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  1. Article ; Online: Multiple instance learning for digital pathology: A review of the state-of-the-art, limitations & future potential.

    Gadermayr, Michael / Tschuchnig, Maximilian

    Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

    2024  Volume 112, Page(s) 102337

    Abstract: Digital whole slides images contain an enormous amount of information providing a strong motivation for the development of automated image analysis tools. Particularly deep neural networks show high potential with respect to various tasks in the field of ...

    Abstract Digital whole slides images contain an enormous amount of information providing a strong motivation for the development of automated image analysis tools. Particularly deep neural networks show high potential with respect to various tasks in the field of digital pathology. However, a limitation is given by the fact that typical deep learning algorithms require (manual) annotations in addition to the large amounts of image data, to enable effective training. Multiple instance learning exhibits a powerful tool for training deep neural networks in a scenario without fully annotated data. These methods are particularly effective in the domain of digital pathology, due to the fact that labels for whole slide images are often captured routinely, whereas labels for patches, regions, or pixels are not. This potential resulted in a considerable number of publications, with the vast majority published in the last four years. Besides the availability of digitized data and a high motivation from the medical perspective, the availability of powerful graphics processing units exhibits an accelerator in this field. In this paper, we provide an overview of widely and effectively used concepts of (deep) multiple instance learning approaches and recent advancements. We also critically discuss remaining challenges as well as future potential.
    MeSH term(s) Neural Networks, Computer ; Algorithms ; Image Processing, Computer-Assisted/methods
    Language English
    Publishing date 2024-01-13
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 639451-6
    ISSN 1879-0771 ; 0895-6111
    ISSN (online) 1879-0771
    ISSN 0895-6111
    DOI 10.1016/j.compmedimag.2024.102337
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Multiple Instance Learning for Digital Pathology

    Gadermayr, Michael / Tschuchnig, Maximilian

    A Review on the State-of-the-Art, Limitations & Future Potential

    2022  

    Abstract: Digital whole slides images contain an enormous amount of information providing a strong motivation for the development of automated image analysis tools. Particularly deep neural networks show high potential with respect to various tasks in the field of ...

    Abstract Digital whole slides images contain an enormous amount of information providing a strong motivation for the development of automated image analysis tools. Particularly deep neural networks show high potential with respect to various tasks in the field of digital pathology. However, a limitation is given by the fact that typical deep learning algorithms require (manual) annotations in addition to the large amounts of image data, to enable effective training. Multiple instance learning exhibits a powerful tool for learning deep neural networks in a scenario without fully annotated data. These methods are particularly effective in this domain, due to the fact that labels for a complete whole slide image are often captured routinely, whereas labels for patches, regions or pixels are not. This potential already resulted in a considerable number of publications, with the majority published in the last three years. Besides the availability of data and a high motivation from the medical perspective, the availability of powerful graphics processing units exhibits an accelerator in this field. In this paper, we provide an overview of widely and effectively used concepts of used deep multiple instance learning approaches, recent advances and also critically discuss remaining challenges and future potential.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2022-06-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Inflation forecasting with attention based transformer neural networks

    Tschuchnig, Maximilian / Tschuchnig, Petra / Ferner, Cornelia / Gadermayr, Michael

    2023  

    Abstract: Inflation is a major determinant for allocation decisions and its forecast is a fundamental aim of governments and central banks. However, forecasting inflation is not a trivial task, as its prediction relies on low frequency, highly fluctuating data ... ...

    Abstract Inflation is a major determinant for allocation decisions and its forecast is a fundamental aim of governments and central banks. However, forecasting inflation is not a trivial task, as its prediction relies on low frequency, highly fluctuating data with unclear explanatory variables. While classical models show some possibility of predicting inflation, reliably beating the random walk benchmark remains difficult. Recently, (deep) neural networks have shown impressive results in a multitude of applications, increasingly setting the new state-of-the-art. This paper investigates the potential of the transformer deep neural network architecture to forecast different inflation rates. The results are compared to a study on classical time series and machine learning models. We show that our adapted transformer, on average, outperforms the baseline in 6 out of 16 experiments, showing best scores in two out of four investigated inflation rates. Our results demonstrate that a transformer based neural network can outperform classical regression and machine learning models in certain inflation rates and forecasting horizons.

    Comment: Paper was rejected and we want to switch to a new dataset. So there will not be a simple resubmit with minor changes but some bigger changes in 1. Dataset and 2. Discussion. We would later resubmit again. Thank you!
    Keywords Economics - Econometrics ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-03-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Anomaly Detection in Medical Imaging -- A Mini Review

    Tschuchnig, Maximilian E. / Gadermayr, Michael

    2021  

    Abstract: The increasing digitization of medical imaging enables machine learning based improvements in detecting, visualizing and segmenting lesions, easing the workload for medical experts. However, supervised machine learning requires reliable labelled data, ... ...

    Abstract The increasing digitization of medical imaging enables machine learning based improvements in detecting, visualizing and segmenting lesions, easing the workload for medical experts. However, supervised machine learning requires reliable labelled data, which is is often difficult or impossible to collect or at least time consuming and thereby costly. Therefore methods requiring only partly labeled data (semi-supervised) or no labeling at all (unsupervised methods) have been applied more regularly. Anomaly detection is one possible methodology that is able to leverage semi-supervised and unsupervised methods to handle medical imaging tasks like classification and segmentation. This paper uses a semi-exhaustive literature review of relevant anomaly detection papers in medical imaging to cluster into applications, highlight important results, establish lessons learned and give further advice on how to approach anomaly detection in medical imaging. The qualitative analysis is based on google scholar and 4 different search terms, resulting in 120 different analysed papers. The main results showed that the current research is mostly motivated by reducing the need for labelled data. Also, the successful and substantial amount of research in the brain MRI domain shows the potential for applications in further domains like OCT and chest X-ray.

    Comment: Conference: iDSC2021
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2021-08-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction

    Tschuchnig, Maximilian Ernst / Coste-Marin, Julia / Steininger, Philipp / Gadermayr, Michael

    2023  

    Abstract: Semantic segmentation is a crucial task in medical image processing, essential for segmenting organs or lesions such as tumors. In this study we aim to improve automated segmentation in CBCTs through multi-task learning. To evaluate effects on different ... ...

    Abstract Semantic segmentation is a crucial task in medical image processing, essential for segmenting organs or lesions such as tumors. In this study we aim to improve automated segmentation in CBCTs through multi-task learning. To evaluate effects on different volume qualities, a CBCT dataset is synthesised from the CT Liver Tumor Segmentation Benchmark (LiTS) dataset. To improve segmentation, two approaches are investigated. First, we perform multi-task learning to add morphology based regularization through a volume reconstruction task. Second, we use this reconstruction task to reconstruct the best quality CBCT (most similar to the original CT), facilitating denoising effects. We explore both holistic and patch-based approaches. Our findings reveal that, especially using a patch-based approach, multi-task learning improves segmentation in most cases and that these results can further be improved by our denoising approach.

    Comment: Accepted at German Conference on Medical Image Computing (BVM) 2024
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 004 ; 006
    Publishing date 2023-12-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Erratum: Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential.

    Tschuchnig, Maximilian E / Oostingh, Gertie J / Gadermayr, Michael

    Patterns (New York, N.Y.)

    2020  Volume 1, Issue 8, Page(s) 100144

    Abstract: This corrects the article DOI: 10.1016/j.patter.2020.100089.]. ...

    Abstract [This corrects the article DOI: 10.1016/j.patter.2020.100089.].
    Language English
    Publishing date 2020-11-13
    Publishing country United States
    Document type Published Erratum
    ISSN 2666-3899
    ISSN (online) 2666-3899
    DOI 10.1016/j.patter.2020.100144
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential.

    Tschuchnig, Maximilian E / Oostingh, Gertie J / Gadermayr, Michael

    Patterns (New York, N.Y.)

    2020  Volume 1, Issue 6, Page(s) 100089

    Abstract: Image analysis in the field of digital pathology has recently gained increased popularity. The use of high-quality whole-slide scanners enables the fast acquisition of large amounts of image data, showing extensive context and microscopic detail at the ... ...

    Abstract Image analysis in the field of digital pathology has recently gained increased popularity. The use of high-quality whole-slide scanners enables the fast acquisition of large amounts of image data, showing extensive context and microscopic detail at the same time. Simultaneously, novel machine-learning algorithms have boosted the performance of image analysis approaches. In this paper, we focus on a particularly powerful class of architectures, the so-called generative adversarial networks (GANs) applied to histological image data. Besides improving performance, GANs also enable previously intractable application scenarios in this field. However, GANs could exhibit a potential for introducing bias. Hereby, we summarize the recent state-of-the-art developments in a generalizing notation, present the main applications of GANs, and give an outlook of some chosen promising approaches and their possible future applications. In addition, we identify currently unavailable methods with potential for future applications.
    Language English
    Publishing date 2020-09-11
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 2666-3899
    ISSN (online) 2666-3899
    DOI 10.1016/j.patter.2020.100089
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  8. Book ; Online: Beyond Desktop Computation

    Uray, Martin / Hirsch, Eduard / Katzinger, Gerold / Gadermayr, Michael

    Challenges in Scaling a GPU Infrastructure

    2021  

    Abstract: Enterprises and labs performing computationally expensive data science applications sooner or later face the problem of scale but unconnected infrastructure. For this up-scaling process, an IT service provider can be hired or in-house personnel can ... ...

    Abstract Enterprises and labs performing computationally expensive data science applications sooner or later face the problem of scale but unconnected infrastructure. For this up-scaling process, an IT service provider can be hired or in-house personnel can attempt to implement a software stack. The first option can be quite expensive if it is just about connecting several machines. For the latter option often experience is missing with the data science staff in order to navigate through the software jungle. In this technical report, we illustrate the decision process towards an on-premises infrastructure, our implemented system architecture, and the transformation of the software stack towards a scaleable GPU cluster system.

    Comment: 6 pages, 2 figures, to be published in Proceedings of the 4th International Data Science Conference - iDSC2021
    Keywords Computer Science - Distributed ; Parallel ; and Cluster Computing ; Computer Science - Artificial Intelligence
    Subject code 004
    Publishing date 2021-10-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article: Making texture descriptors invariant to blur.

    Gadermayr, Michael / Uhl, Andreas

    EURASIP journal on image and video processing

    2016  Volume 2016, Page(s) 14

    Abstract: Besides a high distinctiveness, robustness (or invariance) to image degradations is very desirable for texture feature extraction methods in real-world applications. In this paper, focus is on making arbitrary texture descriptors invariant to blur which ... ...

    Abstract Besides a high distinctiveness, robustness (or invariance) to image degradations is very desirable for texture feature extraction methods in real-world applications. In this paper, focus is on making arbitrary texture descriptors invariant to blur which is often prevalent in real image data. From previous work, we know that most state-of-the-art texture feature extraction methods are unable to cope even with minor blur degradations if the classifier's training stage is based on idealistic data. However, if the training set suffers similarly from the degradations, the obtained accuracies are significantly higher. Exploiting that knowledge, in this approach the level of blur of each image is increased to a certain threshold, based on the estimation of a blur measure. Experiments with synthetically degraded data show that the method is able to generate a high degree of blur invariance without loosing too much distinctiveness. Finally, we show that our method is not limited to ideal Gaussian blur.
    Language English
    Publishing date 2016-03-23
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2272982-3
    ISSN 1687-5281 ; 1687-5176
    ISSN (online) 1687-5281
    ISSN 1687-5176
    DOI 10.1186/s13640-016-0116-7
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  10. Book ; Online: Improving Endoscopic Decision Support Systems by Translating Between Imaging Modalities

    Wimmer, Georg / Gadermayr, Michael / Vécsei, Andreas / Uhl, Andreas

    2020  

    Abstract: Novel imaging technologies raise many questions concerning the adaptation of computer-aided decision support systems. Classification models either need to be adapted or even newly trained from scratch to exploit the full potential of enhanced techniques. ...

    Abstract Novel imaging technologies raise many questions concerning the adaptation of computer-aided decision support systems. Classification models either need to be adapted or even newly trained from scratch to exploit the full potential of enhanced techniques. Both options typically require the acquisition of new labeled training data. In this work we investigate the applicability of image-to-image translation to endoscopic images showing different imaging modalities, namely conventional white-light and narrow-band imaging. In a study on computer-aided celiac disease diagnosis, we explore whether image-to-image translation is capable of effectively performing the translation between the domains. We investigate if models can be trained on virtual (or a mixture of virtual and real) samples to improve overall accuracy in a setting with limited labeled training data. Finally, we also ask whether a translation of testing images to another domain is capable of improving accuracy by exploiting the enhanced imaging characteristics.

    Comment: Submitted to MICCAI 2020
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 004 ; 006
    Publishing date 2020-04-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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