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  1. Book ; Online: Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

    Antani, Sameer / Rajaraman, Sivaramakrishnan

    2023  

    Keywords Research & information: general ; lung ; conventional radiography ; diagnostic procedure ; chronic obstructive pulmonary disease ; COVID-19 ; computed tomography ; lungs ; variability ; segmentation ; hybrid deep learning ; artificial intelligence ; deep learning ; computer-based devices ; radiology ; thoracic diagnostic imaging ; chest X-ray ; CT ; observer tests ; performance ; lung CT images ; nodule detection ; VGG-SegNet ; pre-trained VGG19 ; cardiac amyloidosis ; AL/TTR amyloidosis ; hypertrophic cardiomyopathy ; left ventricular hypertrophy ; convolutional neural network ; Tuberculosis (TB) ; drug resistance ; chest X-rays ; generalization ; localization ; Electrical Impedance Tomography ; lung imaging ; cardiopulmonary monitoring ; aorta ; lung cancer ; pulmonary artery ; pulmonary hypertension ; modality-specific knowledge ; object detection ; RetinaNet ; ensemble learning ; pneumonia ; mean average precision ; source data set ; supervised classification ; coronary artery disease ; machine learning ; cardiopulmonary disease ; faster CNN ; medical imaging ; X-rays ; transfer learning ; explainability ; n/a
    Language English
    Size 1 electronic resource (246 pages)
    Publisher MDPI - Multidisciplinary Digital Publishing Institute
    Publishing place Basel
    Document type Book ; Online
    Note English
    HBZ-ID HT030722297
    ISBN 9783036564357 ; 3036564357
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Article: Editorial on Special Issue "Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases".

    Rajaraman, Sivaramakrishnan / Antani, Sameer

    Diagnostics (Basel, Switzerland)

    2022  Volume 12, Issue 11

    Abstract: Cardiopulmonary diseases are a significant cause of mortality and morbidity worldwide [ ... ]. ...

    Abstract Cardiopulmonary diseases are a significant cause of mortality and morbidity worldwide [...].
    Language English
    Publishing date 2022-10-27
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2662336-5
    ISSN 2075-4418
    ISSN 2075-4418
    DOI 10.3390/diagnostics12112615
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Editorial on Special Issue “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”

    Sivaramakrishnan Rajaraman / Sameer Antani

    Diagnostics, Vol 12, Iss 2615, p

    2022  Volume 2615

    Abstract: Cardiopulmonary diseases are a significant cause of mortality and morbidity worldwide [.] ...

    Abstract Cardiopulmonary diseases are a significant cause of mortality and morbidity worldwide [.]
    Keywords n/a ; Medicine (General) ; R5-920
    Language English
    Publishing date 2022-10-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  4. Article ; Online: Uncovering the effects of model initialization on deep model generalization

    Sivaramakrishnan Rajaraman / Ghada Zamzmi / Feng Yang / Zhaohui Liang / Zhiyun Xue / Sameer Antani

    PLOS Digital Health, Vol 3, Iss

    A study with adult and pediatric chest X-ray images

    2024  Volume 1

    Keywords Computer applications to medicine. Medical informatics ; R858-859.7
    Language English
    Publishing date 2024-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images.

    Rajaraman, Sivaramakrishnan / Zamzmi, Ghada / Yang, Feng / Liang, Zhaohui / Xue, Zhiyun / Antani, Sameer

    PLOS digital health

    2024  Volume 3, Issue 1, Page(s) e0000286

    Abstract: Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest ...

    Abstract Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest X-rays (CXRs) are less understood. Addressing this gap, our study explores three deep model initialization techniques: Cold-start, Warm-start, and Shrink and Perturb start, focusing on adult and pediatric populations. We specifically focus on scenarios with periodically arriving data for training, thereby embracing the real-world scenarios of ongoing data influx and the need for model updates. We evaluate these models for generalizability against external adult and pediatric CXR datasets. We also propose novel ensemble methods: F-score-weighted Sequential Least-Squares Quadratic Programming (F-SLSQP) and Attention-Guided Ensembles with Learnable Fuzzy Softmax to aggregate weight parameters from multiple models to capitalize on their collective knowledge and complementary representations. We perform statistical significance tests with 95% confidence intervals and p-values to analyze model performance. Our evaluations indicate models initialized with ImageNet-pretrained weights demonstrate superior generalizability over randomly initialized counterparts, contradicting some findings for non-medical images. Notably, ImageNet-pretrained models exhibit consistent performance during internal and external testing across different training scenarios. Weight-level ensembles of these models show significantly higher recall (p<0.05) during testing compared to individual models. Thus, our study accentuates the benefits of ImageNet-pretrained weight initialization, especially when used with weight-level ensembles, for creating robust and generalizable deep learning solutions.
    Language English
    Publishing date 2024-01-17
    Publishing country United States
    Document type Journal Article
    ISSN 2767-3170
    ISSN (online) 2767-3170
    DOI 10.1371/journal.pdig.0000286
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Semantically redundant training data removal and deep model classification performance: A study with chest X-rays.

    Rajaraman, Sivaramakrishnan / Zamzmi, Ghada / Yang, Feng / Liang, Zhaohui / Xue, Zhiyun / Antani, Sameer

    Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

    2024  Volume 115, Page(s) 102379

    Abstract: Deep learning (DL) has demonstrated its innate capacity to independently learn hierarchical features from complex and multi-dimensional data. A common understanding is that its performance scales up with the amount of training data. However, the data ... ...

    Abstract Deep learning (DL) has demonstrated its innate capacity to independently learn hierarchical features from complex and multi-dimensional data. A common understanding is that its performance scales up with the amount of training data. However, the data must also exhibit variety to enable improved learning. In medical imaging data, semantic redundancy, which is the presence of similar or repetitive information, can occur due to the presence of multiple images that have highly similar presentations for the disease of interest. Also, the common use of augmentation methods to generate variety in DL training could limit performance when indiscriminately applied to such data. We hypothesize that semantic redundancy would therefore tend to lower performance and limit generalizability to unseen data and question its impact on classifier performance even with large data. We propose an entropy-based sample scoring approach to identify and remove semantically redundant training data and demonstrate using the publicly available NIH chest X-ray dataset that the model trained on the resulting informative subset of training data significantly outperforms the model trained on the full training set, during both internal (recall: 0.7164 vs 0.6597, p<0.05) and external testing (recall: 0.3185 vs 0.2589, p<0.05). Our findings emphasize the importance of information-oriented training sample selection as opposed to the conventional practice of using all available training data.
    Language English
    Publishing date 2024-04-09
    Publishing country United States
    Document type Journal Article
    ZDB-ID 639451-6
    ISSN 1879-0771 ; 0895-6111
    ISSN (online) 1879-0771
    ISSN 0895-6111
    DOI 10.1016/j.compmedimag.2024.102379
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1323: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  7. Article ; Online: Deep learning model calibration for improving performance in class-imbalanced medical image classification tasks.

    Rajaraman, Sivaramakrishnan / Ganesan, Prasanth / Antani, Sameer

    PloS one

    2022  Volume 17, Issue 1, Page(s) e0262838

    Abstract: ... The code is available at https://github.com/sivaramakrishnan-rajaraman/Model_calibration. ...

    Abstract In medical image classification tasks, it is common to find that the number of normal samples far exceeds the number of abnormal samples. In such class-imbalanced situations, reliable training of deep neural networks continues to be a major challenge, therefore biasing the predicted class probabilities toward the majority class. Calibration has been proposed to alleviate some of these effects. However, there is insufficient analysis explaining whether and when calibrating a model would be beneficial. In this study, we perform a systematic analysis of the effect of model calibration on its performance on two medical image modalities, namely, chest X-rays and fundus images, using various deep learning classifier backbones. For this, we study the following variations: (i) the degree of imbalances in the dataset used for training; (ii) calibration methods; and (iii) two classification thresholds, namely, default threshold of 0.5, and optimal threshold from precision-recall (PR) curves. Our results indicate that at the default classification threshold of 0.5, the performance achieved through calibration is significantly superior (p < 0.05) to using uncalibrated probabilities. However, at the PR-guided threshold, these gains are not significantly different (p > 0.05). This observation holds for both image modalities and at varying degrees of imbalance. The code is available at https://github.com/sivaramakrishnan-rajaraman/Model_calibration.
    MeSH term(s) Calibration ; Deep Learning ; Fundus Oculi ; Humans ; Models, Theoretical ; Tomography, X-Ray
    Language English
    Publishing date 2022-01-27
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Intramural
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0262838
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Uncovering the effects of model initialization on deep model generalization

    Sivaramakrishnan Rajaraman / Ghada Zamzmi / Feng Yang / Zhaohui Liang / Zhiyun Xue / Sameer Antani

    PLOS Digital Health, Vol 3, Iss 1, p e

    A study with adult and pediatric chest X-ray images.

    2024  Volume 0000286

    Abstract: Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest ...

    Abstract Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest X-rays (CXRs) are less understood. Addressing this gap, our study explores three deep model initialization techniques: Cold-start, Warm-start, and Shrink and Perturb start, focusing on adult and pediatric populations. We specifically focus on scenarios with periodically arriving data for training, thereby embracing the real-world scenarios of ongoing data influx and the need for model updates. We evaluate these models for generalizability against external adult and pediatric CXR datasets. We also propose novel ensemble methods: F-score-weighted Sequential Least-Squares Quadratic Programming (F-SLSQP) and Attention-Guided Ensembles with Learnable Fuzzy Softmax to aggregate weight parameters from multiple models to capitalize on their collective knowledge and complementary representations. We perform statistical significance tests with 95% confidence intervals and p-values to analyze model performance. Our evaluations indicate models initialized with ImageNet-pretrained weights demonstrate superior generalizability over randomly initialized counterparts, contradicting some findings for non-medical images. Notably, ImageNet-pretrained models exhibit consistent performance during internal and external testing across different training scenarios. Weight-level ensembles of these models show significantly higher recall (p<0.05) during testing compared to individual models. Thus, our study accentuates the benefits of ImageNet-pretrained weight initialization, especially when used with weight-level ensembles, for creating robust and generalizable deep learning solutions.
    Keywords Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 006
    Language English
    Publishing date 2024-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  9. Article ; Online: Does image resolution impact chest X-ray based fine-grained Tuberculosis-consistent lesion segmentation?

    Rajaraman, Sivaramakrishnan / Yang, Feng / Zamzmi, Ghada / Xue, Zhiyun / Antani, Sameer

    ArXiv

    2023  

    Abstract: Deep learning (DL) models are state-of-the-art in segmenting anatomical and disease regions of interest (ROIs) in medical images. Particularly, a large number of DL-based techniques have been reported using chest X-rays (CXRs). However, these models are ... ...

    Abstract Deep learning (DL) models are state-of-the-art in segmenting anatomical and disease regions of interest (ROIs) in medical images. Particularly, a large number of DL-based techniques have been reported using chest X-rays (CXRs). However, these models are reportedly trained on reduced image resolutions for reasons related to the lack of computational resources. Literature is sparse in discussing the optimal image resolution to train these models for segmenting the Tuberculosis (TB)-consistent lesions in CXRs. In this study, we investigated the performance variations using an Inception-V3 UNet model using various image resolutions with/without lung ROI cropping and aspect ratio adjustments, and (ii) identified the optimal image resolution through extensive empirical evaluations to improve TB-consistent lesion segmentation performance. We used the Shenzhen CXR dataset for the study which includes 326 normal patients and 336 TB patients. We proposed a combinatorial approach consisting of storing model snapshots, optimizing segmentation threshold and test-time augmentation (TTA), and averaging the snapshot predictions, to further improve performance with the optimal resolution. Our experimental results demonstrate that higher image resolutions are not always necessary, however, identifying the optimal image resolution is critical to achieving superior performance.
    Language English
    Publishing date 2023-01-27
    Publishing country United States
    Document type Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Can Deep Adult Lung Segmentation Models Generalize to the Pediatric Population?

    Rajaraman, Sivaramakrishnan / Yang, Feng / Zamzmi, Ghada / Xue, Zhiyun / Antani, Sameer

    Expert systems with applications

    2023  Volume 229, Issue Pt A

    Abstract: Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases in a clinical decision support system. Current deep learning models for lung segmentation are trained and ... ...

    Abstract Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases in a clinical decision support system. Current deep learning models for lung segmentation are trained and evaluated on CXR datasets in which the radiographic projections are captured predominantly from the adult population. However, the shape of the lungs is reported to be significantly different across the developmental stages from infancy to adulthood. This might result in age-related data domain shifts that would adversely impact lung segmentation performance when the models trained on the adult population are deployed for pediatric lung segmentation. In this work, our goal is to (i) analyze the generalizability of deep adult lung segmentation models to the pediatric population and (ii) improve performance through a stage-wise, systematic approach consisting of CXR modality-specific weight initializations, stacked ensembles, and an ensemble of stacked ensembles. To evaluate segmentation performance and generalizability, novel evaluation metrics consisting of mean lung contour distance (MLCD) and average hash score (AHS) are proposed in addition to the multi-scale structural similarity index measure (MS-SSIM), the intersection of union (IoU), Dice score, 95% Hausdorff distance (HD95), and average symmetric surface distance (ASSD). Our results showed a significant improvement (
    Language English
    Publishing date 2023-05-24
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2017237-0
    ISSN 0957-4174
    ISSN 0957-4174
    DOI 10.1016/j.eswa.2023.120531
    Database MEDical Literature Analysis and Retrieval System OnLINE

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