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  1. Article ; Online: The emperor has few clothes: a realistic appraisal of current AI in radiology.

    Huisman, Merel / van Ginneken, Bram / Harvey, Hugh

    European radiology

    2024  

    Language English
    Publishing date 2024-03-07
    Publishing country Germany
    Document type Editorial
    ZDB-ID 1085366-2
    ISSN 1432-1084 ; 0938-7994 ; 1613-3749
    ISSN (online) 1432-1084
    ISSN 0938-7994 ; 1613-3749
    DOI 10.1007/s00330-024-10664-0
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    Zs.A 3300: Show issues Location:
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  2. Article ; Online: Tuberculosis Detection from Chest Radiographs: Stop Training Radiologists Now.

    van Ginneken, Bram

    Radiology

    2022  Volume 306, Issue 1, Page(s) 138–139

    MeSH term(s) Humans ; Deep Learning ; Radiography ; Tuberculosis, Pulmonary/diagnostic imaging ; Tuberculosis ; Radiologists
    Language English
    Publishing date 2022-09-06
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.221769
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  3. Article ; Online: The Potential of Artificial Intelligence to Analyze Chest Radiographs for Signs of COVID-19 Pneumonia.

    van Ginneken, Bram

    Radiology

    2020  Volume 299, Issue 1, Page(s) E214–E215

    MeSH term(s) Algorithms ; Artificial Intelligence ; COVID-19 ; Humans ; Radiographic Image Interpretation, Computer-Assisted ; SARS-CoV-2
    Language English
    Publishing date 2020-11-24
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.2020204238
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  4. Article ; Online: Full resolution reconstruction of whole-mount sections from digitized individual tissue fragments.

    Schouten, Daan / van der Laak, Jeroen / van Ginneken, Bram / Litjens, Geert

    Scientific reports

    2024  Volume 14, Issue 1, Page(s) 1497

    Abstract: Whole-mount sectioning is a technique in histopathology where a full slice of tissue, such as a transversal cross-section of a prostate specimen, is prepared on a large microscope slide without further sectioning into smaller fragments. Although this ... ...

    Abstract Whole-mount sectioning is a technique in histopathology where a full slice of tissue, such as a transversal cross-section of a prostate specimen, is prepared on a large microscope slide without further sectioning into smaller fragments. Although this technique can offer improved correlation with pre-operative imaging and is paramount for multimodal research, it is not commonly employed due to its technical difficulty, associated cost and cumbersome integration in (digital) pathology workflows. In this work, we present a computational tool named PythoStitcher which reconstructs artificial whole-mount sections from digitized tissue fragments, thereby bringing the benefits of whole-mount sections to pathology labs currently unable to employ this technique. Our proposed algorithm consists of a multi-step approach where it (i) automatically determines how fragments need to be reassembled, (ii) iteratively optimizes the stitch using a genetic algorithm and (iii) efficiently reconstructs the final artificial whole-mount section on full resolution (0.25 µm/pixel). PythoStitcher was validated on a total of 198 cases spanning five datasets with a varying number of tissue fragments originating from different organs from multiple centers. PythoStitcher successfully reconstructed the whole-mount section in 86-100% of cases for a given dataset with a residual registration mismatch of 0.65-2.76 mm on automatically selected landmarks. It is expected that our algorithm can aid pathology labs unable to employ whole-mount sectioning through faster clinical case evaluation and improved radiology-pathology correlation workflows.
    MeSH term(s) Humans ; Algorithms ; Diagnostic Imaging ; Image Processing, Computer-Assisted
    Language English
    Publishing date 2024-01-17
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-024-52007-5
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  5. Article ; Online: Deep Learning for Triage of Chest Radiographs: Should Every Institution Train Its Own System?

    van Ginneken, Bram

    Radiology

    2018  Volume 290, Issue 2, Page(s) 545–546

    MeSH term(s) Deep Learning ; Neural Networks (Computer) ; Radiography ; Triage
    Language English
    Publishing date 2018-11-13
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.2018182318
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  6. Article ; Online: FRODO: An In-Depth Analysis of a System to Reject Outlier Samples From a Trained Neural Network.

    Calli, Erdi / Van Ginneken, Bram / Sogancioglu, Ecem / Murphy, Keelin

    IEEE transactions on medical imaging

    2023  Volume 42, Issue 4, Page(s) 971–981

    Abstract: An important limitation of state-of-the-art deep learning networks is that they do not recognize when their input is dissimilar to the data on which they were trained and proceed to produce outputs that will be unreliable or nonsensical. In this work, we ...

    Abstract An important limitation of state-of-the-art deep learning networks is that they do not recognize when their input is dissimilar to the data on which they were trained and proceed to produce outputs that will be unreliable or nonsensical. In this work, we describe FRODO (Free Rejection of Out-of-Distribution), a publicly available method that can be easily employed for any trained network to detect input data from a different distribution than is expected. FRODO uses the statistical distribution of intermediate layer outputs to define the expected in-distribution (ID) input image properties. New samples are judged based on the Mahalanobis distance (MD) of their layer outputs from the defined distribution. The method can be applied to any network, and we demonstrate the performance of FRODO in correctly rejecting OOD samples on three distinct architectures for classification, localization, and segmentation tasks in chest X-rays. A dataset of 21,576 X-ray images with 3,655 in-distribution samples is defined for testing. The remaining images are divided into four OOD categories of varying levels of difficulty, and performance at rejecting each type is evaluated using receiver operating characteristic (ROC) analysis. FRODO achieves areas under the ROC (AUC) of between 0.815 and 0.999 in distinguishing OOD samples of different types. This is shown to be comparable with the best-performing state-of-the-art method tested, with the substantial advantage that FRODO integrates seamlessly with any network and requires no extra model to be constructed and trained.
    MeSH term(s) Neural Networks, Computer ; ROC Curve
    Language English
    Publishing date 2023-04-03
    Publishing country United States
    Document type Journal Article
    ZDB-ID 622531-7
    ISSN 1558-254X ; 0278-0062
    ISSN (online) 1558-254X
    ISSN 0278-0062
    DOI 10.1109/TMI.2022.3221898
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    Zs.A 2546: Show issues Location:
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  7. Article ; Online: Dense regression activation maps for lesion segmentation in CT scans of COVID-19 patients.

    Xie, Weiyi / Jacobs, Colin / Charbonnier, Jean-Paul / van Ginneken, Bram

    Medical image analysis

    2023  Volume 86, Page(s) 102771

    Abstract: Automatic lesion segmentation on thoracic CT enables rapid quantitative analysis of lung involvement in COVID-19 infections. However, obtaining a large amount of voxel-level annotations for training segmentation networks is prohibitively expensive. ... ...

    Abstract Automatic lesion segmentation on thoracic CT enables rapid quantitative analysis of lung involvement in COVID-19 infections. However, obtaining a large amount of voxel-level annotations for training segmentation networks is prohibitively expensive. Therefore, we propose a weakly-supervised segmentation method based on dense regression activation maps (dRAMs). Most weakly-supervised segmentation approaches exploit class activation maps (CAMs) to localize objects. However, because CAMs were trained for classification, they do not align precisely with the object segmentations. Instead, we produce high-resolution activation maps using dense features from a segmentation network that was trained to estimate a per-lobe lesion percentage. In this way, the network can exploit knowledge regarding the required lesion volume. In addition, we propose an attention neural network module to refine dRAMs, optimized together with the main regression task. We evaluated our algorithm on 90 subjects. Results show our method achieved 70.2% Dice coefficient, substantially outperforming the CAM-based baseline at 48.6%. We published our source code at https://github.com/DIAGNijmegen/bodyct-dram.
    MeSH term(s) Humans ; COVID-19/diagnostic imaging ; Neural Networks, Computer ; Tomography, X-Ray Computed/methods ; Algorithms ; Image Processing, Computer-Assisted/methods
    Language English
    Publishing date 2023-02-16
    Publishing country Netherlands
    Document type 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.2023.102771
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    Zs.A 5206: Show issues Location:
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  8. Article ; Online: Streaming Convolutional Neural Networks for End-to-End Learning With Multi-Megapixel Images.

    Pinckaers, Hans / van Ginneken, Bram / Litjens, Geert

    IEEE transactions on pattern analysis and machine intelligence

    2022  Volume 44, Issue 3, Page(s) 1581–1590

    Abstract: Due to memory constraints on current hardware, most convolution neural networks (CNN) are trained on sub-megapixel images. For example, most popular datasets in computer vision contain images much less than a megapixel in size (0.09MP for ImageNet and 0 ... ...

    Abstract Due to memory constraints on current hardware, most convolution neural networks (CNN) are trained on sub-megapixel images. For example, most popular datasets in computer vision contain images much less than a megapixel in size (0.09MP for ImageNet and 0.001MP for CIFAR-10). In some domains such as medical imaging, multi-megapixel images are needed to identify the presence of disease accurately. We propose a novel method to directly train convolutional neural networks using any input image size end-to-end. This method exploits the locality of most operations in modern convolutional neural networks by performing the forward and backward pass on smaller tiles of the image. In this work, we show a proof of concept using images of up to 66-megapixels (8192×8192), saving approximately 50GB of memory per image. Using two public challenge datasets, we demonstrate that CNNs can learn to extract relevant information from these large images and benefit from increasing resolution. We improved the area under the receiver-operating characteristic curve from 0.580 (4MP) to 0.706 (66MP) for metastasis detection in breast cancer (CAMELYON17). We also obtained a Spearman correlation metric approaching state-of-the-art performance on the TUPAC16 dataset, from 0.485 (1MP) to 0.570 (16MP). Code to reproduce a subset of the experiments is available at https://github.com/DIAGNijmegen/StreamingCNN.
    MeSH term(s) Algorithms ; Breast Neoplasms ; Diagnostic Imaging ; Female ; Humans ; Neural Networks, Computer
    Language English
    Publishing date 2022-02-03
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1939-3539
    ISSN (online) 1939-3539
    DOI 10.1109/TPAMI.2020.3019563
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  9. Article ; Online: In reply.

    Berg, Hidde Ten / van Bakel, Bram / van de Wouw, Lieke / Jie, Kim E / Schipper, Anoeska / Jansen, Henry / O'Connor, Rory D / van Ginneken, Bram / Kurstjens, Steef

    Annals of emergency medicine

    2023  Volume 83, Issue 3, Page(s) 287–288

    Language English
    Publishing date 2023-12-13
    Publishing country United States
    Document type Letter
    ZDB-ID 603080-4
    ISSN 1097-6760 ; 0196-0644
    ISSN (online) 1097-6760
    ISSN 0196-0644
    DOI 10.1016/j.annemergmed.2023.10.007
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  10. Article ; Online: Google's lung cancer AI: a promising tool that needs further validation.

    Jacobs, Colin / van Ginneken, Bram

    Nature reviews. Clinical oncology

    2019  Volume 16, Issue 9, Page(s) 532–533

    MeSH term(s) Deep Learning ; Early Detection of Cancer ; Humans ; Lung Neoplasms ; Tomography, X-Ray Computed
    Language English
    Publishing date 2019-06-27
    Publishing country England
    Document type Journal Article ; Comment
    ZDB-ID 2491410-1
    ISSN 1759-4782 ; 1759-4774
    ISSN (online) 1759-4782
    ISSN 1759-4774
    DOI 10.1038/s41571-019-0248-7
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