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  1. Article ; Online: Editorial: AI applications for diagnosis of breast cancer.

    Muhammad, L J / Bria, Alessandro

    Frontiers in artificial intelligence

    2023  Volume 6, Page(s) 1247261

    Language English
    Publishing date 2023-10-17
    Publishing country Switzerland
    Document type Editorial
    ISSN 2624-8212
    ISSN (online) 2624-8212
    DOI 10.3389/frai.2023.1247261
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: GravityNet for end-to-end small lesion detection.

    Russo, Ciro / Bria, Alessandro / Marrocco, Claudio

    Artificial intelligence in medicine

    2024  Volume 150, Page(s) 102842

    Abstract: This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which ...

    Abstract This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which they are found. To address this, our approach introduces a new type of pixel-based anchor that dynamically moves towards the targeted lesion for detection. We refer to this new architecture as GravityNet, and the novel anchors as gravity points since they appear to be "attracted" by the lesions. We conducted experiments on two well-established medical problems involving small lesions to evaluate the performance of the proposed approach: microcalcifications detection in digital mammograms and microaneurysms detection in digital fundus images. Our method demonstrates promising results in effectively detecting small lesions in these medical imaging tasks.
    MeSH term(s) Mammography/methods ; Fundus Oculi
    Language English
    Publishing date 2024-03-13
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 645179-2
    ISSN 1873-2860 ; 0933-3657
    ISSN (online) 1873-2860
    ISSN 0933-3657
    DOI 10.1016/j.artmed.2024.102842
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Convolutional Networks and Transformers for Mammography Classification: An Experimental Study.

    Cantone, Marco / Marrocco, Claudio / Tortorella, Francesco / Bria, Alessandro

    Sensors (Basel, Switzerland)

    2023  Volume 23, Issue 3

    Abstract: Convolutional Neural Networks (CNN) have received a large share of research in mammography image analysis due to their capability of extracting hierarchical features directly from raw data. Recently, Vision Transformers are emerging as viable alternative ...

    Abstract Convolutional Neural Networks (CNN) have received a large share of research in mammography image analysis due to their capability of extracting hierarchical features directly from raw data. Recently, Vision Transformers are emerging as viable alternative to CNNs in medical imaging, in some cases performing on par or better than their convolutional counterparts. In this work, we conduct an extensive experimental study to compare the most recent CNN and Vision Transformer architectures for whole mammograms classification. We selected, trained and tested 33 different models, 19 convolutional- and 14 transformer-based, on the largest publicly available mammography image database OMI-DB. We also performed an analysis of the performance at eight different image resolutions and considering all the individual lesion categories in isolation (masses, calcifications, focal asymmetries, architectural distortions). Our findings confirm the potential of visual transformers, which performed on par with traditional CNNs like ResNet, but at the same time show a superiority of modern convolutional networks like EfficientNet.
    MeSH term(s) Mammography/methods ; Neural Networks, Computer ; Image Processing, Computer-Assisted/methods ; Databases, Factual
    Language English
    Publishing date 2023-01-20
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s23031229
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  4. Article ; Online: Learnable DoG convolutional filters for microcalcification detection.

    Cantone, Marco / Marrocco, Claudio / Tortorella, Francesco / Bria, Alessandro

    Artificial intelligence in medicine

    2023  Volume 143, Page(s) 102629

    Abstract: Difference of Gaussians (DoG) convolutional filters are one of the earliest image processing methods employed for detecting microcalcifications on mammogram images before machine and deep learning methods became widespread. DoG is a blob enhancement ... ...

    Abstract Difference of Gaussians (DoG) convolutional filters are one of the earliest image processing methods employed for detecting microcalcifications on mammogram images before machine and deep learning methods became widespread. DoG is a blob enhancement filter that consists in subtracting one Gaussian-smoothed version of an image from another less Gaussian-smoothed version of the same image. Smoothing with a Gaussian kernel suppresses high-frequency spatial information, thus DoG can be regarded as a band-pass filter. However, due to their small size and overimposed breast tissue, microcalcifications vary greatly in contrast-to-noise ratio and sharpness. This makes it difficult to find a single DoG configuration that enhances all microcalcifications. In this work, we propose a convolutional network, named DoG-MCNet, where the first layer automatically learns a bank of DoG filters parameterized by their associated standard deviations. We experimentally show that when employed for microcalcification detection, our DoG layer acts as a learnable bank of band-pass preprocessing filters and improves detection performance by 4.86% AUFROC over baseline MCNet and 1.53% AUFROC over state-of-the-art multicontext ensemble of CNNs.
    MeSH term(s) Humans ; Algorithms ; Calcinosis/diagnostic imaging ; Mammography ; Normal Distribution
    Language English
    Publishing date 2023-07-22
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 645179-2
    ISSN 1873-2860 ; 0933-3657
    ISSN (online) 1873-2860
    ISSN 0933-3657
    DOI 10.1016/j.artmed.2023.102629
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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  5. Book ; Online: Gravity Network for end-to-end small lesion detection

    Russo, Ciro / Bria, Alessandro / Marrocco, Claudio

    2023  

    Abstract: This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which ...

    Abstract This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which they are found. To address this, our approach introduces a new type of pixel-based anchor that dynamically moves towards the targeted lesion for detection. We refer to this new architecture as GravityNet, and the novel anchors as gravity points since they appear to be "attracted" by the lesions. We conducted experiments on two well-established medical problems involving small lesions to evaluate the performance of the proposed approach: microcalcifications detection in digital mammograms and microaneurysms detection in digital fundus images. Our method demonstrates promising results in effectively detecting small lesions in these medical imaging tasks.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-09-22
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Addressing class imbalance in deep learning for small lesion detection on medical images.

    Bria, Alessandro / Marrocco, Claudio / Tortorella, Francesco

    Computers in biology and medicine

    2020  Volume 120, Page(s) 103735

    Abstract: Deep learning methods utilizing Convolutional Neural Networks (CNNs) have led to dramatic advances in automated understanding of medical images. However, in many medical image classification tasks, lesions occupy only a few pixels of the image. This ... ...

    Abstract Deep learning methods utilizing Convolutional Neural Networks (CNNs) have led to dramatic advances in automated understanding of medical images. However, in many medical image classification tasks, lesions occupy only a few pixels of the image. This results in a significant class imbalance between lesion and background. From recent literature, it is known that class imbalance may negatively affect the performance of CNN classification. However, very few research exists in the context of lesion detection. In this work, we propose a two-stage deep learning framework able to deal with the high class imbalance encountered during training of small lesion detectors. First, we train a deep cascade (DC) of long sequences of decision trees with an algorithm designed to handle unbalanced data that also drastically reduces the number of background samples reaching the final stage. The remaining samples are fed to a CNN, whose training benefits from both rebalance and hard mining done by the DC. We evaluated DC-CNN on two severely unbalanced classification problems: microcalcification detection and microaneurysm detection. In both cases, DC-CNN outperformed the CNNs trained with commonly used methods for addressing class imbalance such as oversampling, undersampling, hard mining, cost sensitive learning, and one-class classification. The DC-CNN was also ∼10x faster than CNN at test time.
    MeSH term(s) Algorithms ; Deep Learning ; Neural Networks, Computer
    Language English
    Publishing date 2020-04-01
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2020.103735
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 653: Show issues Location:
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  7. Article: Exploiting Multi-Level Parallelism for Stitching Very Large Microscopy Images.

    Bria, Alessandro / Bernaschi, Massimo / Guarrasi, Massimiliano / Iannello, Giulio

    Frontiers in neuroinformatics

    2019  Volume 13, Page(s) 41

    Abstract: Due to the limited field of view of the microscopes, acquisitions of macroscopic specimens require many parallel image stacks to cover the whole volume of interest. Overlapping regions are introduced among stacks in order to make it possible automatic ... ...

    Abstract Due to the limited field of view of the microscopes, acquisitions of macroscopic specimens require many parallel image stacks to cover the whole volume of interest. Overlapping regions are introduced among stacks in order to make it possible automatic alignment by means of a 3D stitching tool. Since state-of-the-art microscopes coupled with chemical clearing procedures can generate 3D images whose size exceeds the Terabyte, parallelization is required to keep stitching time within acceptable limits. In the present paper we discuss how multi-level parallelization reduces the execution times of TeraStitcher, a tool designed to deal with very large images. Two algorithms performing dataset partition for efficient parallelization in a transparent way are presented together with experimental results proving the effectiveness of the approach that achieves a speedup close to 300×, when both coarse- and fine-grained parallelism are exploited. Multi-level parallelization of TeraStitcher led to a significant reduction of processing times with no changes in the user interface, and with no additional effort required for the maintenance of code.
    Language English
    Publishing date 2019-06-04
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2452979-5
    ISSN 1662-5196
    ISSN 1662-5196
    DOI 10.3389/fninf.2019.00041
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Book ; Online: Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images

    Elbatel, Marawan / Bornberg, Christina / Kattel, Manasi / Almar, Enrique / Marrocco, Claudio / Bria, Alessandro

    2022  

    Abstract: In-vitro tests are an alternative to animal testing for the toxicity of medical devices. Detecting cells as a first step, a cell expert evaluates the growth of cells according to cytotoxicity grade under the microscope. Thus, human fatigue plays a role ... ...

    Abstract In-vitro tests are an alternative to animal testing for the toxicity of medical devices. Detecting cells as a first step, a cell expert evaluates the growth of cells according to cytotoxicity grade under the microscope. Thus, human fatigue plays a role in error making, making the use of deep learning appealing. Due to the high cost of training data annotation, an approach without manual annotation is needed. We propose Seamless Iterative Semi-Supervised correction of Imperfect labels (SISSI), a new method for training object detection models with noisy and missing annotations in a semi-supervised fashion. Our network learns from noisy labels generated with simple image processing algorithms, which are iteratively corrected during self-training. Due to the nature of missing bounding boxes in the pseudo labels, which would negatively affect the training, we propose to train on dynamically generated synthetic-like images using seamless cloning. Our method successfully provides an adaptive early learning correction technique for object detection. The combination of early learning correction that has been applied in classification and semantic segmentation before and synthetic-like image generation proves to be more effective than the usual semi-supervised approach by > 15% AP and > 20% AR across three different readers. Our code is available at https://github.com/marwankefah/SISSI.

    Comment: To appear at MICCAI 2022 Workshop on Domain Adaptation and Representation Transfer (DART)
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2022-08-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: TeraStitcher - A tool for fast automatic 3D-stitching of teravoxel-sized microscopy images

    Bria Alessandro / Iannello Giulio

    BMC Bioinformatics, Vol 13, Iss 1, p

    2012  Volume 316

    Abstract: Abstract Background Further advances in modern microscopy are leading to teravoxel-sized tiled 3D images at high resolution, thus increasing the dimension of the stitching problem of at least two orders of magnitude. The existing software solutions do ... ...

    Abstract Abstract Background Further advances in modern microscopy are leading to teravoxel-sized tiled 3D images at high resolution, thus increasing the dimension of the stitching problem of at least two orders of magnitude. The existing software solutions do not seem adequate to address the additional requirements arising from these datasets, such as the minimization of memory usage and the need to process just a small portion of data. Results We propose a free and fully automated 3D Stitching tool designed to match the special requirements coming out of teravoxel-sized tiled microscopy images that is able to stitch them in a reasonable time even on workstations with limited resources. The tool was tested on teravoxel-sized whole mouse brain images with micrometer resolution and it was also compared with the state-of-the-art stitching tools on megavoxel-sized publicy available datasets. This comparison confirmed that the solutions we adopted are suited for stitching very large images and also perform well on datasets with different characteristics. Indeed, some of the algorithms embedded in other stitching tools could be easily integrated in our framework if they turned out to be more effective on other classes of images. To this purpose, we designed a software architecture which separates the strategies that use efficiently memory resources from the algorithms which may depend on the characteristics of the acquired images. Conclusions TeraStitcher is a free tool that enables the stitching of Teravoxel-sized tiled microscopy images even on workstations with relatively limited resources of memory (<8 GB) and processing power. It exploits the knowledge of approximate tile positions and uses ad-hoc strategies and algorithms designed for such very large datasets. The produced images can be saved into a multiresolution representation to be efficiently retrieved and processed. We provide TeraStitcher both as standalone application and as plugin of the free software Vaa3D.
    Keywords Stitching ; 3D images ; Teravoxel-sized images ; Microscopy ; Brain imaging ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
    Subject code 004
    Language English
    Publishing date 2012-11-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: TeraFly: real-time three-dimensional visualization and annotation of terabytes of multidimensional volumetric images.

    Bria, Alessandro / Iannello, Giulio / Onofri, Leonardo / Peng, Hanchuan

    Nature methods

    2016  Volume 13, Issue 3, Page(s) 192–194

    MeSH term(s) Algorithms ; Computer Graphics ; Databases, Factual ; Datasets as Topic ; Documentation/methods ; Imaging, Three-Dimensional/methods ; Information Storage and Retrieval/methods ; Microscopy/methods ; Software ; User-Computer Interface
    Language English
    Publishing date 2016-03
    Publishing country United States
    Document type Letter
    ZDB-ID 2169522-2
    ISSN 1548-7105 ; 1548-7091
    ISSN (online) 1548-7105
    ISSN 1548-7091
    DOI 10.1038/nmeth.3767
    Database MEDical Literature Analysis and Retrieval System OnLINE

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