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EAI/Springer Innovations in Communication and computing 2022年 vii-viii页

作者： Johri, Prashant Diván, Mario José Khanam, Ruqaiya Marciszack, Marcelo Will, Adrián Yamuna Expressway School of Computing Science & Engineering Galgotias University G.B.Nagar Plot No 2 Sector 17 A Greater Noida Uttar Pradesh India Computer Science and Engineering Department SET Sharda University Greater Noida Uttar Pradesh India Information System Department National Technological University-Tucumán Regional Faculty Tucumán Tucumán Argentina Data Science Research Group at Economy School National University of La Pampa Santa Rosa Argentina National Technological University-Córdoba Regional Faculty Córdoba Argentina

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Learning to learn relation for important people detection in still images

arXiv

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arXiv 2019年

作者： Li, Wei-Hong Hong, Fa-Ting Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University Vico Group School of Informatics University of Edinburgh Accuvision Technology Co. Ltd. Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

Humans can easily recognize the importance of people in social event images, and they always focus on the most important individuals. However, learning to learn the relation between people in an image, and inferring the most important person based on this relation, remains undeveloped. In this work, we propose a deep imPOrtance relation NeTwork (POINT) that combines both relation modeling and feature learning. In particular, we infer two types of interaction modules: the person-person interaction module that learns the interaction between people and the event-person interaction module that learns to describe how a person is involved in the event occurring in an image. We then estimate the importance relations among people from both interactions and encode the relation feature from the importance relations. In this way, POINT automatically learns several types of relation features in parallel, and we aggregate these relation features and the person's feature to form the importance feature for important people classification. Extensive experimental results show that our method is effective for important people detection and verify the efficacy of learning to learn relations for important people detection. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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The Active Asteroids Citizen science Program: Overview and First Results

arXiv

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arXiv 2024年

作者： Chandler, Colin Orion Trujillo, Chadwick A. Oldroyd, William J. Kueny, Jay K. Burris, William A. Hsieh, Henry H. DeSpain, Jarod A. Sedaghat, Nima Sheppard, Scott S. Farrell, Kennedy A. Trilling, David E. Gustafsson, Annika Magbanua, Mark Jesus Mendoza Mazzucato, Michele T. Bosch, Milton K.D. Shaw-Diaz, Tiffany Gonano, Virgilio Lamperti, Al da Silva Campos, José A. Goodwin, Brian L. Terentev, Ivan A. Dukes, Charles J.A. Deen, Sam Dept. of Astronomy The DiRAC Institute University of Washington 3910 15th Ave NE SeattleWA98195 United States LSST Interdisciplinary Network for Collaboration and Computing 933 N. Cherry Avenue TucsonAZ85721 United States Dept. of Astronomy and Planetary Science Northern Arizona University PO Box 6010 FlagstaffAZ86011 United States Raw Data Speaks Initiative United States University of Arizona Dept. of Astronomy Steward Observatory 933 North Cherry Avenue Rm. N204 TucsonAZ85721 United States Wyant College of Optical Sciences University of Arizona 1630 E. University Blvd. TucsonAZ85721 United States Lowell Observatory 1400 W Mars Hill Rd FlagstaffAZ86001 United States Dept. of Physics San Diego State University 5500 Campanile Drive San DiegoCA92182 United States Planetary Science Institute 1700 East Fort Lowell Rd. Suite 106 TucsonAZ85719 United States Institute of Astronomy and Astrophysics Academia Sinica P.O. Box 23-141 Taipei10617 Taiwan Raw Data Speaks Initiative Dept. of Terrestrial Magnetism Carnegie Institution for Science 5241 Broad Branch Road. NW WashingtonDC20015 United States Southwest Research Institute BoulderCO80302 United States Dept. of Laboratory Medicine University of California San Francisco 2340 Sutter Street San FranciscoCA94143 United States Active Asteroids Citizen Scientist Royal Astronomical Society Burlington House Piccadilly LondonW1J 0BQ United Kingdom Physical Sciences Group Siena Academy of Sciences Piazzetta Silvio Gigli 2 Siena53100 Italy Delaware Valley Amateur Astronomers 112 Pebble Beach Drive RoyersfordPA19468 United States Astronomical Society of Southern Africa PO Box 9 Observatory Cape Town7935 South Africa

We present the Citizen science program Active Asteroids and describe discoveries stemming from our ongoing project. Our NASA Partner program is hosted on the Zooniverse online platform and launched on 2021 August 31, with the goal of engaging the community in the search for active asteroids - asteroids with comet-like tails or comae. We also set out to identify other unusual active solar system objects, such as active Centaurs, active quasi-Hilda asteroids, and Jupiter Family Comets (JFCs). Active objects are rare in large part because they are difficult to identify, so we ask volunteers to assist us in searching for active bodies in our collection of millions of images of known minor planets. We produced these cutout images with our project pipeline that makes use of publicly available Dark Energy Camera (DECam) data. Since the project launch, roughly 8,300 volunteers have scrutinized some 430,000 images to great effect, which we describe in this work. In total we have identified previously unknown activity on 15 asteroids, plus one Centaur, that were thought to be asteroidal (i.e., inactive). Of the asteroids, we classify four as active quasi-Hilda asteroids, seven as JFCs, and four as active asteroids, consisting of one Main-belt Comet (MBC) and three MBC candidates. We also include our findings concerning known active objects that our program facilitated, an unanticipated avenue of scientific discovery. These include discovering activity occurring during an orbital epoch for which objects were not known to be active, and the reclassification of objects based on our dynamical analyses. © 2024, CC BY.

关键词： Comets

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

引用

Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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BraTS-Path Challenge: Assessing Heterogeneous Histopathologic Brain Tumor Sub-regions

arXiv

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arXiv 2024年

作者： Bakas, Spyridon Thakur, Siddhesh P. Faghani, Shahriar Moassefi, Mana Baid, Ujjwal Chung, Verena Pati, Sarthak Innani, Shubham Baheti, Bhakti Albrecht, Jake Karargyris, Alexandros Kassem, Hasan Nasrallah, MacLean P. Ahrendsen, Jared T. Barresi, Valeria Gubbiotti, Maria A. Lpez, Giselle Y. Lucas, Calixto-Hope G. Miller, Michael L. Cooper, Lee A.D. Huse, Jason T. Bell, William R. Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurological Surgery Indiana University School of Medicine IndianapolisIN United States Department of Radiology and Imaging Sciences Indiana University School of Medicine IndianapolisIN United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IndianapolisIN United States Medical Research Group MLCommons San FranciscoCA United States Department of Radiology Mayo Clinic MN United States Sage Bionetworks United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology Feinberg School of Medicine Northwestern University ChicagoIL United States Department of Diagnostics and Public Health University of Verona Verona Italy Division of Pathology and Laboratory Medicine Department of Anatomical Pathology University of Texas MD Anderson Cancer Center TX United States The Preston Robert Tisch Brain Tumor Center at Duke DurhamNC United States Department of Pathology Duke University School of Medicine DurhamNC United States Department of Neurosurgery Duke University Medical School DurhamNC United States Division of Neuropathology Department of Pathology Johns Hopkins University School of Medicine BaltimoreMD United States Department of Pathology and Cell Biology Columbia University Irving Medical Center New YorkNY United States Department of Translational Molecular Pathology University of Texas MD Anderson Cancer Center TX United States

Glioblastoma is the most common primary adult brain tumor, with a grim prognosis - median survival of 12-18 months following treatment, and 4 months otherwise. Glioblastoma is widely infiltrative in the cerebral hemispheres and well-defined by heterogeneous molecular and micro-environmental histopathologic profiles, which pose a major obstacle in treatment. Correctly diagnosing these tumors and assessing their heterogeneity is crucial for choosing the precise treatment and potentially enhancing patient survival rates. In the gold-standard histopathology-based approach to tumor diagnosis, detecting various morpho-pathological features of distinct histology throughout digitized tissue sections is crucial. Such "features" include the presence of cellular tumor, geographic necrosis, pseudopalisading necrosis, areas abundant in microvascular proliferation, infiltration into the cortex, wide extension in subcortical white matter, leptomeningeal infiltration, regions dense with macrophages, and the presence of perivascular or scattered lymphocytes. With these features in mind and building upon the main aim of the BraTS Cluster of Challenges https://***/brats2024, the goal of the BraTS-Path challenge is to provide a systematically prepared comprehensive dataset and a benchmarking environment to develop and fairly compare deep-learning models capable of identifying tumor sub-regions of distinct histologic profile. These models aim to further our understanding of the disease and assist in the diagnosis and grading of conditions in a consistent manner. © 2024, CC BY.

关键词： Tumors

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Dual Encoding for Zero-Example Video Retrieval

Dual Encoding for Zero-Example Video Retrieval

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Jianfeng Dong Xirong Li Chaoxi Xu Shouling Ji Yuan He Gang Yang Xun Wang College of Computer and Information Engineering Zhejiang Gongshang University Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AI & Media Computing Lab School of Information Renmin University of China College of Computer Science Zhejiang University Alibaba Group

ISBN: (纸本)9781728132945

This paper attacks the challenging problem of zero-example video retrieval. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described in natural language text with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is required. The majority of existing methods are concept based, extracting relevant concepts from queries and videos and accordingly establishing associations between the two modalities. In contrast, this paper takes a concept-free approach, proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Dual encoding is conceptually simple, practically effective and end-to-end. As experiments on three benchmarks, i.e. MSR-VTT, TRECVID 2016 and 2017 Ad-hoc Video Search show, the proposed solution establishes a new state-of-the-art for zero-example video retrieval.

关键词： Videotapes video retrieval End users end-to-end Dual coding

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Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation

arXiv

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arXiv 2021年

作者： Dang, Vien Ngoc Galati, Francesco Cortese, Rosa Di Giacomo, Giuseppe Marconetto, Viola Mathur, Prateek Lekadir, Karim Lorenzi, Marco Prados, Ferran Zuluaga, Maria A. Data Science Department EURECOM Sophia Antipolis France Artificial Intelligence in Medicine Lab Facultat de Matemàtiques I Informàtica Universitat de Barcelona Spain Queen Square MS Centre Dept of Neuroinflammation UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London United Kingdom Politecnico di Torino Turin Italy Université Côte d'Azur Inria Sophia Antipolis Epione Research Group Valbonne France Centre for Medical Image Computing Dept of Medical Physics and Bioengineering University College London United Kingdom National Institute for Health Research University College London Hospitals Biomedical Research Centre London United Kingdom e-Health Center Universitat Oberta de Catalunya Barcelona Spain

Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors. First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image. Second, due to the complexity of vascular trees and the small size of vessels, it is challenging to obtain the amount of annotated training data typically needed by deep learning methods. To address these problems, we propose a novel annotation-efficient deep learning vessel segmentation framework. The framework avoids pixel-wise annotations, only requiring weak patch-level labels to discriminate between vessel and non-vessel 2D patches in the training set, in a setup similar to the CAPTCHAs used to differentiate humans from bots in web applications. The user-provided weak annotations are used for two tasks: 1) to synthesize pixel-wise pseudo-labels for vessels and background in each patch, which are used to train a segmentation network, and 2) to train a classifier network. The classifier network allows to generate additional weak patch labels, further reducing the annotation burden, and it acts as a noise filter for poor quality images. We use this framework for the segmentation of the cerebrovascular tree in Time-of-Flight angiography (TOF) and Susceptibility-Weighted Images (SWI). The results show that the framework achieves state-of-the-art accuracy, while reducing the annotation time by ∼77% w.r.t. learning-based segmentation methods using pixel-wise labels for training. Copyright © 2021, The Authors. All rights reserved.

关键词： Pixels

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Harnessing multimodal approaches for depression detection using large language models and facial expressions

Npj mental health research

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Npj mental health research 2024年第1期3卷 66页

作者： Misha Sadeghi Robert Richer Bernhard Egger Lena Schindler-Gmelch Lydia Helene Rupp Farnaz Rahimi Matthias Berking Bjoern M Eskofier Machine Learning and Data Analytics Lab (MaD Lab) Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany. misha.sadeghi@fau.de. Machine Learning and Data Analytics Lab (MaD Lab) Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany. Chair of Visual Computing (LGDV) Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91058 Germany. Chair of Clinical Psychology and Psychotherapy (KliPs) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany. Translational Digital Health Group Institute of AI for Health Helmholtz Zentrum München - German Research Center for Environmental Health Neuherberg 85764 Germany.

Detecting depression is a critical component of mental health diagnosis, and accurate assessment is essential for effective treatment. This study introduces a novel, fully automated approach to predicting depression severity using the E-DAIC dataset. We employ Large Language Models (LLMs) to extract depression-related indicators from interview transcripts, utilizing the Patient Health Questionnaire-8 (PHQ-8) score to train the prediction model. Additionally, facial data extracted from video frames is integrated with textual data to create a multimodal model for depression severity prediction. We evaluate three approaches: text-based features, facial features, and a combination of both. Our findings show the best results are achieved by enhancing text data with speech quality assessment, with a mean absolute error of 2.85 and root mean square error of 4.02. This study underscores the potential of automated depression detection, showing text-only models as robust and effective while paving the way for multimodal analysis.

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