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  1. Article: Mitosis detection in breast cancer histology images with deep neural networks.

    Cireşan, Dan C / Giusti, Alessandro / Gambardella, Luca M / Schmidhuber, Jürgen

    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

    2014  Volume 16, Issue Pt 2, Page(s) 411–418

    Abstract: We use deep max-pooling convolutional neural networks to detect mitosis in breast histology images. The networks are trained to classify each pixel in the images, using as context a patch centered on the pixel. Simple postprocessing is then applied to ... ...

    Abstract We use deep max-pooling convolutional neural networks to detect mitosis in breast histology images. The networks are trained to classify each pixel in the images, using as context a patch centered on the pixel. Simple postprocessing is then applied to the network output. Our approach won the ICPR 2012 mitosis detection competition, outperforming other contestants by a significant margin.
    MeSH term(s) Algorithms ; Biopsy ; Breast Neoplasms/pathology ; Breast Neoplasms/physiopathology ; Cell Nucleus/pathology ; Female ; Humans ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/methods ; Microscopy/methods ; Mitosis ; Neural Networks (Computer) ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity
    Language English
    Publishing date 2014-02-22
    Publishing country Germany
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    DOI 10.1007/978-3-642-40763-5_51
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Handwritten Digit Recognition with a Committee of Deep Neural Nets on GPUs

    Cireşan, Dan C. / Meier, Ueli / Gambardella, Luca M. / Schmidhuber, Jürgen

    2011  

    Abstract: The competitive MNIST handwritten digit recognition benchmark has a long history of broken records since 1998. The most recent substantial improvement by others dates back 7 years (error rate 0.4%) . Recently we were able to significantly improve this ... ...

    Abstract The competitive MNIST handwritten digit recognition benchmark has a long history of broken records since 1998. The most recent substantial improvement by others dates back 7 years (error rate 0.4%) . Recently we were able to significantly improve this result, using graphics cards to greatly speed up training of simple but deep MLPs, which achieved 0.35%, outperforming all the previous more complex methods. Here we report another substantial improvement: 0.31% obtained using a committee of MLPs.

    Comment: 9 pages, 4 figures, 3 tables
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Neural and Evolutionary Computing
    Publishing date 2011-03-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

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  3. Article: Candidate sampling for neuron reconstruction from anisotropic electron microscopy volumes.

    Funke, Jan / Martel, Julien N P / Gerhard, Stephan / Andres, Bjoern / Cireşan, Dan C / Giusti, Alessandro / Gambardella, Luca M / Schmidhuber, Jürgen / Pfister, Hanspeter / Cardona, Albert / Cook, Matthew

    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

    2014  Volume 17, Issue Pt 1, Page(s) 17–24

    Abstract: The automatic reconstruction of neurons from stacks of electron microscopy sections is an important computer vision problem in neuroscience. Recent advances are based on a two step approach: First, a set of possible 2D neuron candidates is generated for ... ...

    Abstract The automatic reconstruction of neurons from stacks of electron microscopy sections is an important computer vision problem in neuroscience. Recent advances are based on a two step approach: First, a set of possible 2D neuron candidates is generated for each section independently based on membrane predictions of a local classifier. Second, the candidates of all sections of the stack are fed to a neuron tracker that selects and connects them in 3D to yield a reconstruction. The accuracy of the result is currently limited by the quality of the generated candidates. In this paper, we propose to replace the heuristic set of candidates used in previous methods with samples drawn from a conditional random field (CRF) that is trained to label sections of neural tissue. We show on a stack of Drosophila melanogaster neural tissue that neuron candidates generated with our method produce 30% less reconstruction errors than current candidate generation methods. Two properties of our CRF are crucial for the accuracy and applicability of our method: (1) The CRF models the orientation of membranes to produce more plausible neuron candidates. (2) The interactions in the CRF are restricted to form a bipartite graph, which allows a great sampling speed-up without loss of accuracy.
    MeSH term(s) Algorithms ; Animals ; Anisotropy ; Cells, Cultured ; Data Interpretation, Statistical ; Drosophila melanogaster ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/methods ; Imaging, Three-Dimensional/methods ; Microscopy, Electron/methods ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sample Size ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Subtraction Technique
    Language English
    Publishing date 2014-10-15
    Publishing country Germany
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    DOI 10.1007/978-3-319-10404-1_3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  4. Book ; Online: High-Performance Neural Networks for Visual Object Classification

    Cireşan, Dan C. / Meier, Ueli / Masci, Jonathan / Gambardella, Luca M. / Schmidhuber, Jürgen

    2011  

    Abstract: We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve ...

    Abstract We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the best published results on benchmarks for object classification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with error rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple back-propagation perform better than more shallow ones. Learning is surprisingly rapid. NORB is completely trained within five epochs. Test error rates on MNIST drop to 2.42%, 0.97% and 0.48% after 1, 3 and 17 epochs, respectively.

    Comment: 12 pages, 2 figures, 5 tables
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Neural and Evolutionary Computing
    Publishing date 2011-02-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Article ; Online: Assessment of algorithms for mitosis detection in breast cancer histopathology images.

    Veta, Mitko / van Diest, Paul J / Willems, Stefan M / Wang, Haibo / Madabhushi, Anant / Cruz-Roa, Angel / Gonzalez, Fabio / Larsen, Anders B L / Vestergaard, Jacob S / Dahl, Anders B / Cireşan, Dan C / Schmidhuber, Jürgen / Giusti, Alessandro / Gambardella, Luca M / Tek, F Boray / Walter, Thomas / Wang, Ching-Wei / Kondo, Satoshi / Matuszewski, Bogdan J /
    Precioso, Frederic / Snell, Violet / Kittler, Josef / de Campos, Teofilo E / Khan, Adnan M / Rajpoot, Nasir M / Arkoumani, Evdokia / Lacle, Miangela M / Viergever, Max A / Pluim, Josien P W

    Medical image analysis

    2015  Volume 20, Issue 1, Page(s) 237–248

    Abstract: The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is ... ...

    Abstract The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.
    MeSH term(s) Algorithms ; Breast Neoplasms/pathology ; Female ; Humans ; Mitosis ; Observer Variation
    Language English
    Publishing date 2015-02
    Publishing country Netherlands
    Document type Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1356436-5
    ISSN 1361-8423 ; 1361-8431 ; 1361-8415
    ISSN (online) 1361-8423 ; 1361-8431
    ISSN 1361-8415
    DOI 10.1016/j.media.2014.11.010
    Database MEDical Literature Analysis and Retrieval System OnLINE

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