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  1. Article ; Online: A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes.

    Kazemi, Azar / Rasouli-Saravani, Ashkan / Gharib, Masoumeh / Albuquerque, Tomé / Eslami, Saeid / Schüffler, Peter J

    Computers in biology and medicine

    2024  Volume 173, Page(s) 108306

    Abstract: The incidence of colorectal cancer (CRC), one of the deadliest cancers around the world, is increasing. Tissue microenvironment (TME) features such as tumor-infiltrating lymphocytes (TILs) can have a crucial impact on diagnosis or decision-making for ... ...

    Abstract The incidence of colorectal cancer (CRC), one of the deadliest cancers around the world, is increasing. Tissue microenvironment (TME) features such as tumor-infiltrating lymphocytes (TILs) can have a crucial impact on diagnosis or decision-making for treating patients with CRC. While clinical studies showed that TILs improve the host immune response, leading to a better prognosis, inter-observer agreement for quantifying TILs is not perfect. Incorporating machine learning (ML) based applications in clinical routine may promote diagnosis reliability. Recently, ML has shown potential for making progress in routine clinical procedures. We aim to systematically review the TILs analysis based on ML in CRC histological images. Deep learning (DL) and non-DL techniques can aid pathologists in identifying TILs, and automated TILs are associated with patient outcomes. However, a large multi-institutional CRC dataset with a diverse and multi-ethnic population is necessary to generalize ML methods.
    MeSH term(s) Humans ; Lymphocytes, Tumor-Infiltrating/pathology ; Reproducibility of Results ; Colorectal Neoplasms/diagnosis ; Colorectal Neoplasms/pathology ; Tumor Microenvironment
    Language English
    Publishing date 2024-03-13
    Publishing country United States
    Document type Systematic Review ; Journal Article ; Review
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2024.108306
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Unimodal Distributions for Ordinal Regression

    Cardoso, Jaime S. / Cruz, Ricardo / Albuquerque, Tomé

    2023  

    Abstract: In many real-world prediction tasks, class labels contain information about the relative order between labels that are not captured by commonly used loss functions such as multicategory cross-entropy. Recently, the preference for unimodal distributions ... ...

    Abstract In many real-world prediction tasks, class labels contain information about the relative order between labels that are not captured by commonly used loss functions such as multicategory cross-entropy. Recently, the preference for unimodal distributions in the output space has been incorporated into models and loss functions to account for such ordering information. However, current approaches rely on heuristics that lack a theoretical foundation. Here, we propose two new approaches to incorporate the preference for unimodal distributions into the predictive model. We analyse the set of unimodal distributions in the probability simplex and establish fundamental properties. We then propose a new architecture that imposes unimodal distributions and a new loss term that relies on the notion of projection in a set to promote unimodality. Experiments show the new architecture achieves top-2 performance, while the proposed new loss term is very competitive while maintaining high unimodality.

    Comment: 6 pages
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-03-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Ordinal losses for classification of cervical cancer risk.

    Albuquerque, Tomé / Cruz, Ricardo / Cardoso, Jaime S

    PeerJ. Computer science

    2021  Volume 7, Page(s) e457

    Abstract: Cervical cancer is the fourth leading cause of cancer-related deaths in women, especially in low to middle-income countries. Despite the outburst of recent scientific advances, there is no totally effective treatment, especially when diagnosed in an ... ...

    Abstract Cervical cancer is the fourth leading cause of cancer-related deaths in women, especially in low to middle-income countries. Despite the outburst of recent scientific advances, there is no totally effective treatment, especially when diagnosed in an advanced stage. Screening tests, such as cytology or colposcopy, have been responsible for a substantial decrease in cervical cancer deaths. Cervical cancer automatic screening via Pap smear is a highly valuable cell imaging-based detection tool, where cells must be classified as being within one of a multitude of ordinal classes, ranging from abnormal to normal. Current approaches to ordinal inference for neural networks are found to not sufficiently take advantage of the ordinal problem or to be too uncompromising. A non-parametric ordinal loss for neuronal networks is proposed that promotes the output probabilities to follow a unimodal distribution. This is done by imposing a set of different constraints over all pairs of consecutive labels which allows for a more flexible decision boundary relative to approaches from the literature. Our proposed loss is contrasted against other methods from the literature by using a plethora of deep architectures. A first conclusion is the benefit of using non-parametric ordinal losses against parametric losses in cervical cancer risk prediction. Additionally, the proposed loss is found to be the top-performer in several cases. The best performing model scores an accuracy of 75.6% for seven classes and 81.3% for four classes.
    Language English
    Publishing date 2021-04-23
    Publishing country United States
    Document type Journal Article
    ISSN 2376-5992
    ISSN (online) 2376-5992
    DOI 10.7717/peerj-cs.457
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Quality Control in Digital Pathology: Automatic Fragment Detection and Counting.

    Albuquerque, Tome / Moreira, Ana / Barros, Beatriz / Montezuma, Diana / Oliveira, Sara P / Neto, Pedro C / Monteiro, Joao / Ribeiro, Liliana / Goncalves, Sofia / Monteiro, Ana / Pinto, Isabel M / Cardoso, Jaime S

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2022  Volume 2022, Page(s) 588–593

    Abstract: Manual assessment of fragments during the pro-cessing of pathology specimens is critical to ensure that the material available for slide analysis matches that captured during grossing without losing valuable material during this process. However, this ... ...

    Abstract Manual assessment of fragments during the pro-cessing of pathology specimens is critical to ensure that the material available for slide analysis matches that captured during grossing without losing valuable material during this process. However, this step is still performed manually, resulting in lost time and delays in making the complete case available for evaluation by the pathologist. To overcome this limitation, we developed an autonomous system that can detect and count the number of fragments contained on each slide. We applied and compared two different methods: conventional machine learning methods and deep convolutional network methods. For conventional machine learning methods, we tested a two-stage approach with a supervised classifier followed by unsupervised hierarchical clustering. In addition, Fast R-CNN and YOLOv5, two state-of-the-art deep learning models for detection, were used and compared. All experiments were performed on a dataset comprising 1276 images of colorec-tal biopsy and polypectomy specimens manually labeled for fragment/set detection. The best results were obtained with the YOLOv5 architecture with a map@0.5 of 0.977 for fragment/set detection.
    MeSH term(s) Biopsy ; Machine Learning ; Neural Networks, Computer ; Quality Control
    Language English
    Publishing date 2022-09-09
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC48229.2022.9871208
    Database MEDical Literature Analysis and Retrieval System OnLINE

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