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  1. Article ; Online: Augmented reality microscopes for cancer histopathology.

    Razavian, Narges

    Nature medicine

    2019  Volume 25, Issue 9, Page(s) 1334–1336

    MeSH term(s) Humans ; Microscopy/trends ; Neoplasms/diagnostic imaging ; Neoplasms/pathology ; User-Computer Interface
    Language English
    Publishing date 2019-09-09
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1220066-9
    ISSN 1546-170X ; 1078-8956
    ISSN (online) 1546-170X
    ISSN 1078-8956
    DOI 10.1038/s41591-019-0574-4
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    Zs.A 4212: Show issues Location:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 39: Show issues

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  2. Article: On gaps of clinical diagnosis of dementia subtypes: A study of Alzheimer's disease and Lewy body disease.

    Wei, Hui / Masurkar, Arjun V / Razavian, Narges

    Frontiers in aging neuroscience

    2023  Volume 15, Page(s) 1149036

    Abstract: Introduction: Alzheimer's disease (AD) and Lewy body disease (LBD) are the two most common neurodegenerative dementias and can occur in combination (AD+LBD). Due to overlapping biomarkers and symptoms, clinical differentiation of these subtypes could be ...

    Abstract Introduction: Alzheimer's disease (AD) and Lewy body disease (LBD) are the two most common neurodegenerative dementias and can occur in combination (AD+LBD). Due to overlapping biomarkers and symptoms, clinical differentiation of these subtypes could be difficult. However, it is unclear how the magnitude of diagnostic uncertainty varies across dementia spectra and demographic variables. We aimed to compare clinical diagnosis and post-mortem autopsy-confirmed pathological results to assess the clinical subtype diagnosis quality across these factors.
    Methods: We studied data of 1,920 participants recorded by the National Alzheimer's Coordinating Center from 2005 to 2019. Selection criteria included autopsy-based neuropathological assessments for AD and LBD, and the initial visit with Clinical Dementia Rating (CDR) stage of normal, mild cognitive impairment, or mild dementia. Longitudinally, we analyzed the first visit at each subsequent CDR stage. This analysis included positive predictive values, specificity, sensitivity and false negative rates of clinical diagnosis, as well as disparities by sex, race, age, and education. If autopsy-confirmed AD and/or LBD was missed in the clinic, the alternative clinical diagnosis was analyzed.
    Findings: In our findings, clinical diagnosis of AD+LBD had poor sensitivities. Over 61% of participants with autopsy-confirmed AD+LBD were diagnosed clinically as AD. Clinical diagnosis of AD had a low sensitivity at the early dementia stage and low specificities at all stages. Among participants diagnosed as AD in the clinic, over 32% had concurrent LBD neuropathology at autopsy. Among participants diagnosed as LBD, 32% to 54% revealed concurrent autopsy-confirmed AD pathology. When three subtypes were missed by clinicians, "No cognitive impairment" and "primary progressive aphasia or behavioral variant frontotemporal dementia" were the leading primary etiologic clinical diagnoses. With increasing dementia stages, the clinical diagnosis accuracy of black participants became significantly worse than other races, and diagnosis quality significantly improved for males but not females.
    Discussion: These findings demonstrate that clinical diagnosis of AD, LBD, and AD+LBD are inaccurate and suffer from significant disparities on race and sex. They provide important implications for clinical management, anticipatory guidance, trial enrollment and applicability of potential therapies for AD, and promote research into better biomarker-based assessment of LBD pathology.
    Language English
    Publishing date 2023-03-21
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2558898-9
    ISSN 1663-4365
    ISSN 1663-4365
    DOI 10.3389/fnagi.2023.1149036
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  3. Article ; Online: Author Correction: Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs.

    Liu, Sheng / Masurkar, Arjun V / Rusinek, Henry / Chen, Jingyun / Zhang, Ben / Zhu, Weicheng / Fernandez-Granda, Carlos / Razavian, Narges

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 16528

    Language English
    Publishing date 2023-10-02
    Publishing country England
    Document type Published Erratum
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-43726-2
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  4. Article ; Online: Deep multi-task learning and random forest for series classification by pulse sequence type and orientation.

    Kasmanoff, Noah / Lee, Matthew D / Razavian, Narges / Lui, Yvonne W

    Neuroradiology

    2022  Volume 65, Issue 1, Page(s) 77–87

    Abstract: Purpose: Increasingly complex MRI studies and variable series naming conventions reveal limitations of rule-based image routing, especially in health systems with multiple scanners and sites. Accurate methods to identify series based on image content ... ...

    Abstract Purpose: Increasingly complex MRI studies and variable series naming conventions reveal limitations of rule-based image routing, especially in health systems with multiple scanners and sites. Accurate methods to identify series based on image content would aid post-processing and PACS viewing. Recent deep/machine learning efforts classify 5-8 basic brain MR sequences. We present an ensemble model combining a convolutional neural network and a random forest classifier to differentiate 25 brain sequences and image orientation.
    Methods: Series were grouped by descriptions into 25 sequences and 4 orientations. Dataset A, obtained from our institution, was divided into training (16,828 studies; 48,512 series; 112,028 images), validation (4746 studies; 16,612 series; 26,222 images) and test sets (6348 studies; 58,705 series; 3,314,018 images). Dataset B, obtained from a separate hospital, was used for out-of-domain external validation (1252 studies; 2150 series; 234,944 images). We developed an ensemble model combining a 2D convolutional neural network with a custom multi-task learning architecture and random forest classifier trained on DICOM metadata to classify sequence and orientation by series.
    Results: The neural network, random forest, and ensemble achieved 95%, 97%, and 98% overall sequence accuracy on dataset A, and 98%, 99%, and 99% accuracy on dataset B, respectively. All models achieved > 99% orientation accuracy on both datasets.
    Conclusion: The ensemble model for series identification accommodates the complexity of brain MRI studies in state-of-the-art clinical practice. Expanding on previous work demonstrating proof-of-concept, our approach is more comprehensive with greater sequence diversity and orientation classification.
    MeSH term(s) Humans ; Random Forest ; Neural Networks, Computer ; Magnetic Resonance Imaging ; Brain/diagnostic imaging
    Language English
    Publishing date 2022-07-30
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 123305-1
    ISSN 1432-1920 ; 0028-3940
    ISSN (online) 1432-1920
    ISSN 0028-3940
    DOI 10.1007/s00234-022-03023-7
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    Zs.A 658: Show issues Location:
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  5. Book ; Online: Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of Clinical MRI Scans

    Chen, Long / Chen, Liben / Xu, Binfeng / Zhang, Wenxin / Razavian, Narges

    2023  

    Abstract: The aging population of the U.S. drives the prevalence of Alzheimer's disease. Brookmeyer et al. forecasts approximately 15 million Americans will have either clinical AD or mild cognitive impairment by 2060. In response to this urgent call, methods for ... ...

    Abstract The aging population of the U.S. drives the prevalence of Alzheimer's disease. Brookmeyer et al. forecasts approximately 15 million Americans will have either clinical AD or mild cognitive impairment by 2060. In response to this urgent call, methods for early detection of Alzheimer's disease have been developed for prevention and pre-treatment. Notably, literature on the application of deep learning in the automatic detection of the disease has been proliferating. This study builds upon previous literature and maintains a focus on leveraging multi-modal information to enhance automatic detection. We aim to predict the stage of the disease - Cognitively Normal (CN), Mildly Cognitive Impairment (MCI), and Alzheimer's Disease (AD), based on two different types of brain MRI scans. We design an AlexNet-based deep learning model that learns the synergy of complementary information from both T1 and FLAIR MRI scans.
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2023-11-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling

    Zhu, Weicheng / Liu, Sheng / Fernandez-Granda, Carlos / Razavian, Narges

    2023  

    Abstract: Self-supervised learning (SSL) has emerged as a powerful technique for learning rich representations from unlabeled data. The data representations are able to capture many underlying attributes of data, and be useful in downstream prediction tasks. In ... ...

    Abstract Self-supervised learning (SSL) has emerged as a powerful technique for learning rich representations from unlabeled data. The data representations are able to capture many underlying attributes of data, and be useful in downstream prediction tasks. In real-world settings, spurious correlations between some attributes (e.g. race, gender and age) and labels for downstream tasks often exist, e.g. cancer is usually more prevalent among elderly patients. In this paper, we investigate SSL in the presence of spurious correlations and show that the SSL training loss can be minimized by capturing only a subset of the conspicuous features relevant to those sensitive attributes, despite the presence of other important predictive features for the downstream tasks. To address this issue, we investigate the learning dynamics of SSL and observe that the learning is slower for samples that conflict with such correlations (e.g. elder patients without cancer). Motivated by these findings, we propose a learning-speed aware SSL (LA-SSL) approach, in which we sample each training data with a probability that is inversely related to its learning speed. We evaluate LA-SSL on three datasets that exhibit spurious correlations between different attributes, demonstrating that it improves the robustness of pretrained representations on downstream classification tasks.

    Comment: Accepted by NeurIPS 2023 Workshop Self-Supervised Learning - Theory and Practice, 18 pages, 7 figures, 7 tables
    Keywords Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-11-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Artificial Intelligence Explained for Nonexperts.

    Razavian, Narges / Knoll, Florian / Geras, Krzysztof J

    Seminars in musculoskeletal radiology

    2020  Volume 24, Issue 1, Page(s) 3–11

    Abstract: Artificial intelligence (AI) has made stunning progress in the last decade, made possible largely due to the advances in training deep neural networks with large data sets. Many of these solutions, initially developed for natural images, speech, or text, ...

    Abstract Artificial intelligence (AI) has made stunning progress in the last decade, made possible largely due to the advances in training deep neural networks with large data sets. Many of these solutions, initially developed for natural images, speech, or text, are now becoming successful in medical imaging. In this article we briefly summarize in an accessible way the current state of the field of AI. Furthermore, we highlight the most promising approaches and describe the current challenges that will need to be solved to enable broad deployment of AI in clinical practice.
    MeSH term(s) Artificial Intelligence ; Diagnostic Imaging/methods ; Humans ; Image Interpretation, Computer-Assisted/methods ; Musculoskeletal Diseases/diagnostic imaging ; Musculoskeletal System/diagnostic imaging
    Language English
    Publishing date 2020-01-28
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 1360919-1
    ISSN 1098-898X ; 1089-7860
    ISSN (online) 1098-898X
    ISSN 1089-7860
    DOI 10.1055/s-0039-3401041
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    Zs.A 4887: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  8. Book ; Online: Interpretable Prediction of Lung Squamous Cell Carcinoma Recurrence With Self-supervised Learning

    Zhu, Weicheng / Fernandez-Granda, Carlos / Razavian, Narges

    2022  

    Abstract: Lung squamous cell carcinoma (LSCC) has a high recurrence and metastasis rate. Factors influencing recurrence and metastasis are currently unknown and there are no distinct histopathological or morphological features indicating the risks of recurrence ... ...

    Abstract Lung squamous cell carcinoma (LSCC) has a high recurrence and metastasis rate. Factors influencing recurrence and metastasis are currently unknown and there are no distinct histopathological or morphological features indicating the risks of recurrence and metastasis in LSCC. Our study focuses on the recurrence prediction of LSCC based on H&E-stained histopathological whole-slide images (WSI). Due to the small size of LSCC cohorts in terms of patients with available recurrence information, standard end-to-end learning with various convolutional neural networks for this task tends to overfit. Also, the predictions made by these models are hard to interpret. Histopathology WSIs are typically very large and are therefore processed as a set of smaller tiles. In this work, we propose a novel conditional self-supervised learning (SSL) method to learn representations of WSI at the tile level first, and leverage clustering algorithms to identify the tiles with similar histopathological representations. The resulting representations and clusters from self-supervision are used as features of a survival model for recurrence prediction at the patient level. Using two publicly available datasets from TCGA and CPTAC, we show that our LSCC recurrence prediction survival model outperforms both LSCC pathological stage-based approach and machine learning baselines such as multiple instance learning. The proposed method also enables us to explain the recurrence histopathological risk factors via the derived clusters. This can help pathologists derive new hypotheses regarding morphological features associated with LSCC recurrence.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2022-03-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Artificial intelligence and cancer.

    Troyanskaya, Olga / Trajanoski, Zlatko / Carpenter, Anne / Thrun, Sebastian / Razavian, Narges / Oliver, Nuria

    Nature cancer

    2020  Volume 1, Issue 2, Page(s) 149–152

    MeSH term(s) Artificial Intelligence ; Humans ; Neoplasms/diagnosis
    Language English
    Publishing date 2020-05-22
    Publishing country England
    Document type Journal Article
    ISSN 2662-1347
    ISSN (online) 2662-1347
    DOI 10.1038/s43018-020-0034-6
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  10. Article ; Online: Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models.

    Hong, Runyu / Liu, Wenke / DeLair, Deborah / Razavian, Narges / Fenyö, David

    Cell reports. Medicine

    2021  Volume 2, Issue 9, Page(s) 100400

    Abstract: The determination of endometrial carcinoma histological subtypes, molecular subtypes, and mutation status is critical for the diagnostic process, and directly affects patients' prognosis and treatment. Sequencing, albeit slower and more expensive, can ... ...

    Abstract The determination of endometrial carcinoma histological subtypes, molecular subtypes, and mutation status is critical for the diagnostic process, and directly affects patients' prognosis and treatment. Sequencing, albeit slower and more expensive, can provide additional information on molecular subtypes and mutations that can be used to better select treatments. Here, we implement a customized multi-resolution deep convolutional neural network, Panoptes, that predicts not only the histological subtypes but also the molecular subtypes and 18 common gene mutations based on digitized H&E-stained pathological images. The model achieves high accuracy and generalizes well on independent datasets. Our results suggest that Panoptes, with further refinement, has the potential for clinical application to help pathologists determine molecular subtypes and mutations of endometrial carcinoma without sequencing.
    MeSH term(s) Algorithms ; Area Under Curve ; Deep Learning ; Endometrial Neoplasms/classification ; Endometrial Neoplasms/diagnosis ; Endometrial Neoplasms/genetics ; Endometrial Neoplasms/pathology ; Female ; Humans ; Imaging, Three-Dimensional ; ROC Curve
    Language English
    Publishing date 2021-09-23
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ISSN 2666-3791
    ISSN (online) 2666-3791
    DOI 10.1016/j.xcrm.2021.100400
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