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Transformer-based normative modelling for anomaly detection of early schizophrenia

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arXiv 2022年

作者： Da Costa, Pedro F. Dafflon, Jessica Mendes, Sergio Leonardo Sato, João Ricardo Jorge Cardoso, M. Leech, Robert Jones, Emily J.H. Pinaya, Walter H.L. Institute of Psychiatry Psychology & Neuroscience King’s College London United Kingdom Centre for Brain and Cognitive Development Birkbeck College London United Kingdom Data Science and Sharing Team National Institute of Mental Health BethesdaMD United States Machine Learning Team National Institute of Mental Health BethesdaMD United States Center of Mathematics Computing and Cognition Universidade Federal do ABC Brazil School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom

Despite the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. machine learning studies have shown that classifiers tend to be overly narrow in the diagnosis prediction task. The overlap between conditions leads to high heterogeneity among participants that is not adequately captured by classification models. To address this issue, normative approaches have surged as an alternative method. By using a generative model to learn the distribution of healthy brain data patterns, we can identify the presence of pathologies as deviations or outliers from the distribution learned by the model. In particular, deep generative models showed great results as normative models to identify neurological lesions in the brain. However, unlike most neurological lesions, psychiatric disorders present subtle changes widespread in several brain regions, making these alterations challenging to identify. In this work, we evaluate the performance of transformer-based normative models to detect subtle brain changes expressed in adolescents and young adults. We trained our model on 3D MRI scans of neurotypical individuals (N=1,765). Then, we obtained the likelihood of neurotypical controls and psychiatric patients with early-stage schizophrenia from an independent dataset (N=93) from the Human Connectome Project. Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0.82 when assessing the difference between controls and individuals with early-stage schizophrenia. Our approach surpassed recent normative methods based on brain age and Gaussian Process, showing the promising use of deep generative models to help in individualised analyses. © 2022, CC BY.

关键词： Brain

Brain Imaging Generation with Latent Diffusion Models

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arXiv 2022年

作者： Pinaya, Walter H.L. Tudosiu, Petru-Daniel Dafflon, Jessica Da Costa, Pedro F. Fernandez, Virginia Nachev, Parashkev Ourselin, Sebastien Cardoso, M. Jorge Department of Biomedical Engineering School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Data Science and Sharing Team Functional Magnetic Resonance Imaging Facility National Institute of Mental Health BethesdaMD20892 United States Machine Learning Team Functional Magnetic Resonance Imaging Facility National Institute of Mental Health BethesdaMD20892 United States Institute of Psychiatry Psychology & Neuroscience King’s College London United Kingdom Centre for Brain and Cognitive Development Birkbeck College United Kingdom Institute of Neurology University College London United Kingdom

Deep neural networks have brought remarkable breakthroughs in medical image analysis. However, due to their data-hungry nature, the modest dataset sizes in medical imaging projects might be hindering their full potential. Generating synthetic data provides a promising alternative, allowing to complement training datasets and conducting medical image research at a larger scale. Diffusion models recently have caught the attention of the computer vision community by producing photorealistic synthetic images. In this study, we explore using Latent Diffusion Models to generate synthetic images from high-resolution 3D brain images. We used T1w MRI images from the UK Biobank dataset (N=31,740) to train our models to learn about the probabilistic distribution of brain images, conditioned on covariables, such as age, sex, and brain structure volumes. We found that our models created realistic data, and we could use the conditioning variables to control the data generation effectively. Besides that, we created a synthetic dataset with 100,000 brain images and made it openly available to the scientific community. © 2022, CC BY.

关键词： Deep neural networks

Encrypted machine learning of molecular quantum properties

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arXiv 2022年

作者： Weinreich, Jan von Rudorff, Guido Falk von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria University of Vienna Vienna Doctoral School in Physics Boltzmanngasse 5 Vienna1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Large machine learning models with improved predictions have become widely available in the chemical sciences. Unfortunately, these models do not protect the privacy necessary within commercial settings, prohibiting the use of potentially extremely valuable data by others. Encrypting the prediction process can solve this problem by double-blind model evaluation and prohibits the extraction of training or query data. Unfortunately, contemporary ML models based on fully homomorphic encryption or federated learning are either too expensive for practical use or have to trade higher speed for weaker security. We have implemented secure and computationally feasible encrypted machine learning models using oblivious transfer enabling and secure predictions of molecular quantum properties across chemical compound space. However, we find that encrypted predictions using kernel ridge regression models are a million times more expensive than without encryption. This demonstrates a dire need for a compact machine learning model architecture, including molecular representation and kernel matrix size, that minimizes model evaluation costs. © 2022, CC BY.

关键词： Forecasting

Towards self-driving laboratories: The central role of density functional theory in the AI age

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arXiv 2023年

作者： Huang, Bing von Rudorff, Guido Falk Anatole von Lilienfeld, O. University of Vienna Faculty of Physics Kolingasse 14-16 WienAT1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Heinrich-Plett-Straße 40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Department of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Density functional theory (DFT) plays a pivotal role for the chemical and materials science due to its relatively high predictive power, applicability, versatility and computational efficiency. We review recent progress in machine learning model developments which has relied heavily on density functional theory for synthetic data generation and for the design of model architectures. The general relevance of these developments is placed in some broader context for the chemical and materials sciences. Resulting in DFT based machine learning models with high efficiency, accuracy, scalability, and transferability (EAST), recent progress indicates probable ways for the routine use of successful experimental planning software within self-driving laboratories. © 2023, CC0.

关键词： Density functional theory

Large language models illuminate a progressive pathway to artificial intelligent healthcare assistant

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Medicine Plus

Medicine Plus 2024年第2期1卷 102-124页

作者： Mingze Yuan Peng Bao Jiajia Yuan Yunhao Shen Zifan Chen Yi Xie Jie Zhao Quanzheng Li Yang Chen Li Zhang Lin Shen Bin Dong Center for Data Science Peking UniversityBeijing 100871China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and Institute Beijing 100142China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China Center for Machine Learning Research Peking University Beijing 100871China National Biomedical Imaging Center Peking UniversityBeijing 100871China Peking University Changsha Institute for Computing and Digital Economy Changsha 410205China Massachusetts General Hospital Boston MA 02114-2696USA Harvard Medical School BostonMA 02115USA

With the rapid development of artificial intelligence,large language models(LLMs)have shown promising capabilities in mimicking human-level language comprehen-sion and *** has sparked significant interest in applying LLMs to enhance various aspects of healthcare,ranging from medical education to clinical decision ***,medicine involves multifaceted data modalities and nuanced reasoning skills,presenting challenges for integrating *** review introduces the fundamental applications of general-purpose and specialized LLMs,demon-strating their utilities in knowledge retrieval,research support,clinical workflow automation,and diagnostic *** the inherent multimodality of medicine,the review emphasizes the multimodal LLMs and discusses their ability to process diverse data types like medical imaging and electronic health records to augment diagnostic *** address LLMs'limitations regarding personalization and complex clinical reasoning,the review further explores the emerging develop-ment of LLM-powered autonomous agents for ***,it summarizes the evaluation methodologies for assessing LLMs'reliability and safety in medical *** have transformative potential in medicine;however,there is a pivotal need for continuous optimizations and ethical oversight before these models can be effectively integrated into clinical practice.

关键词： Large language models Artificial intelligence Medicine Healthcare assistant Prompt engineering In-context learning

Customer Segmentation Using Clustering Analysis

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Customer Segmentation Using Clustering Analysis

Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Moses Makuei Jiet Aahash Kamble Chetan Puri Prajyot Yesankar Prateek Verma Rajendra Rewatkar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

This research focuses on the crucial role of the clustering technique in data mining, specifically in market forecasting and planning. The study presents a comprehensive report on utilizing the k-means clustering technique and data visualization tools to develop a real- time system for a supermarket, predicting sales across various annual cycles. An open-source dataset of a mall with n = 200 customers divided into k = 3 groups, demonstrated high accuracy in predicting day-to-day sales statistics when compared with actual results. The research highlights the effectiveness of the clustering technique in real-time sales prediction for supermarkets.

关键词： Sociology Refining Decision making data visualization Prediction algorithms Real-time systems Planning

AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics

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arXiv 2024年

作者： Dippel, Jonas Prenißl, Niklas Hense, Julius Liznerski, Philipp Winterhoff, Tobias Schallenberg, Simon Kloft, Marius Buchstab, Oliver Horst, David Alber, Maximilian Ruff, Lukas Müller, Klaus-Robert Klauschen, Frederick Machine Learning Group Technische Universität Berlin Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Institute of Pathology Charité – Universitätsmedizin Berlin Berlin Germany Berlin Institute of Health Charité – Universitätsmedizin Berlin BIH Biomedical Innovation Academy BIH Charité Junior Digital Clinician Scientist Program Charitéplatz 1 Berlin10117 Germany RPTU Kaiserslautern Germany Aignostics GmbH Berlin Germany Institute of Pathology Ludwig-Maximilians-Universität Munich Germany Munich Partner Site Germany Department of Artificial Intelligence Korea University Seoul Korea Republic of Max-Planck Institute for Informatics Saarbrücken Germany

While previous studies have demonstrated the potential of AI to diagnose diseases in imaging data, clinical implementation is still lagging behind. This is partly because AI models require training with large numbers of examples only available for common diseases. In clinical reality, however, only few diseases are common, whereas the majority of diseases are less frequent (long-tail distribution). Current AI models overlook or misclassify these diseases. We propose a deep anomaly detection approach that only requires training data from common diseases to detect also all less frequent diseases. We collected two large real-world datasets of gastrointestinal biopsies, which are prototypical of the problem. Herein, the ten most common findings account for approximately 90% of cases, whereas the remaining 10% contained 56 disease entities, including many cancers. 17 million histological images from 5,423 cases were used for training and evaluation. Without any specific training for the diseases, our best-performing model reliably detected a broad spectrum of infrequent ("anomalous") pathologies with 95.0% (stomach) and 91.0% (colon) AUROC and generalized across scanners and hospitals. By design, the proposed anomaly detection can be expected to detect any pathological alteration in the diagnostic tail of gastrointestinal biopsies, including rare primary or metastatic cancers. This study establishes the first effective clinical application of AI-based anomaly detection in histopathology that can flag anomalous cases, facilitate case prioritization, reduce missed diagnoses and enhance the general safety of AI models, thereby driving AI adoption and automation in routine diagnostics and beyond. © 2024, CC BY-NC-ND.

关键词： Diseases

CBAM_SAUNet: A novel attention U-Net for effective segmentation of corner cases

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CBAM_SAUNet: A novel attention U-Net for effective segmentat...

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

作者： Srividya Tirunellai Rajamani Kumar Rajamani Angeline J Karthika R Björn W. Schuller Chair of Embedded Intelligence for Health Care & Wellbeing University of Augsburg Germany Department of Artificial Intelligence Marwadi University Rajkot Gujarat India Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India CHI - Chair of Health Informatics MRI Technische Universität München (TUM) Germany Munich Data Science Institute Germany Munich Center for Machine Learning and GLAM - the Group on Language Audio & Music Imperial College London London UK

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

U-Net has been demonstrated to be effective for the task of medical image segmentation. Additionally, integrating attention mechanism into U-Net has been shown to yield significant benefits. The Shape Attentive U-Net (SAUNet) is one such recently proposed attention U-Net that also focuses on interpretability. Furthermore, recent research has focused on identification and reporting of corner cases in segmentation to accelerate the utilisation of deep learning models in clinical practise. However, achieving good model performance on such corner cases is a less-explored research area. In this paper, we propose CBAM_SAUNet which enhances the dual attention decoder block of SAUNet to improve its performance on corner cases. We achieve this by utilising a novel variant of the Convolutional Block Attention Module (CBAM)’s channel attention in the decoder block of SAUNet. We demonstrate the effectiveness of CBAM_SAUNet in the Automated Cardiac Diagnosis Challenge (ACDC) cardiac MRI segmentation challenge. Our proposed novel approach results in improvement in the Dice scores of 12% for Left Ventricle (LV) as well as Right Ventricle (RV) segmentation and 8% for Myocardium (MYO) for the identified corner-case dataset.

关键词： Deep learning Image segmentation Attention mechanisms Shape Magnetic resonance imaging Myocardium Decoding Medical diagnostic imaging

BIGDML: Towards exact machine learning force fields for materials

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arXiv 2021年

作者： Sauceda, Huziel E. Gálvez-González, Luis E. Chmiela, Stefan Paz-Borbón, Lauro Oliver Müller, Klaus-Robert Tkatchenko, Alexandre Machine Learning Group Technische Universität Berlin Berlin10587 Germany BASLEARN TU Berlin BASF Joint Lab for Machine Learning Technische Universität Berlin Berlin10587 Germany División de Ciencias Exactas y Naturales Universidad de Sonora Blvd. Luis Encinas & Rosales Hermosillo Mexico BIFOLD – Berlin Institute for the Foundations of Learning and Data Germany Instituto de Física Universidad Nacional Autónoma de México Apartado Postal 20-364 CDMX01000 Mexico Google Research Brain team Berlin Germany Department of Artificial Intelligence Korea University Anam-dong Seongbuk-gu Seoul02841 Korea Republic of Max Planck Institute for Informatics Stuhlsatzenhausweg Saarbrücken66123 Germany Department of Physics and Materials Science University of Luxembourg LuxembourgL-1511 Luxembourg

machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that restrict their practical applicability to small subsets of chemical space or require exhaustive datasets for training. Here, we introduce the Bravais-Inspired Gradient-Domain machine learning (BIGDML) approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 geometries for materials including pristine and defect-containing 2D and 3D semiconductors and metals, as well as chemisorbed and physisorbed atomic and molecular adsorbates on surfaces. The BIGDML model employs the full relevant symmetry group for a given material, does not assume artificial atom types or localization of atomic interactions and exhibits high data efficiency and state-of-the-art energy accuracies (errors substantially below 1 meV per atom) for an extended set of materials. Extensive path-integral molecular dynamics carried out with BIGDML models demonstrate the counterintuitive localization of benzene–graphene dynamics induced by nuclear quantum effects and allow to rationalize the Arrhenius behavior of hydrogen diffusion coefficient in a Pd crystal for a wide range of temperatures. Copyright © 2021, The Authors. All rights reserved.

关键词： machine learning