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Human-in-the-Loop Segmentation of Multi-species Coral imagery

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Human-in-the-Loop Segmentation of Multi-species Coral Imager...

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Scarlett Raine Ross Marchant Brano Kusy Frederic Maire Niko Sünderhauf Tobias Fischer QUT Centre for Robotics Australia CSIRO Data61 Australia Image Analytics Australia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Broad-scale marine surveys performed by underwater vehicles significantly increase the availability of coral reef imagery, however it is costly and time-consuming for domain experts to label images. Point label propagation is an approach used to leverage existing image data labeled with sparse point labels. The resulting augmented ground truth generated is then used to train a semantic segmentation model. Here, we first demonstrate that recent advances in foundation models enable generation of multi-species coral augmented ground truth masks using denoised DINOv2 features and K-Nearest Neighbors (KNN), without the need for any pre-training or custom-designed algorithms. For extremely sparsely labeled images, we propose a labeling regime based on human-in-the-loop principles, resulting in significant improvement in annotation efficiency: If only 5 point labels per image are available, our proposed human-in-the-loop approach improves on the state-of-the-art by 17.3% for pixel accuracy and 22.6% for mIoU; and by 10.6% and 19.1% when 10 point labels per image are available. Even if the human-in-the-loop labeling regime is not used, the denoised DINOv2 features with a KNN outperforms the prior state-of-the-art by 3.5% for pixel accuracy and 5.7% for mIoU (5 grid points). We also provide a detailed analysis of how point labeling style and the quantity of points per image affects the point label propagation quality and provide general recommendations on maximizing point label efficiency.

关键词： Surveys Accuracy Annotations Semantic segmentation Conferences Marine vegetation Nearest neighbor methods

image Labels Are All You Need for Coarse Seagrass Segmentation

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Image Labels Are All You Need for Coarse Seagrass Segmentati...

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Scarlett Raine Ross Marchant Brano Kusy Frederic Maire Tobias Fischer QUT Centre for Robotics Queensland University of Technology Australia CSIRO Data61 Australia Image Analytics Australia

Seagrass meadows serve as critical carbon sinks, but estimating the amount of carbon they store requires knowledge of the seagrass species present. Underwater and surface vehicles equipped with machine learning algorithms can help to accurately estimate the composition and extent of seagrass meadows at scale. However, previous approaches for seagrass detection and classification have required supervision from patch-level labels. In this paper, we reframe seagrass classification as a weakly supervised coarse segmentation problem where image-level labels are used during training (25 times fewer labels compared to patch-level labeling) and patch-level outputs are obtained at inference time. To this end, we introduce SeaFeats, an architecture that uses unsupervised contrastive pre-training and feature similarity, and SeaCLIP, a model that showcases the effectiveness of large language models as a supervisory signal in domain-specific applications. We demonstrate that an ensemble of SeaFeats and SeaCLIP leads to highly robust performance. Our method outperforms previous approaches that require patch-level labels on the multi-species ‘DeepSeagrass’ dataset by 6.8% (absolute) for the class-weighted F1 score, and by 12.1% (absolute) for the seagrass presence/absence F1 score on the ‘Global Wetlands’ dataset. We also present two case studies for real-world deployment: outlier detection on the Global Wetlands dataset, and application of our method on imagery collected by the FloatyBoat autonomous surface vehicle.

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image Labels Are All You Need for Coarse Seagrass Segmentation

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arXiv 2023年

作者： Raine, Scarlett Marchant, Ross Kusy, Brano Maire, Frederic Fischer, Tobias QUT Centre for Robotics Queensland University of Technology Australia CSIRO Data61 Australia Image Analytics Australia

关键词： Carbon

Decentralized Kernel Ridge Regression Based on data-Dependent Random Feature

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arXiv 2024年

作者： Yang, Ruikai He, Fan He, Mingzhen Yang, Jie Huang, Xiaolin The Institute of Image Processing and Pattern Recognition The MOE Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China The STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven LeuvenB-3001 Belgium

Random feature (RF) has been widely used for node consistency in decentralized kernel ridge regression (KRR). Currently, the consistency is guaranteed by imposing constraints on coefficients of features, necessitating that the random features on different nodes are identical. However, in many applications, data on different nodes varies significantly on the number or distribution, which calls for adaptive and data-dependent methods that generate different RFs. To tackle the essential difficulty, we propose a new decentralized KRR algorithm that pursues consensus on decision functions, which allows great flexibility and well adapts data on nodes. The convergence is rigorously given and the effectiveness is numerically verified: by capturing the characteristics of the data on each node, while maintaining the same communication costs as other methods, we achieved an average regression accuracy improvement of 25.5% across six real-world data sets. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm

data Imputation by Pursuing Better Classification: A Supervised Kernel-Based Method

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arXiv 2024年

作者： Yang, Ruikai He, Fan He, Mingzhen Wang, Kaijie Huang, Xiaolin Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University 800 Dongchuan RD Shanghai200240 China STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Oude Markt 13 Leuven3000 Belgium

data imputation, the process of filling in missing feature elements for incomplete data sets, plays a crucial role in data-driven learning. A fundamental belief is that data imputation is helpful for learning performance, and it follows that the pursuit of better classification can guide the data imputation process. While some works consider using label information to assist in this task, their simplistic utilization of labels lacks flexibility and may rely on strict assumptions. In this paper, we propose a new framework that effectively leverages supervision information to complete missing data in a manner conducive to classification. Specifically, this framework operates in two stages. Firstly, it leverages labels to supervise the optimization of similarity relationships among data, represented by the kernel matrix, with the goal of enhancing classification accuracy. To mitigate overfitting that may occur during this process, a perturbation variable is introduced to improve the robustness of the framework. Secondly, the learned kernel matrix serves as additional supervision information to guide data imputation through regression, utilizing the block coordinate descent method. The superiority of the proposed method is evaluated on four real-world data sets by comparing it with state-of-the-art imputation methods. Remarkably, our algorithm significantly outperforms other methods when the data is missing more than 60% of the features. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

ENHANCING KERNEL FLEXIBILITY VIA LEARNING ASYMMETRIC LOCALLY-ADAPTIVE KERNELS

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arXiv 2023年

作者： He, Fan He, Mingzhen Shi, Lei Huang, Xiaolin Suykens, Johan A.K. STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Belgium Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Shanghai Key Laboratory for Contemporary Applied Mathematics School of Mathematical Sciences Fudan University China

The lack of sufficient flexibility is the key bottleneck of kernel-based learning that relies on manually designed, pre-given, and non-trainable kernels. To enhance kernel flexibility, this paper introduces the concept of Locally-Adaptive-Bandwidths (LAB) as trainable parameters to enhance the Radial Basis Function (RBF) kernel, giving rise to the LAB RBF kernel. The parameters in LAB RBF kernels are data-dependent, and its number can increase with the dataset, allowing for better adaptation to diverse data patterns and enhancing the flexibility of the learned function. This newfound flexibility also brings challenges, particularly with regards to asymmetry and the need for an efficient learning algorithm. To address these challenges, this paper for the first time establishes an asymmetric kernel ridge regression framework and introduces an iterative kernel learning algorithm. This novel approach not only reduces the demand for extensive support data but also significantly improves generalization by training bandwidths on the available training data. Experimental results on real datasets underscore the remarkable performance of the proposed algorithm, showcasing its superior capability in handling large-scale datasets compared to Nyström approximation-based algorithms. Moreover, it demonstrates a significant improvement in regression accuracy over existing kernel-based learning methods and even surpasses residual neural networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Bandwidth

Optimal codes in the Stiefel manifold

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arXiv 2024年

作者： Jasper, John Mankovich, Nathan Mixon, Dustin G. Department of Mathematics and Statistics Air Force Institute of Technology Wright-Patterson AFB OH United States Image Processing Laboratory University of Valencia Valencia Spain Department of Mathematics The Ohio State University ColumbusOH United States Translational Data Analytics Institute The Ohio State University ColumbusOH United States

We consider the coding problem in the Stiefel manifold with chordal distance. After considering various low-dimensional instances of this problem, we use Rankin's bounds on spherical codes to prove upper bounds on the minimum distance of a Stiefel code, and then we construct several examples of codes that achieve equality in these bounds. © 2024, CC BY.

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Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of data: Results from Clinical Neuroscience

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arXiv 2023年

作者： Wang, Rongguang Erus, Guray Chaudhari, Pratik Davatzikos, Christos Department of Electrical and Systems Engineering University of Pennsylvania United States Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania United States Department of Computer and Information Science University of Pennsylvania United States Department of Radiology Perelman School of Medicine University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania 3700 Hamilton Walk 7th Floor PhiladelphiaPA19104 United States

Machine learning (ML) is revolutionizing many areas of engineering and science, including healthcare. However, it is also facing a reproducibility crisis, especially in healthcare. ML models that are carefully constructed from and evaluated on data from one part of the population may not generalize well on data from a different population group, or acquisition instrument settings and acquisition protocols. We tackle this problem in the context of neuroimaging of Alzheimer's disease (AD), schizophrenia (SZ) and brain aging. We develop a weighted empirical risk minimization approach that optimally combines data from a source group, e.g., subjects are stratified by attributes such as sex, age group, race and clinical cohort to make predictions on a target group, e.g., other sex, age group, etc. using a small fraction (10%) of data from the target group. We apply this method to multi-source data of 15,363 individuals from 20 neuroimaging studies to build ML models for diagnosis of AD and SZ, and estimation of brain age. We found that this approach achieves substantially better accuracy than existing domain adaptation techniques: it obtains area under curve greater than 0.95 for AD classification, area under curve greater than 0.7 for SZ classification and mean absolute error less than 5 years for brain age prediction on all target groups, achieving robustness to variations of scanners, protocols, and demographic or clinical characteristics. In some cases, it is even better than training on all data from the target group, because it leverages the diversity and size of a larger training set. We also demonstrate the utility of our models for prognostic tasks such as predicting disease progression in individuals with mild cognitive impairment. Critically, our brain age prediction models lead to new clinical insights regarding correlations with neurophysiological tests. In summary, we present a relatively simple methodology, along with ample experimental evidence, supporting

关键词： Neuroimaging

Restoration of Marker Occluded Hematoxylin and Eosin Stained Whole Slide Histology images Using Generative Adversarial Networks

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Restoration of Marker Occluded Hematoxylin and Eosin Stained...

IEEE International Symposium on Biomedical Imaging

作者： Bairavi Venkatesh Tosha Shaht Antong Chen Soheil Ghafurian Image Data Analytics Informatics IT Merck & Co. Inc. West Point PA USA Image Data Analytics Informatics IT Merck & Co. Inc. Rahway NJ USA

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

It is common for pathologists to annotate specific regions of the tissue, such as tumor, directly on the glass slide with markers. Although this practice was helpful prior to the advent of histology whole slide digitization, it often occludes important details which are increasingly relevant to immuno-oncology due to recent advancements in digital pathology imaging techniques. The current work uses a generative adversarial network with cycle loss to remove these annotations while still maintaining the underlying structure of the tissue by solving an image-to-image translation problem. We train our network on up to 300 whole slide images with marker inks and show that 70% of the corrected image patches are indistinguishable from originally uncontaminated image tissue to a human expert. This portion increases 97% when we replace the human expert with a deep residual network. We demonstrated the fidelity of the method to the original image by calculating the correlation between image gradient magnitudes. We observed a revival of up to 94,000 nuclei per slide in our dataset, the majority of which were located on tissue border.

关键词： Ink Training image reconstruction image segmentation Tumors Correlation Green products