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  1. Article ; Online: Class-Agnostic Weighted Normalization of Staining in Histopathology Images Using a Spatially Constrained Mixture Model.

    Shafiei, Sobhan / Safarpoor, Amir / Jamalizadeh, Ahad / Tizhoosh, H R

    IEEE transactions on medical imaging

    2020  Volume 39, Issue 11, Page(s) 3355–3366

    Abstract: The colorless biopsied tissue samples are usually stained in order to visualize different microscopic structures for diagnostic purposes. But color variations associated with the process of sample preparation, usage of raw materials, diverse staining ... ...

    Abstract The colorless biopsied tissue samples are usually stained in order to visualize different microscopic structures for diagnostic purposes. But color variations associated with the process of sample preparation, usage of raw materials, diverse staining protocols, and using different slide scanners may adversely influence both visual inspection and computer-aided image analysis. As a result, many methods are proposed for histopathology image stain normalization in recent years. In this study, we introduce a novel approach for stain normalization based on learning a mixture of multivariate skew-normal distributions for stain clustering and parameter estimation alongside a stain transformation technique. The proposed method, labeled "Class-Agnostic Weighted Normalization" (short CLAW normalization), has the ability to normalize a source image by learning the color distribution of both source and target images within an expectation-maximization framework. The novelty of this approach is its flexibility to quantify the underlying both symmetric and nonsymmetric distributions of the different stain components while it is considering the spatial information. The performance of this new stain normalization scheme is tested on several publicly available digital pathology datasets to compare it against state-of-the-art normalization algorithms in terms of ability to preserve the image structure and information. All in all, our proposed method performed superior more consistently in comparison with existing methods in terms of information preservation, visual quality enhancement, and boosting computer-aided diagnosis algorithm performance.
    MeSH term(s) Algorithms ; Color ; Diagnosis, Computer-Assisted ; Image Processing, Computer-Assisted ; Staining and Labeling
    Language English
    Publishing date 2020-10-28
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 622531-7
    ISSN 1558-254X ; 0278-0062
    ISSN (online) 1558-254X
    ISSN 0278-0062
    DOI 10.1109/TMI.2020.2992108
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2546: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  2. Article ; Online: Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study.

    Sikaroudi, Milad / Safarpoor, Amir / Ghojogh, Benyamin / Shafiei, Sobhan / Crowley, Mark / Tizhoosh, H R

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

    2020  Volume 2020, Page(s) 1400–1403

    Abstract: As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks have boosted the performance of representation learning, the need for a large ...

    Abstract As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks have boosted the performance of representation learning, the need for a large sets of labeled data limits the application of such methods. As an example, high-quality delineations of regions of interest in the field of pathology is a tedious and time-consuming task due to the large image dimensions. In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning. We investigated the notion of similarity and dissimilarity in pathology whole-slide images and compared different setups from unsupervised and semi-supervised to supervised learning in our experiments. Additionally, different approaches were tested, applying few-shot learning on two publicly available pathology image datasets. We achieved high accuracy and generalization when the learned representations were applied to two different pathology datasets.
    MeSH term(s) Algorithms ; Data Collection ; Dietary Supplements ; Lymphatic System ; Neural Networks, Computer
    Language English
    Publishing date 2020-10-05
    Publishing country United States
    Document type Journal Article
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC44109.2020.9176279
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks

    Ghojogh, Benyamin / Sikaroudi, Milad / Shafiei, Sobhan / Tizhoosh, H. R. / Karray, Fakhri / Crowley, Mark

    2020  

    Abstract: Siamese neural network is a very powerful architecture for both feature extraction and metric learning. It usually consists of several networks that share weights. The Siamese concept is topology-agnostic and can use any neural network as its backbone. ... ...

    Abstract Siamese neural network is a very powerful architecture for both feature extraction and metric learning. It usually consists of several networks that share weights. The Siamese concept is topology-agnostic and can use any neural network as its backbone. The two most popular loss functions for training these networks are the triplet and contrastive loss functions. In this paper, we propose two novel loss functions, named Fisher Discriminant Triplet (FDT) and Fisher Discriminant Contrastive (FDC). The former uses anchor-neighbor-distant triplets while the latter utilizes pairs of anchor-neighbor and anchor-distant samples. The FDT and FDC loss functions are designed based on the statistical formulation of the Fisher Discriminant Analysis (FDA), which is a linear subspace learning method. Our experiments on the MNIST and two challenging and publicly available histopathology datasets show the effectiveness of the proposed loss functions.

    Comment: Accepted (to appear) in International Joint Conference on Neural Networks (IJCNN) 2020, IEEE, in IEEE World Congress on Computational Intelligence (WCCI) 2020
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2020-04-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  4. Book ; Online: Supervision and Source Domain Impact on Representation Learning

    Sikaroudi, Milad / Safarpoor, Amir / Ghojogh, Benyamin / Shafiei, Sobhan / Crowley, Mark / Tizhoosh, H. R.

    A Histopathology Case Study

    2020  

    Abstract: As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks have boosted the performance of representation learning, the need for a large ...

    Abstract As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks have boosted the performance of representation learning, the need for a large set of labeled data limits the application of such methods. As an example, high-quality delineations of regions of interest in the field of pathology is a tedious and time-consuming task due to the large image dimensions. In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning. We investigated the notion of similarity and dissimilarity in pathology whole-slide images and compared different setups from unsupervised and semi-supervised to supervised learning in our experiments. Additionally, different approaches were tested, applying few-shot learning on two publicly available pathology image datasets. We achieved high accuracy and generalization when the learned representations were applied to two different pathology datasets.

    Comment: Accepted for presentation at the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'20)
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2020-05-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  5. Article ; Online: Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence.

    Kalra, Shivam / Tizhoosh, H R / Shah, Sultaan / Choi, Charles / Damaskinos, Savvas / Safarpoor, Amir / Shafiei, Sobhan / Babaie, Morteza / Diamandis, Phedias / Campbell, Clinton J V / Pantanowitz, Liron

    NPJ digital medicine

    2020  Volume 3, Page(s) 31

    Abstract: The emergence of digital pathology has opened new horizons for histopathology. Artificial intelligence (AI) algorithms are able to operate on digitized slides to assist pathologists with different tasks. Whereas AI-involving classification and ... ...

    Abstract The emergence of digital pathology has opened new horizons for histopathology. Artificial intelligence (AI) algorithms are able to operate on digitized slides to assist pathologists with different tasks. Whereas AI-involving classification and segmentation methods have obvious benefits for image analysis, image search represents a fundamental shift in computational pathology. Matching the pathology of new patients with already diagnosed and curated cases offers pathologists a new approach to improve diagnostic accuracy through visual inspection of similar cases and computational majority vote for consensus building. In this study, we report the results from searching the largest public repository (The Cancer Genome Atlas, TCGA) of whole-slide images from almost 11,000 patients. We successfully indexed and searched almost 30,000 high-resolution digitized slides constituting 16 terabytes of data comprised of 20 million 1000 × 1000 pixels image patches. The TCGA image database covers 25 anatomic sites and contains 32 cancer subtypes. High-performance storage and GPU power were employed for experimentation. The results were assessed with conservative "majority voting" to build consensus for subtype diagnosis through vertical search and demonstrated high accuracy values for both frozen section slides (e.g., bladder urothelial carcinoma 93%, kidney renal clear cell carcinoma 97%, and ovarian serous cystadenocarcinoma 99%) and permanent histopathology slides (e.g., prostate adenocarcinoma 98%, skin cutaneous melanoma 99%, and thymoma 100%). The key finding of this validation study was that computational consensus appears to be possible for rendering diagnoses if a sufficiently large number of searchable cases are available for each cancer subtype.
    Language English
    Publishing date 2020-03-10
    Publishing country England
    Document type Journal Article
    ISSN 2398-6352
    ISSN (online) 2398-6352
    DOI 10.1038/s41746-020-0238-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides.

    Riasatian, Abtin / Babaie, Morteza / Maleki, Danial / Kalra, Shivam / Valipour, Mojtaba / Hemati, Sobhan / Zaveri, Manit / Safarpoor, Amir / Shafiei, Sobhan / Afshari, Mehdi / Rasoolijaberi, Maral / Sikaroudi, Milad / Adnan, Mohd / Shah, Sultaan / Choi, Charles / Damaskinos, Savvas / Campbell, Clinton Jv / Diamandis, Phedias / Pantanowitz, Liron /
    Kashani, Hany / Ghodsi, Ali / Tizhoosh, H R

    Medical image analysis

    2021  Volume 70, Page(s) 102032

    Abstract: Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. Their contribution to the performance of image analysis can be improved through fine-tuning. As an ... ...

    Abstract Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. Their contribution to the performance of image analysis can be improved through fine-tuning. As an ultimate solution, one might even train a deep network from scratch with the domain-relevant images, a highly desirable option which is generally impeded in pathology by lack of labeled images and the computational expense. In this study, we propose a new network, namely KimiaNet, that employs the topology of the DenseNet with four dense blocks, fine-tuned and trained with histopathology images in different configurations. We used more than 240,000 image patches with 1000×1000 pixels acquired at 20× magnification through our proposed "high-cellularity mosaic" approach to enable the usage of weak labels of 7126 whole slide images of formalin-fixed paraffin-embedded human pathology samples publicly available through The Cancer Genome Atlas (TCGA) repository. We tested KimiaNet using three public datasets, namely TCGA, endometrial cancer images, and colorectal cancer images by evaluating the performance of search and classification when corresponding features of different networks are used for image representation. As well, we designed and trained multiple convolutional batch-normalized ReLU (CBR) networks. The results show that KimiaNet provides superior results compared to the original DenseNet and smaller CBR networks when used as feature extractor to represent histopathology images.
    MeSH term(s) Humans ; Image Processing, Computer-Assisted ; Neoplasms/diagnostic imaging ; Neural Networks, Computer
    Language English
    Publishing date 2021-03-10
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1356436-5
    ISSN 1361-8423 ; 1361-8431 ; 1361-8415
    ISSN (online) 1361-8423 ; 1361-8431
    ISSN 1361-8415
    DOI 10.1016/j.media.2021.102032
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5206: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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