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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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Position: Social Choice Should Guide AI Alignment in Dealing with Diverse human Feedback 41

Position: Social Choice Should Guide AI Alignment in Dealing...

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41st International Conference on Machine Learning, ICML 2024

作者： Conitzer, Vincent Freedman, Rachel Heitzig, Jobst Holliday, Wesley H. Jacobs, Bob M. Lambert, Nathan Mossé, Milan Pacuit, Eric Russell, Stuart Schoelkopf, Hailey Tewolde, Emanuel Zwicker, William S. Foundations of Cooperative AI Lab Computer Science Department Carnegie Mellon University Pittsburgh United States Institute for Ethics in AI University of Oxford Oxford United Kingdom Center for Human-Compatible AI Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Potsdam Institute for Climate Impact Research Brandenburg Potsdam Germany Department of Philosophy University of California Berkeley United States Department of Philosophy and Moral Sciences Ghent University Ghent Belgium Allen Institute for AI BerkeleyCA United States Department of Philosophy University of Maryland College Park United States EleutherAI United States Department of Mathematics Union College Schenectady United States Murat Sertel Center for Advanced Economic Studies Istanbul Bilgi University Istanbul Turkey

Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, such as helping to commit crimes or producing racist text. One approach to fine-tuning, called reinforcement learning from human feedback, learns from humans' expressed preferences over multiple outputs. Another approach is constitutional AI, in which the input from humans is a list of high-level principles. But how do we deal with potentially diverging input from humans? How can we aggregate the input into consistent data about "collective" preferences or otherwise use it to make collective choices about model behavior? In this paper, we argue that the field of social choice is well positioned to address these questions, and we discuss ways forward for this agenda, drawing on discussions in a recent workshop on Social Choice for AI Ethics and Safety held in Berkeley, CA, USA in December 2023. Copyright 2024 by the author(s)

关键词： Reinforcement learning

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DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting

arXiv

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arXiv 2022年

作者： Igamberdiev, Timour Arnold, Thomas Habernal, Ivan Trustworthy Human Language Technologies Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany

Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents. In practice, existing systems may lack the means to validate their privacy-preserving claims, leading to problems of transparency and reproducibility. We introduce DP-Rewrite, an open-source framework for differentially private text rewriting which aims to solve these problems by being modular, extensible, and highly customizable. Our system incorporates a variety of downstream datasets, models, pre-training procedures, and evaluation metrics to provide a flexible way to lead and validate private text rewriting research. To demonstrate our software in practice, we provide a set of experiments as a case study on the ADePT DP text rewriting system, detecting a privacy leak in its pre-training approach. Our system is publicly available, and we hope that it will help the community to make DP text rewriting research more accessible and transparent. © 2022, CC BY-SA.

关键词： Transparency

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Implementation of a Hip Fracture Classification Network Based on CT Tomography Scans

Implementation of a Hip Fracture Classification Network Base...

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International Conference on computer and Communications (ICCC)

作者： Kui Zhao Meng Yang Ti Zhang Die Hu Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Science University of Chinese Academy of Science Shenyang China Department of Orthopedics Peking University Third Hospital Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Hip fractures is the most common hip clinical trauma. The majority of elderly patients experience a decrease in bone density due to osteoporosis, making them highly prone to various types of hip fractures. In this context, accurate diagnosis and classification of hip fractures are crucial for guiding clinical decision-making and ensuring the best possible patient outcomes. The BV C- N et proposed in this study is a two-stage model for the identification of hip fracture types, which is based on the integration of the V-Net and ConvNeXt architectures. First, deep learning segmentation model is trained to generate candidate fracture regions, and then, a deep learning classification model is trained in the second stage to classify the segmented local fracture regions according to the candidate fracture regions generated in the first stage to ascertain the type of fracture. The results show that the two-stage deep learning model proposed in this study enhances the accuracy of hip fracture type recognition and reduces the time required for manual classification, which can better assist doctors in fracture region recognition.

关键词： Deep learning Image segmentation Adaptation models Osteoporosis Accuracy Computed tomography Transfer learning Predictive models Older adults Hip

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Exploring Uni-manual Around Ear Off-Device Gestures for Earables

arXiv

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arXiv 2024年

作者： Shimon, Shaikh Shawon Arefin Neshati, Ali Sun, Junwei Xu, Qiang Zhao, Jian School of Computer Science University of Waterloo 200 University Ave W WaterlooON Canada Ontario Tech University 2000 Simcoe St N OshawaONL1G 0C5 Canada Huawei Human-Machine Interaction Lab 19 Allstate Pkwy MarkhamON Canada School of Computer Science University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada

Small form factor limits physical input space in earable (i.e., ear-mounted wearable) devices. Off-device earable inputs in alternate mid-air and on-skin around-ear interaction spaces using uni-manual gestures can address this input space limitation. Segmenting these alternate interaction spaces to create multiple gesture regions for reusing off-device gestures can expand earable input vocabulary by a large margin. Although prior earable interaction research has explored off-device gesture preferences and recognition techniques in such interaction spaces, supporting gesture reuse over multiple gesture regions needs further exploration. We collected and analyzed 7560 uni-manual gesture motion data from 18 participants to explore earable gesture reuse by segmentation of on-skin and mid-air spaces around the ear. Our results show that gesture performance degrades significantly beyond 3 mid-air and 5 on-skin around-ear gesture regions for different uni-manual gesture classes (e.g., swipe, pinch, tap). We also present qualitative findings on most and least preferred regions (and associated boundaries) by end-users for different uni-manual gesture shapes across both interaction spaces for earable devices. Our results complement earlier elicitation studies and interaction technologies for earables to help expand the gestural input vocabulary and potentially drive future commercialization of such devices. © 2024, CC BY.

关键词： Digital storage

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Towards expert gaze modeling and recognition of a user's attention in realtime 24

Towards expert gaze modeling and recognition of a user's att...

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24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Castner, Nora Geßler, Lea Geisler, David Hüttig, Fabian Kasneci, Enkelejda Human-Computer Interaction University of Tübingen Germany Institute of Computer Science University of Tübingen Germany Department of Prosthodontics University Hospital Tübingen Germany

One of the appealing areas of expertise research is devoted to measuring the effectiveness of training programs for novices. With recent progress in eye tracking, gaze-based interaction systems recognize a user's attention and can direct it accordingly. Moreover, dynamic visualization of an expert gaze model facilitates novice training by guiding the gaze to relevant areas. In addition, the system should be aware of realtime attention to remove an overlay that could occlude relevant information. We use an implementation of subtle gaze direction (SGD) and the simplified scanpath of a dentist to train naive participants in finding anomalies in dental radiographs. We were able to effectively direct user gaze to relevant image features without occluding the area when attention was recognized. Additionally, participants reported that the intervention was helpful for image inspection. The results of the model intervention show minimal improvements in anomaly detection, which is expected of naive subjects. We advocate that the system has the potential to be highly effective for advanced students and trainees with a certain foundation of conceptual knowledge. © 2020 The Authors. Published by Elsevier B.V.

关键词： Eye tracking

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Evolving Deep Neural Network by Customized Moth Flame Optimization Algorithm for Underwater Targets Recognition

arXiv

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arXiv 2023年

作者： Khishe, Mohammad Mohammadi, Mokhtar Rashid, Tarik A. Mahmud, Hoger Mirjalili, Seyedali Department of Marine Electronics and Communication Engineering Imam Khomeini Marine Science University Nowshahr Iran Department of Information Technology College of Engineering and Computer Science Lebanese French University KR Iraq Computer Science and Engineering Department Science and Engineering Science University of Kurdistan Hewler KR Erbil Iraq Department of Computer Science College of Science and Technology University of Human Development KR Sulaymaniyah Iraq Centre for Artificial Intelligence Research and Optimization Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

This chapter proposes using the Moth Flame Optimization (MFO) algorithm for fine-tuning a Deep Neural Network to recognize different underwater sonar datasets. Same as other models evolved by metaheuristic algorithms, premature convergence, trapping in local minima, and failure to converge in a reasonable time are three defects MFO confronts in solving problems with high-dimension search space. Spiral flying is the key component of the MFO as it determines how the moths adjust their positions in relation to flames;thereby, the shape of spiral motions can regulate the transition behavior between the exploration and exploitation phases. Therefore, this chapter investigates the efficiency of seven spiral motions with different curvatures and slopes in the performance of the MFO, especially for underwater target classification tasks. To assess the performance of the customized model, in addition to benchmark Sejnowski & Gorman's dataset, two experimental sonar datasets, i.e., the passive sonar and active datasets, are exploited. The results of MFO and its modifications are compared to four novel nature-inspired algorithms, including Heap-Based Optimizer (HBO), Chimp Optimization Algorithm (ChOA), Ant Lion Optimization (ALO), Stochastic Fractals Search (SFS), as well as the classic Particle Swarm Optimization (PSO). The results confirm that the customized MFO shows better performance than the other state-of-the-art models so that the classification rates are increased 1.5979, 0.9985, and 2.0879 for Sejnowski & Gorman, passive, and active datasets, respectively. The results also approve that time complexity is not significantly increased by using different spiral motions. © 2023, CC BY.

关键词： Deep neural networks

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Insight into the liver dysfunction in COVID-19 patients: Molecular mechanisms and possible therapeutic strategies

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World Journal of Gastroenterology 2023年第14期29卷 2064-2077页

作者： Naina Khullar Jasvinder Singh Bhatti Satwinder Singh Bhawana Thukral P Hemachandra Reddy Gurjit Kaur Bhatti Department of Zoology Mata Gujri CollegeFatehgarh Sahib 140407PunjabIndia Laboratory of Translational Medicine and Nanotherapeutics Department of Human Genetics and Molecular MedicineSchool of Health SciencesCentral University of PunjabBathinda 151401PunjabIndia Department of Computer Science and Technology Central University of PunjabBathinda 151401PunjabIndia Department of Nutrition and Dietetics University Institute of Applied Health SciencesChandigarh UniversityMohali 140413PunjabIndia Department of Internal Medicine Texas Tech University Health Sciences CenterLubbockTX 79430United States Department of Medical Lab Technology University Institute of Applied Health SciencesChandigarh UniversityMohali 140413PunjabIndia

As of June 2022,more than 530 million people worldwide have become ill with coronavirus disease 2019(COVID-19).Although COVID-19 is most commonly associated with respiratory distress(severe acute respiratory syndrome),metaanalysis have indicated that liver dysfunction also occurs in patients with severe *** studies revealed distinctive patterning in the receptors on the hepatic cells that helps in viral invasion through the expression of angiotensinconverting enzyme *** has also been reported that in some patients with COVID-19,therapeutic strategies,including repurposed drugs(mitifovir,lopinavir/ritonavir,tocilizumab,etc.)triggered liver injury and cholestatic *** proven indicators support cytokine storm-induced hepatic *** there are 1.5 billion patients with chronic liver disease worldwide,it becomes imperative to critically evaluate the molecular mechanisms concerning hepatotropism of COVID-19 and identify new potential *** review also designated a comprehensive outlook of comorbidities and the impact of lifestyle and genetics in managing patients with COVID-19.

关键词： COVID-19 Liver damage Pharmacotherapy Cytokine storm Molecular mechanisms

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Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification

Domain-Specific Pre-training Improves Confidence in Whole Sl...

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

作者： Soham Rohit Chitnis Sidong Liu Tirtharaj Dash Tanmay Tulsidas Verlekar Antonio Di Ieva Shlomo Berkovsky Lovekesh Vig Ashwin Srinivasan Department of Computer Science & Information Systems Birla Institute of Technology and Science Pilani Faculty of Medicine Health and Human Sciences Macquarie University Boolean Lab University of California San Diego TCS Research India

Whole Slide Images (WSIs) or histopathology images are used in digital pathology. WSIs pose great challenges to deep learning models for clinical diagnosis, owing to their size and lack of pixel-level annotations. With the recent advancements in computational pathology, newer multiple-instance learning-based models have been proposed. Multiple-instance learning for WSIs necessitates creating patches and uses the encoding of these patches for diagnosis. These models use generic pre-trained models (ResNet-50 pre-trained on ImageNet) for patch encoding. The recently proposed KimiaNet, a DenseNet121 model pre-trained on TCGA slides, is a domain-specific pre-trained model. This paper shows the effect of domain-specific pre-training on WSI classification. To investigate the effect of domain-specific pre-training, we considered the current state-of-the-art multiple-instance learning models, 1) CLAM, an attention-based model, and 2) TransMIL, a self-attention-based model, and evaluated the models’ confidence and predictive performance in detecting primary brain tumors - gliomas. Domain-specific pre-training improves the confidence of the models and also achieves a new state-of-the-art performance of WSI-based glioma subtype classification, showing a high clinical applicability in assisting glioma diagnosis. We will publicly share our code and experimental results at https://***/soham-chitnis10/WSI-domain-specific.

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Corsetto: A Kinesthetic Garment for Designing, Composing for, and Experiencing an Intersubjective Haptic Voice 23

Corsetto: A Kinesthetic Garment for Designing, Composing for...

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Proceedings of the 2023 CHI Conference on human Factors in Computing Systems

作者： Ozgun Kilic Afsar Yoav Luft Kelsey Cotton Ekaterina R. Stepanova Claudia Núñez-Pacheco Rebecca Kleinberger Fehmi Ben Abdesslem Hiroshi Ishii Kristina Höök MIT Media Lab Massachusetts Institute of Technology United States Media Technology and Interaction Design KTH Royal Institute of Technology Sweden Division of Interaction Design at Department of Computer Science and Engineering Chalmers University of Technology Sweden Sweden School of Interactive Arts and Technology Simon Fraser University Canada College of Art Media & Design Northeastern University United States and MIT Media Lab Massachusetts Institute of Technology United States RISE SICS Computer Systems Sweden

ISBN: (纸本)9781450394215

We present a novel intercorporeal experience – an intersubjective haptic voice. Through an autobiographical design inquiry, based on singing techniques from the classical opera tradition, we created Corsetto, a kinesthetic garment for transferring somatic reminiscents of vocal experience from an expert singer to a listener. We then composed haptic gestures enacted in the Corsetto, emulating upper-body movements of the live singer performing a piece by Morton Feldman named Three Voices. The gestures in the Corsetto added a haptics-based ‘fourth voice’ to the immersive opera performance. Finally, we invited audiences who were asked to wear Corsetto during live performances. Afterwards they engaged in micro-phenomenological interviews. The analysis revealed how the Corsetto managed to bridge inner and outer bodily sensations, creating a feeling of a shared intercorporeal experience, dissolving boundaries between listener, singer and performance. We propose that ‘intersubjective haptics’ can be a generative medium not only for singing performances, but other possible intersubjective experiences.

关键词： voice micro-phenomenology haptics machine learning Robotic textiles shape changing interfaces somaesthetic interaction design

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