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  1. Article ; Online: Topographic mapping of the glioblastoma proteome reveals a triple-axis model of intra-tumoral heterogeneity

    K. H. Brian Lam / Alberto J. Leon / Weili Hui / Sandy Che-Eun Lee / Ihor Batruch / Kevin Faust / Almos Klekner / Gábor Hutóczki / Marianne Koritzinsky / Maxime Richer / Ugljesa Djuric / Phedias Diamandis

    Nature Communications, Vol 13, Iss 1, Pp 1-

    2022  Volume 14

    Abstract: Gioblastoma tumours consist of different niches defined by histology. Here, the authors use proteomics and machine learning to assign protein expression programs to these niches, and reveal that KRAS and hypoxia are associated with drug resistance. ...

    Abstract Gioblastoma tumours consist of different niches defined by histology. Here, the authors use proteomics and machine learning to assign protein expression programs to these niches, and reveal that KRAS and hypoxia are associated with drug resistance.
    Keywords Science ; Q
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction

    Kevin Faust / Quin Xie / Dominick Han / Kartikay Goyle / Zoya Volynskaya / Ugljesa Djuric / Phedias Diamandis

    BMC Bioinformatics, Vol 19, Iss 1, Pp 1-

    2018  Volume 15

    Abstract: Abstract Background There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks ( ... ...

    Abstract Abstract Background There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce. Results Here, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by a priori statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on post-hoc tuning. Conclusion Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered.
    Keywords Digital pathology ; Deep learning ; Convolutional neural networks ; t-SNE ; Diagnostics ; Neuropathology ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
    Subject code 006
    Language English
    Publishing date 2018-05-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Physician perspectives on integration of artificial intelligence into diagnostic pathology

    Shihab Sarwar / Anglin Dent / Kevin Faust / Maxime Richer / Ugljesa Djuric / Randy Van Ommeren / Phedias Diamandis

    npj Digital Medicine, Vol 2, Iss 1, Pp 1-

    2019  Volume 7

    Abstract: Abstract Advancements in computer vision and artificial intelligence (AI) carry the potential to make significant contributions to health care, particularly in diagnostic specialties such as radiology and pathology. The impact of these technologies on ... ...

    Abstract Abstract Advancements in computer vision and artificial intelligence (AI) carry the potential to make significant contributions to health care, particularly in diagnostic specialties such as radiology and pathology. The impact of these technologies on physician stakeholders is the subject of significant speculation. There is however a dearth of information regarding the opinions, enthusiasm, and concerns of the pathology community at large. Here, we report results from a survey of 487 pathologist-respondents practicing in 54 countries, conducted to examine perspectives on AI implementation in clinical practice. Despite limitations, including difficulty with quantifying response bias and verifying identity of respondents to this anonymous and voluntary survey, several interesting findings were uncovered. Overall, respondents carried generally positive attitudes towards AI, with nearly 75% reporting interest or excitement in AI as a diagnostic tool to facilitate improvements in workflow efficiency and quality assurance in pathology. Importantly, even within the more optimistic cohort, a significant number of respondents endorsed concerns about AI, including the potential for job displacement and replacement. Overall, around 80% of respondents predicted the introduction of AI technology in the pathology laboratory within the coming decade. Attempts to identify statistically significant demographic characteristics (e.g., age, sex, type/place of practice) predictive of attitudes towards AI using Kolmogorov–Smirnov (KS) testing revealed several associations. Important themes which were commented on by respondents included the need for increasing efforts towards physician training and resolving medical-legal implications prior to the generalized implementation of AI in pathology.
    Keywords Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 941
    Language English
    Publishing date 2019-04-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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