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  1. Article ; Online: Clinically Relevant Features for Predicting the Severity of Surgical Site Infections.

    Boubekki, Ahcene / Myhre, Jonas Nordhaug / Luppino, Luigi Tommaso / Mikalsen, Karl Oyvind / Revhaug, Arthur / Jenssen, Robert

    IEEE journal of biomedical and health informatics

    2022  Volume 26, Issue 4, Page(s) 1794–1801

    Abstract: Surgical site infections are hospital-acquired infections resulting in severe risk for patients and significantly increased costs for healthcare providers. In this work, we show how to leverage irregularly sampled preoperative blood tests to predict, on ... ...

    Abstract Surgical site infections are hospital-acquired infections resulting in severe risk for patients and significantly increased costs for healthcare providers. In this work, we show how to leverage irregularly sampled preoperative blood tests to predict, on the day of surgery, a future surgical site infection and its severity. Our dataset is extracted from the electronic health records of patients who underwent gastrointestinal surgery and developed either deep, shallow or no infection. We represent the patients using the concentrations of fourteen common blood components collected over the four weeks preceding the surgery partitioned into six time windows. A gradient boosting based classifier trained on our new set of features reports an AUROC of 0.991 for predicting a postoperative infection and and AUROC of 0.937 for classifying the severity of the infection. Further analyses support the clinical relevance of our approach as the most important features describe the nutritional status and the liver function over the two weeks prior to surgery.
    MeSH term(s) Electronic Health Records ; Forecasting ; Humans ; Risk Factors ; Surgical Wound Infection/diagnosis
    Language English
    Publishing date 2022-04-14
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2695320-1
    ISSN 2168-2208 ; 2168-2194
    ISSN (online) 2168-2208
    ISSN 2168-2194
    DOI 10.1109/JBHI.2021.3121038
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images.

    Luppino, Luigi Tommaso / Hansen, Mads Adrian / Kampffmeyer, Michael / Bianchi, Filippo Maria / Moser, Gabriele / Jenssen, Robert / Anfinsen, Stian Normann

    IEEE transactions on neural networks and learning systems

    2022  Volume PP

    Abstract: Image translation with convolutional autoencoders has recently been used as an approach to multimodal change detection (CD) in bitemporal satellite images. A main challenge is the alignment of the code spaces by reducing the contribution of change pixels ...

    Abstract Image translation with convolutional autoencoders has recently been used as an approach to multimodal change detection (CD) in bitemporal satellite images. A main challenge is the alignment of the code spaces by reducing the contribution of change pixels to the learning of the translation function. Many existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. We propose to extract relational pixel information captured by domain-specific affinity matrices at the input and use this to enforce alignment of the code spaces and reduce the impact of change pixels on the learning objective. A change prior is derived in an unsupervised fashion from pixel pair affinities that are comparable across domains. To achieve code space alignment, we enforce pixels with similar affinity relations in the input domains to be correlated also in code space. We demonstrate the utility of this procedure in combination with cycle consistency. The proposed approach is compared with the state-of-the-art machine learning and deep learning algorithms. Experiments conducted on four real and representative datasets show the effectiveness of our methodology.
    Language English
    Publishing date 2022-05-12
    Publishing country United States
    Document type Journal Article
    ISSN 2162-2388
    ISSN (online) 2162-2388
    DOI 10.1109/TNNLS.2022.3172183
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI.

    Cepeda, Santiago / Luppino, Luigi Tommaso / Pérez-Núñez, Angel / Solheim, Ole / García-García, Sergio / Velasco-Casares, María / Karlberg, Anna / Eikenes, Live / Sarabia, Rosario / Arrese, Ignacio / Zamora, Tomás / Gonzalez, Pedro / Jiménez-Roldán, Luis / Kuttner, Samuel

    Cancers

    2023  Volume 15, Issue 6

    Abstract: The globally accepted surgical strategy in glioblastomas is removing the enhancing tumor. However, the peritumoral region harbors infiltration areas responsible for future tumor recurrence. This study aimed to evaluate a predictive model that identifies ... ...

    Abstract The globally accepted surgical strategy in glioblastomas is removing the enhancing tumor. However, the peritumoral region harbors infiltration areas responsible for future tumor recurrence. This study aimed to evaluate a predictive model that identifies areas of future recurrence using a voxel-based radiomics analysis of magnetic resonance imaging (MRI) data. This multi-institutional study included a retrospective analysis of patients diagnosed with glioblastoma who underwent surgery with complete resection of the enhancing tumor. Fifty-five patients met the selection criteria. The study sample was split into training (N = 40) and testing (N = 15) datasets. Follow-up MRI was used for ground truth definition, and postoperative structural multiparametric MRI was used to extract voxel-based radiomic features. Deformable coregistration was used to register the MRI sequences for each patient, followed by segmentation of the peritumoral region in the postoperative scan and the enhancing tumor in the follow-up scan. Peritumoral voxels overlapping with enhancing tumor voxels were labeled as recurrence, while non-overlapping voxels were labeled as nonrecurrence. Voxel-based radiomic features were extracted from the peritumoral region. Four machine learning-based classifiers were trained for recurrence prediction. A region-based evaluation approach was used for model evaluation. The Categorical Boosting (CatBoost) classifier obtained the best performance on the testing dataset with an average area under the curve (AUC) of 0.81 ± 0.09 and an accuracy of 0.84 ± 0.06, using region-based evaluation. There was a clear visual correspondence between predicted and actual recurrence regions. We have developed a method that accurately predicts the region of future tumor recurrence in MRI scans of glioblastoma patients. This could enable the adaptation of surgical and radiotherapy treatment to these areas to potentially prolong the survival of these patients.
    Language English
    Publishing date 2023-03-22
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2527080-1
    ISSN 2072-6694
    ISSN 2072-6694
    DOI 10.3390/cancers15061894
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection

    Luppino, Luigi Tommaso / Kampffmeyer, Michael / Bianchi, Filippo Maria / Moser, Gabriele / Serpico, Sebastiano Bruno / Jenssen, Robert / Anfinsen, Stian Normann

    2020  

    Abstract: Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection. Existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always ... ...

    Abstract Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection. Existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. A main challenge in the unsupervised problem setting is to avoid that change pixels affect the learning of the translation function. We propose two new network architectures trained with loss functions weighted by priors that reduce the impact of change pixels on the learning objective. The change prior is derived in an unsupervised fashion from relational pixel information captured by domain-specific affinity matrices. Specifically, we use the vertex degrees associated with an absolute affinity difference matrix and demonstrate their utility in combination with cycle consistency and adversarial training. The proposed neural networks are compared with state-of-the-art algorithms. Experiments conducted on three real datasets show the effectiveness of our methodology.
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2020-01-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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