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- Andersson, Axel, et al.
(författare)
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End-to-end Multiple Instance Learning with Gradient Accumulation
- 2022
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Ingår i: 2022 IEEE International Conference on Big Data (Big Data). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665480451 - 9781665480468 ; , s. 2742-2746
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Konferensbidrag (refereegranskat)abstract
- Being able to learn on weakly labeled data and provide interpretability are two of the main reasons why attention-based deep multiple instance learning (ABMIL) methods have become particularly popular for classification of histopathological images. Such image data usually come in the form of gigapixel-sized whole-slide-images (WSI) that are cropped into smaller patches (instances). However, the sheer volume of the data poses a practical big data challenge: All the instances from one WSI cannot fit the GPU memory of conventional deep-learning models. Existing solutions compromise training by relying on pre-trained models, strategic selection of instances, sub-sampling, or self-supervised pre-training. We propose a training strategy based on gradient accumulation that enables direct end-to-end training of ABMIL models without being limited by GPU memory. We conduct experiments on both QMNIST and Imagenette to investigate the performance and training time and compare with the conventional memory-expensive baseline as well as a recent sampled-based approach. This memory-efficient approach, although slower, reaches performance indistinguishable from the memory-expensive baseline.
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| 4. |
- Bajic, Buda, et al.
(författare)
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Blind restoration of images degraded with mixed poisson-Gaussian noise with application in transmission electron microscopy
- 2016
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Ingår i: 2016 Ieee 13Th International Symposium On Biomedical Imaging (ISBI). - : IEEE. - 9781479923496 - 9781479923502 ; , s. 123-127
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Konferensbidrag (refereegranskat)abstract
- Noise and blur, present in images after acquisition, negatively affect their further analysis. For image enhancement when the Point Spread Function (PSF) is unknown, blind deblurring is suitable, where both the PSF and the original image are simultaneously reconstructed. In many realistic imaging conditions, noise is modelled as a mixture of Poisson (signal-dependent) and Gaussian (signal independent) noise. In this paper we propose a blind deconvolution method for images degraded by such mixed noise. The method is based on regularized energy minimization. We evaluate its performance on synthetic images, for different blur kernels and different levels of noise, and compare with non-blind restoration. We illustrate the performance of the method on Transmission Electron Microscopy images of cilia, used in clinical practice for diagnosis of a particular type of genetic disorders.
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| 6. |
- Bajic, Buda, et al.
(författare)
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Generalised deep learning framework for HEp-2 cell recognition using local binary pattern maps
- 2020
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Ingår i: IET Image Processing. - : INST ENGINEERING TECHNOLOGY-IET. - 1751-9659 .- 1751-9667. ; 14:6, s. 1201-1208
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Tidskriftsartikel (refereegranskat)abstract
- The authors propose a novel HEp-2 cell image classifier to improve the automation process of patients' serum evaluation. The authors' solution builds on the recent progress in deep learning based image classification. They propose an ensemble approach using multiple state-of-the-art architectures. They incorporate additional texture information extracted by an improved version of local binary patterns maps, $\alpha $alpha LBP-maps, which enables to create a very effective cell image classifier. This innovative combination is trained on three publicly available datasets and its general applicability is demonstrated through the evaluation on three independent test sets. The presented results show that their approach leads to a general improvement of performance on average on the three public datasets.
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| 7. |
- Bajic, Buda, et al.
(författare)
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Restoration of images degraded by signal-dependent noise based on energy minimization : an empirical study
- 2016
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Ingår i: Journal of Electronic Imaging (JEI). - 1017-9909 .- 1560-229X. ; 25:4
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Tidskriftsartikel (refereegranskat)abstract
- Most energy minimization-based restoration methods are developed for signal-independent Gaussian noise. The assumption of Gaussian noise distribution leads to a quadratic data fidelity term, which is appealing in optimization. When an image is acquired with a photon counting device, it contains signal-dependent Poisson or mixed Poisson–Gaussian noise. We quantify the loss in performance that occurs when a restoration method suited for Gaussian noise is utilized for mixed noise. Signal-dependent noise can be treated by methods based on either classical maximum a posteriori (MAP) probability approach or on a variance stabilization approach (VST). We compare performances of these approaches on a large image material and observe that VST-based methods outperform those based on MAP in both quality of restoration and in computational efficiency. We quantify improvement achieved by utilizing Huber regularization instead of classical total variation regularization. The conclusion from our study is a recommendation to utilize a VST-based approach combined with regularization by Huber potential for restoration of images degraded by blur and signal-dependent noise. This combination provides a robust and flexible method with good performance and high speed.
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| 8. |
- Bajic, Buda, et al.
(författare)
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Single image super-resolution reconstruction in presence of mixed Poisson-Gaussian noise
- 2016
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Ingår i: 2016 SIXTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA). - : IEEE. - 9781467389105
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Konferensbidrag (refereegranskat)abstract
- Single image super-resolution (SR) reconstructionaims to estimate a noise-free and blur-free high resolution imagefrom a single blurred and noisy lower resolution observation.Most existing SR reconstruction methods assume that noise in theimage is white Gaussian. Noise resulting from photon countingdevices, as commonly used in image acquisition, is, however,better modelled with a mixed Poisson-Gaussian distribution. Inthis study we propose a single image SR reconstruction methodbased on energy minimization for images degraded by mixedPoisson-Gaussian noise.We evaluate performance of the proposedmethod on synthetic images, for different levels of blur andnoise, and compare it with recent methods for non-Gaussiannoise. Analysis shows that the appropriate treatment of signaldependentnoise, provided by our proposed method, leads tosignificant improvement in reconstruction performance.
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| 9. |
- Bajic, Buda, et al.
(författare)
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Sparsity promoting super-resolution coverage segmentation by linear unmixing in presence of blur and noise
- 2019
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Ingår i: Journal of Electronic Imaging (JEI). - : IS&T & SPIE. - 1017-9909 .- 1560-229X. ; 28:1
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Tidskriftsartikel (refereegranskat)abstract
- We present a segmentation method that estimates the relative coverage of each pixel in a sensed image by each image component. The proposed super-resolution blur-aware model (utilizes a priori knowledge of the image blur) for linear unmixing of image intensities relies on a sparsity promoting approach expressed by two main requirements: (i) minimization of Huberized total variation, providing smooth object boundaries and noise removal, and (ii) minimization of nonedge image fuzziness, responding to an assumption that imaged objects are crisp and that fuzziness is mainly due to the imaging and digitization process. Edge fuzziness due to partial coverage is allowed, enabling subpixel precise feature estimates. The segmentation is formulated as an energy minimization problem and solved by the spectral projected gradient method, utilizing a graduated nonconvexity scheme. Quantitative and qualitative evaluation on synthetic and real multichannel images confirms good performance, particularly relevant when subpixel precision in segmentation and subsequent analysis is a requirement. (C) 2019 SPIE and IS&T
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| 10. |
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Bioimage Data Analysis Workflows
- 2019
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Samlingsverk (redaktörskap) (refereegranskat)abstract
- This Open Access textbook provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python. Besides gaining knowhow on algorithm usage, readers will learn how to create an analysis pipeline by scripting language; these skills are important in order to document reproducible image analysis workflows.The textbook is chiefly intended for advanced undergraduates in the life sciences and biomedicine without a theoretical background in data analysis, as well as for postdocs, staff scientists and faculty members who need to perform regular quantitative analyses of microscopy images.
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