataset, which was used to generate bounding box prompts for the SAM input after visual inspection and manual adjustment if necessary. Thresholding segmentation based on 42% of maximum intensity and various U-Net variants are implemented for comparison. Dice Similarity Coefficient (DSC), Hausdorff Distance at 95th percentile (HD95), and SUVmean of segmented tumors compared to the ground truth were analyzed. Our results indicate that 5 out of 54 cases need manual adjustment for the nnUNet-based prompts for SAM. SAM+nnUNet performs best in all quantitative indices. We conclude that our proposed method is promising for H&N tumor segmentation on PET.

关键词： Training Image segmentation Visualization Head Training data Manuals Artificial neural networks

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Leveraging Unstructured Statistical Knowledge in a Probabilistic Language of Thought 42

Leveraging Unstructured Statistical Knowledge in a Probabili...

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42nd Annual Meeting of the cognitive science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020

作者： Lew, Alexander K. Tessler, Michael Henry Mansinghka, Vikash K. Tenenbaum, Joshua B. Department of Electrical Engineering and Computer Science MIT United States Department of Brain and Cognitive Sciences MIT United States

One hallmark of human reasoning is that we can bring to bear a diverse web of common-sense knowledge in any situation. The vastness of our knowledge poses a challenge for the practical implementation of reasoning systems as well as for our cognitive theories - how do people represent their commonsense knowledge? On the one hand, our best models of sophisticated reasoning are top-down, making use primarily of symbolically-encoded knowledge. On the other, much of our understanding of the statistical properties of our environment may arise in a bottom-up fashion, for example through associationist learning mechanisms. Indeed, recent advances in AI have enabled the development of billion-parameter language models that can scour for patterns in gigabytes of text from the web, picking up a surprising amount of common-sense knowledge along the way-but they fail to learn the structure of coherent reasoning. We propose combining these approaches, by em- bedding language-model-backed primitives into a state-of-the-art probabilistic programming language (PPL). On two open-ended reasoning tasks, we show that our PPL models with neural knowledge components characterize the distribution of human responses more accurately than the neural language models alone, raising interesting questions about how people might use language as an interface to common-sense knowledge, and suggesting that building probabilistic models with neural language-model components may be a promising approach for more human-like AI. © 2020 The Author(s)

关键词： Probability distributions

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Deep and Dynamic Metabolic and Structural Imaging in Living Tissues

arXiv

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arXiv 2024年

作者： Liu, Kunzan Cao, Honghao Shashaty, Kasey Yu, Li-Yu Spitz, Sarah Pramotton, Francesca Michela Wan, Zhengpeng Kan, Ellen L. Tevonian, Erin N. Levy, Manuel Lendaro, Eva Kamm, Roger D. Griffith, Linda G. Wang, Fan Qiu, Tong You, Sixian Research Laboratory of Electronics Massachusetts Institute of Technology United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Department of Biological Engineering Massachusetts Institute of Technology United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology United States Department of Mechanical Engineering Massachusetts Institute of Technology United States

Label-free imaging through two-photon autofluorescence (2PAF) of NAD(P)H allows for non-destructive and high-resolution visualization of cellular activities in living systems. However, its application to thick tissues and organoids has been restricted by its limited penetration depth within 300 µm, largely due to tissue scattering at the typical excitation wavelength (∼750 nm) required for NAD(P)H. Here, we demonstrate that the imaging depth for NAD(P)H can be extended to over 700 µm in living engineered human multicellular microtissues by adopting multimode fiber (MMF)-based low-repetition-rate high-peak-power three-photon (3P) excitation of NAD(P)H at 1100 nm. This is achieved by having over 0.5 MW peak power at the band of 1100±25 nm through adaptively modulating multimodal nonlinear pulse propagation with a compact fiber shaper. Moreover, the 8-fold increase in pulse energy at 1100 nm enables faster imaging of monocyte behaviors in the living multicellular models. These results represent a significant advance for deep and dynamic metabolic and structural imaging of intact living biosystems. The modular design (MMF with a slip-on fiber shaper) is anticipated to allow wide adoption of this methodology for demanding in vivo and in vitro imaging applications, including cancer research, autoimmune diseases, and tissue engineering. © 2024, CC BY.

关键词： Multimode fibers

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A CONFORMER BASED ACOUSTIC MODEL FOR ROBUST AUTOMATIC SPEECH RECOGNITION

arXiv

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arXiv 2022年

作者： Yang, Yufeng Wang, Peidong Wang, DeLiang Department of Computer Science and Engineering The Ohio State University United States Microsoft Corporation United States Center for Cognitive and Brain Sciences The Ohio State University United States

This study addresses robust automatic speech recognition (ASR) by introducing a Conformer-based acoustic model. The proposed model builds on the wide residual bi-directional long short-term memory network (WRBN) with utterance-wise dropout and iterative speaker adaptation, but employs a Conformer encoder instead of the recurrent network. The Conformer encoder uses a convolution-augmented attention mechanism for acoustic modeling. The proposed system is evaluated on the monaural ASR task of the CHiME-4 corpus. Coupled with utterance-wise normalization and speaker adaptation, our model achieves 6.25% word error rate, which outperforms WRBN by 8.4% relatively. In addition, the proposed Conformer-based model is 18.3% smaller in model size and reduces total training time by 79.6%. Copyright © 2022, The Authors. All rights reserved.

关键词： Acoustic Modeling

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Exp[licit] A Robot modeling Software based on Exponential Maps

arXiv

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arXiv 2023年

作者： Lachner, Johannes Nah, Moses C. Stramigioli, Stefano Hogan, Neville Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA02139 United States Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede7522 Netherlands

Deriving a robot’s equation of motion typically requires placing multiple coordinate frames, commonly using the Denavit-Hartenberg convention to express the kinematic and dynamic relationships between segments. This paper presents an alternative using the differential geometric method of Exponential Maps, which reduces the number of coordinate frame choices to two. The traditional and differential geometric methods are compared, and the conceptual and practical differences are detailed. The open-source software, Exp[licit]TM, based on the differential geometric method, is introduced. It is intended for use by researchers and engineers with basic knowledge of geometry and robotics. Code snippets and an example application are provided to demonstrate the benefits of the differential geometric method and assist users to get started with the software. © 2023, CC BY.

关键词： Open source software

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DISD-Net: A Dynamic Interactive Network with Self-distillation for Cross-subject Multi-modal Emotion Recognition

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IEEE Transactions on Multimedia 2025年

作者： Cheng, Cheng Liu, Wenzhe Wang, Xinying Feng, Lin Jia, Ziyu Liaoning Normal University Research Center of Brain and Cognitive Neuroscience Dalian116029 China Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei230601 China Key Laboratory of Biomedical Engineering of Hainan Province Haikou570228 China Huzhou University School of Information Engineering Huzhou313000 China Dalian University of Technology School of Computer Science and Technology Dalian116081 China Institute of Automation Chinese Academy of Sciences Brainnetome Center Beijing100190 China

Multi-modal Emotion Recognition (MER) has demonstrated competitive performance in affective computing, owing to synthesizing information from diverse modalities. However, many existing approaches still face unresolved challenges, such as: (i) how to learn compact yet representative features from multi-modal data simultaneously, and (ii) how to address differences among subjects and enhance the generalization of the emotion recognition model, given the diverse nature of individual biological signals. To this end, we propose a Dynamic Interactive Network with Self-Distillation (DISD-Net) for cross-subject MER. The DISD-Net incorporates a dynamin interactive module to capture the intra- and inter-modal interactions from multi-modal data. Additionally, to enhance compactness in modal representations, we leverage the soft labels generated by the DISD-Net model as supplemental training guidance. This involves incorporating self-distillation, aiming to transfer the knowledge that the DISD-Net model contains hard and soft labels to each modality. Finally, domain adaptation (DA) is seamlessly integrated into the dynamic interactive and self-distillation components, forming a unified framework to extract subject-invariant multi-modal emotional features. Experimental results indicate that the proposed model achieves a mean accuracy of 75.00% with a standard deviation of 7.68% for the DEAP dataset and a mean accuracy of 65.65% with a standard deviation of 5.08% for the SEED-IV dataset. © 1999-2012 IEEE.

关键词： domain adaptation (DA) dynamic interactive Multi-modal Emotion Recognition (MER) self-distillation

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Paper Quality Assessment based on Individual Wisdom Metrics from Open Peer Review

arXiv

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arXiv 2025年

作者： Zahorodnii, Andrii van den Bosch, Jasper J.F. Charest, Ian Summerfield, Christopher Fiete, Ila R. Department of Electrical Engineering and Computer Science MIT United States Department of Brain and Cognitive Sciences McGovern Institute MIT United States School of Psychology University of Leeds United Kingdom Département de Psychologie Universit é de Montréal Canada Department of Experimental Psychology University of Oxford United Kingdom

This study proposes a data-driven framework for enhancing the accuracy and efficiency of scientific peer review through an open, bottom-up process that estimates reviewer quality. Traditional closed peer review systems, while essential for quality control, are often slow, costly, and subject to biases that can impede scientific progress. Here, we introduce a method that evaluates individual reviewer reliability by quantifying agreement with community consensus scores and applying Bayesian weighting to refine paper quality assessments. We analyze open peer review data from two major scientific conferences, and demonstrate that reviewer-specific quality scores significantly improve the reliability of paper quality estimation. Perhaps surprisingly, we find that reviewer quality scores are unrelated to authorship quality. Our model incorporates incentive structures to recognize high-quality reviewers and encourage broader coverage of submitted papers, thereby mitigating the common "rich-get-richer" pitfall of social media. These findings suggest that open peer review, with mechanisms for estimating and incentivizing reviewer quality, offers a scalable and equitable alternative for scientific publishing, with potential to enhance the speed, fairness, and transparency of the peer review process. Copyright © 2025, The Authors. All rights reserved.

关键词： Data quality

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Tagging Continuous Labels for EEG-based Emotion Classification

Tagging Continuous Labels for EEG-based Emotion Classificati...

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

作者： Rong-Fei Gu Li-Ming Zhao Wei-Long Zheng Bao-Liang Lu Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence the Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering and Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai People’s Republic of China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai People’s Republic of China

EEG-based emotion classification has long been a critical task in the field of affective brain-computer interface (aBCI). The majority of leading researches construct supervised learning models based on labeled datasets. Several datasets have been released, including different kinds of emotions while utilizing various forms of stimulus materials. However, they adopt discrete labeling methods, in which the EEG data collected during the same stimulus material are given a same label. These methods neglect the fact that emotion changes continuously, and mislabeled data possibly exist. The imprecision of discrete labels may hinder the progress of emotion classification in concerned works. Therefore, we develop an efficient system in this paper to support continuous labeling by giving each sample a unique label, and construct a continuously labeled EEG emotion dataset. Using our dataset with continuous labels, we demonstrate the superiority of continuous labeling in emotion classification through experiments on several classification models. We further utilize the continuous labels to identify the EEG features under induced and non-induced emotions in both our dataset and a public dataset. Our experimental results reveal the learnability and generality of the relation between the EEG features and their continuous labels.

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EEG-Eye Movements Cross-Modal Decision Confidence Measurement with Generative Adversarial Networks

EEG-Eye Movements Cross-Modal Decision Confidence Measuremen...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Cheng Fei Rui Li Li-Ming Zhao Wei-Long Zheng Bao-Liang Lu Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering and Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai People's Republic of China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai People's Republic of China

Decision confidence is an individual's feeling of correctness or optimization when making a decision. Various physiological signals, including electroencephalography (EEG) and eye movements have been studied extensively in measuring levels of decision confidence in humans. While multimodal fusion generally performs better than single-modal approaches, it requires data from different modalities at a greater cost. In particular, collection of EEG data is more complicated and time consuming while eye movement signals are much easier to acquire. To tackle this problem, we propose a cross-modal method based on generative adversarial learning. In our method, the intrinsic relationship between eye movement and EEG features in a high-level feature space can be learned in the training phase, and then we can obtain multimodal information during the test phase when only eye movements are available as inputs. Experimental results on the SEED-VPDC dataset demonstrate that our proposed method outperforms single-modal methods trained and tested only on eye movement signals with an improvement of approximately 5.43% in accuracy, and maintains competitive performance in comparison with multimodal methods. Our cross-modal approach requires only eye movements as inputs and reduces reliance on EEG data, making the decision confidence measurement more applicable and practicable.

关键词： Training Costs Neural engineering Particle measurements Generative adversarial networks Electroencephalography Adversarial machine learning

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Prototype-based Domain Generalization Framework for Subject-Independent brain-computer Interfaces

arXiv

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arXiv 2022年

作者： Musellim, Serkan Han, Dong-Kyun Jeong, Ji-Hoon Lee, Seong-Whan Department of Brain and Cognitive Engineering Korea University Korea Republic of Department of Computer Science Chungbuk National University Korea Republic of Department of Artificial Intelligence Korea University Korea Republic of

brain-computer interface (BCI) is challenging to use in practice due to the inter/intra-subject variability of electroencephalography (EEG). The BCI system, in general, necessitates a calibration technique to obtain subject/session-specific data in order to tune the model each time the system is utilized. This issue is acknowledged as a key hindrance to BCI, and a new strategy based on domain generalization has recently evolved to address it. In light of this, we’ve concentrated on developing an EEG classification framework that can be applied directly to data from unknown domains (i.e. subjects), using only data acquired from separate subjects previously. For this purpose, in this paper, we proposed a framework that employs the open-set recognition technique as an auxiliary task to learn subject-specific style features from the source dataset while helping the shared feature extractor with mapping the features of the unseen target dataset as a new unseen domain. Our aim is to impose cross-instance style in-variance in the same domain and reduce the open space risk on the potential unseen subject in order to improve the generalization ability of the shared feature extractor. Our experiments showed that using the domain information as an auxiliary network increases the generalization performance. Copyright © 2022, The Authors. All rights reserved.

关键词： brain computer interface

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