المؤلفون: Selvan, Raghavendra, Pepin, Bob, Igel, Christian, Samuel, Gabrielle, Dam, Erik B

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning

الوصف: The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in terms of compute, data, energy, and carbon emissions. These costs are becoming a new type of entry barrier to researchers and practitioners with limited access to resources at such scale, particularly in the Global South. In this work, we take a comprehensive look at the landscape of existing DL models for vision tasks and demonstrate their usefulness in settings where resources are limited. To account for the resource consumption of DL models, we introduce a novel measure to estimate the performance per resource unit, which we call the PePR score. Using a diverse family of 131 unique DL architectures (spanning 1M to 130M trainable parameters) and three medical image datasets, we capture trends about the performance-resource trade-offs. In applications like medical image analysis, we argue that small-scale, specialized models are better than striving for large-scale models. Furthermore, we show that using pretrained models can significantly reduce the computational resources and data required. We hope this work will encourage the community to focus on improving AI equity by developing methods and models with smaller resource footprints.
Comment: Source code available at https://github.com/saintslab/PePRTest

الوصول الحر: http://arxiv.org/abs/2403.12562Test

المؤلفون: Friis-Jensen, Ulrik, Johansen, Frederik L., Anker, Andy S., Dam, Erik B., Jensen, Kirsten M. Ø., Selvan, Raghavendra

مصطلحات موضوعية: Computer Science - Machine Learning, Statistics - Machine Learning

الوصف: Advances in graph machine learning (ML) have been driven by applications in chemistry as graphs have remained the most expressive representations of molecules. While early graph ML methods focused primarily on small organic molecules, recently, the scope of graph ML has expanded to include inorganic materials. Modelling the periodicity and symmetry of inorganic crystalline materials poses unique challenges, which existing graph ML methods are unable to address. Moving to inorganic nanomaterials increases complexity as the scale of number of nodes within each graph can be broad ($10$ to $10^5$). The bulk of existing graph ML focuses on characterising molecules and materials by predicting target properties with graphs as input. However, the most exciting applications of graph ML will be in their generative capabilities, which is currently not at par with other domains such as images or text. We invite the graph ML community to address these open challenges by presenting two new chemically-informed large-scale inorganic (CHILI) nanomaterials datasets: A medium-scale dataset (with overall >6M nodes, >49M edges) of mono-metallic oxide nanomaterials generated from 12 selected crystal types (CHILI-3K) and a large-scale dataset (with overall >183M nodes, >1.2B edges) of nanomaterials generated from experimentally determined crystal structures (CHILI-100K). We define 11 property prediction tasks and 6 structure prediction tasks, which are of special interest for nanomaterial research. We benchmark the performance of a wide array of baseline methods and use these benchmarking results to highlight areas which need future work. To the best of our knowledge, CHILI-3K and CHILI-100K are the first open-source nanomaterial datasets of this scale -- both on the individual graph level and of the dataset as a whole -- and the only nanomaterials datasets with high structural and elemental diversity.
Comment: 16 pages, 15 figures, 8 tables. Dataset is available at https://github.com/UlrikFriisJensen/CHILITest

الوصول الحر: http://arxiv.org/abs/2402.13221Test

المؤلفون: Pandey, Sumit, Changdar, Satyasaran, Perslev, Mathias, Dam, Erik B

مصطلحات موضوعية: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning

الوصف: Automated segmentation of distinct tumor regions is critical for accurate diagnosis and treatment planning in pediatric brain tumors. This study evaluates the efficacy of the Multi-Planner U-Net (MPUnet) approach in segmenting different tumor subregions across three challenging datasets: Pediatrics Tumor Challenge (PED), Brain Metastasis Challenge (MET), and Sub-Sahara-Africa Adult Glioma (SSA). These datasets represent diverse scenarios and anatomical variations, making them suitable for assessing the robustness and generalization capabilities of the MPUnet model. By utilizing multi-planar information, the MPUnet architecture aims to enhance segmentation accuracy. Our results show varying performance levels across the evaluated challenges, with the tumor core (TC) class demonstrating relatively higher segmentation accuracy. However, variability is observed in the segmentation of other classes, such as the edema and enhancing tumor (ET) regions. These findings emphasize the complexity of brain tumor segmentation and highlight the potential for further refinement of the MPUnet approach and inclusion of MRI more data and preprocessing.

الوصول الحر: http://arxiv.org/abs/2401.06499Test

المؤلفون: Pandey, Sumit, Chen, Kuan-Fu, Dam, Erik B.

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence

الوصف: This paper introduces a comprehensive approach for segmenting regions of interest (ROI) in diverse medical imaging datasets, encompassing ultrasound, CT scans, and X-ray images. The proposed method harnesses the capabilities of the YOLOv8 model for approximate boundary box detection across modalities, alongside the Segment Anything Model (SAM) and High Quality (HQ) SAM for fully automatic and precise segmentation. To generate boundary boxes, the YOLOv8 model was trained using a limited set of 100 images and masks from each modality. The results obtained from our approach are extensively computed and analyzed, demonstrating its effectiveness and potential in medical image analysis. Various evaluation metrics, including precision, recall, F1 score, and Dice Score, were employed to quantify the accuracy of the segmentation results. A comparative analysis was conducted to assess the individual and combined performance of the YOLOv8, YOLOv8+SAM, and YOLOv8+HQ-SAM models. The results indicate that the SAM model performs better than the other two models, exhibiting higher segmentation accuracy and overall performance. While HQ-SAM offers potential advantages, its incremental gains over the standard SAM model may not justify the additional computational cost. The YOLOv8+SAM model shows promise for enhancing medical image segmentation and its clinical implications.

الوصول الحر: http://arxiv.org/abs/2310.12995Test

المؤلفون: Selvan, Raghavendra, Schön, Julian, Dam, Erik B

المصدر: Lecture Notes Comp. Sci.14394 (2023)

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Artificial Intelligence

الوصف: The accelerated development of machine learning methods, primarily deep learning, are causal to the recent breakthroughs in medical image analysis and computer aided intervention. The resource consumption of deep learning models in terms of amount of training data, compute and energy costs are known to be massive. These large resource costs can be barriers in deploying these models in clinics, globally. To address this, there are cogent efforts within the machine learning community to introduce notions of resource efficiency. For instance, using quantisation to alleviate memory consumption. While most of these methods are shown to reduce the resource utilisation, they could come at a cost in performance. In this work, we probe into the trade-off between resource consumption and performance, specifically, when dealing with models that are used in critical settings such as in clinics.
Comment: Accepted to the Resource Efficient Medical Image Analysis workshop at MICCAI-2023. Source code available at https://github.com/raghavian/redlTest

الوصول الحر: http://arxiv.org/abs/2303.10181Test

المؤلفون: Selvan, Raghavendra, Bhagwat, Nikhil, Anthony, Lasse F. Wolff, Kanding, Benjamin, Dam, Erik B.

مصطلحات موضوعية: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning

الوصف: The increasing energy consumption and carbon footprint of deep learning (DL) due to growing compute requirements has become a cause of concern. In this work, we focus on the carbon footprint of developing DL models for medical image analysis (MIA), where volumetric images of high spatial resolution are handled. In this study, we present and compare the features of four tools from literature to quantify the carbon footprint of DL. Using one of these tools we estimate the carbon footprint of medical image segmentation pipelines. We choose nnU-net as the proxy for a medical image segmentation pipeline and experiment on three common datasets. With our work we hope to inform on the increasing energy costs incurred by MIA. We discuss simple strategies to cut-down the environmental impact that can make model selection and training processes more efficient.
Comment: Accepted to be presented as an Oral Presentation at 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022. 13 pages. 5 figures

الوصول الحر: http://arxiv.org/abs/2203.02202Test

المؤلفون: Hunter, David J, Deveza, Leticia A, Collins, Jamie E, Losina, Elena, Katz, Jeffrey N, Nevitt, Michael C, Lynch, John A, Roemer, Frank W, Guermazi, Ali, Bowes, Michael A, Dam, Erik B, Eckstein, Felix, Kwoh, C Kent, Hoffmann, Steve, Kraus, Virginia B

المصدر: Arthritis Care & Research. 74(7)

مصطلحات موضوعية: Biomedical and Clinical Sciences, Clinical Sciences, Prevention, Biomedical Imaging, Pain Research, Arthritis, Aging, Chronic Pain, Clinical Research, Musculoskeletal, Biomarkers, Cartilage, Articular, Disease Progression, Humans, Knee Joint, Magnetic Resonance Imaging, National Institutes of Health (U.S.), Osteoarthritis, Knee, Synovitis, United States, Public Health and Health Services, Psychology, Clinical sciences, Allied health and rehabilitation science

الوصف: ObjectiveTo determine the optimal combination of imaging and biochemical biomarkers for use in the prediction of knee osteoarthritis (OA) progression.MethodsThe present study was a nested case-control trial from the Foundation of the National Institutes of Health OA Biomarkers Consortium that assessed study participants with a Kellgren/Lawrence grade of 1-3 who had complete biomarker data available (n = 539 to 550). Cases were participants' knees that had radiographic and pain progression between 24 and 48 months compared to baseline. Radiographic progression only was assessed in secondary analyses. Biomarkers (baseline and 24-month changes) that had a P value of

وصف الملف: application/pdf

الوصول الحر: https://escholarship.org/uc/item/64n63926Test

المؤلفون: Selvan, Raghavendra, Dam, Erik B, Flensborg, Søren Alexander, Petersen, Jens

المصدر: Journal of Machine Learning for Biomedical Imaging. 2022:005. pp 1-24

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Tensor networks are efficient factorisations of high-dimensional tensors into a network of lower-order tensors. They have been most commonly used to model entanglement in quantum many-body systems and more recently are witnessing increased applications in supervised machine learning. In this work, we formulate image segmentation in a supervised setting with tensor networks. The key idea is to first lift the pixels in image patches to exponentially high-dimensional feature spaces and using a linear decision hyper-plane to classify the input pixels into foreground and background classes. The high-dimensional linear model itself is approximated using the matrix product state (MPS) tensor network. The MPS is weight-shared between the non-overlapping image patches resulting in our strided tensor network model. The performance of the proposed model is evaluated on three 2D- and one 3D- biomedical imaging datasets. The performance of the proposed tensor network segmentation model is compared with relevant baseline methods. In the 2D experiments, the tensor network model yields competitive performance compared to the baseline methods while being more resource efficient.
Comment: Journal extension of our preliminary conference work "Segmenting two-dimensional structures with strided tensor networks", Selvan et al. 2021, available at arXiv:2102.06900. 24 pages, 12 figures. Accepted to be published at the Journal of Machine Learning for Biomedical Imaging, to be updated at https://www.melba-journal.org/papers/2022:005.htmlTest

الوصول الحر: http://arxiv.org/abs/2109.07138Test

المؤلفون: Selvan, Raghavendra, Schön, Julian, Dam, Erik B.

المصدر: Selvan , R , Schön , J & Dam , E B 2024 , Operating Critical Machine Learning Models in Resource Constrained Regimes . in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops : MTSAIL 2023, LEAF 2023, AI4Treat 2023, MMMI 2023, REMIA 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings . , Chapter 29 , Springer , Lecture Notes in Computer Science , vol. 14394 , pp. 325-335 , 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 , Vancouver , Canada , 08/10/2023 . https://doi.org/10.1007/978-3-031-47425-5_29Test

الوصف: The accelerated development of machine learning methods, primarily deep learning, are causal to the recent breakthroughs in medical image analysis and computer aided intervention. The resource consumption of deep learning models in terms of amount of training data, compute and energy costs are known to be massive. These large resource costs can be barriers in deploying these models in clinics, globally. To address this, there are cogent efforts within the machine learning community to introduce notions of resource efficiency. For instance, using quantisation to alleviate memory consumption. While most of these methods are shown to reduce the resource utilisation, they could come at a cost in performance. In this work, we probe into the trade-off between resource consumption and performance, specifically, when dealing with models that are used in critical settings such as in clinics.

وصف الملف: application/pdf

الإتاحة: https://doi.org/10.1007/978-3-031-47425-5_29Test
https://curis.ku.dk/portal/da/publications/operating-critical-machine-learning-models-inresource-constrained-regimesTest(e8440101-184b-46ce-b4c0-7acb155b5d5f).html
https://curis.ku.dk/ws/files/383097267/Operating_critical.pdfTest

المؤلفون: Selvan, Raghavendra, Dam, Erik B, Petersen, Jens

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Tensor networks provide an efficient approximation of operations involving high dimensional tensors and have been extensively used in modelling quantum many-body systems. More recently, supervised learning has been attempted with tensor networks, primarily focused on tasks such as image classification. In this work, we propose a novel formulation of tensor networks for supervised image segmentation which allows them to operate on high resolution medical images. We use the matrix product state (MPS) tensor network on non-overlapping patches of a given input image to predict the segmentation mask by learning a pixel-wise linear classification rule in a high dimensional space. The proposed model is end-to-end trainable using backpropagation. It is implemented as a Strided Tensor Network to reduce the parameter complexity. The performance of the proposed method is evaluated on two public medical imaging datasets and compared to relevant baselines. The evaluation shows that the strided tensor network yields competitive performance compared to CNN-based models while using fewer resources. Additionally, based on the experiments we discuss the feasibility of using fully linear models for segmentation tasks.
Comment: Accepted to be presented at the 27th international conference on Information Processing in Medical Imaging (IPMI-2021), Bornholm, Denmark. Source code at https://github.com/raghavian/strided-tenetTest. Version 2: Minor fixes to notation in Eq.1 and typos

الوصول الحر: http://arxiv.org/abs/2102.06900Test