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  1. Article ; Online: Segmentation of liver and liver lesions using deep learning.

    Fallahpoor, Maryam / Nguyen, Dan / Montahaei, Ehsan / Hosseini, Ali / Nikbakhtian, Shahram / Naseri, Maryam / Salahshour, Faeze / Farzanefar, Saeed / Abbasi, Mehrshad

    Physical and engineering sciences in medicine

    2024  

    Abstract: Segmentation of organs and lesions could be employed for the express purpose of dosimetry in nuclear medicine, assisted image interpretations, and mass image processing studies. Deep leaning created liver and liver lesion segmentation on clinical 3D MRI ... ...

    Abstract Segmentation of organs and lesions could be employed for the express purpose of dosimetry in nuclear medicine, assisted image interpretations, and mass image processing studies. Deep leaning created liver and liver lesion segmentation on clinical 3D MRI data has not been fully addressed in previous experiments. To this end, the required data were collected from 128 patients, including their T1w and T2w MRI images, and ground truth labels of the liver and liver lesions were generated. The collection of 110 T1w-T2w MRI image sets was divided, with 94 designated for training and 16 for validation. Furthermore, 18 more datasets were separately allocated for use as hold-out test datasets. The T1w and T2w MRI images were preprocessed into a two-channel format so that they were used as inputs to the deep learning model based on the Isensee 2017 network. To calculate the final Dice coefficient of the network performance on test datasets, the binary average of T1w and T2w predicted images was used. The deep learning model could segment all 18 test cases, with an average Dice coefficient of 88% for the liver and 53% for the liver tumor. Liver segmentation was carried out with rather a high accuracy; this could be achieved for liver dosimetry during systemic or selective radiation therapies as well as for attenuation correction in PET/MRI scanners. Nevertheless, the delineation of liver lesions was not optimal; therefore, tumor detection was not practical by the proposed method on clinical data.
    Language English
    Publishing date 2024-02-21
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2662-4737
    ISSN (online) 2662-4737
    DOI 10.1007/s13246-024-01390-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: DGSAN

    Montahaei, Ehsan / Alihosseini, Danial / Baghshah, Mahdieh Soleymani

    Discrete Generative Self-Adversarial Network

    2019  

    Abstract: Although GAN-based methods have received many achievements in the last few years, they have not been entirelysuccessful in generating discrete data. The most crucial challenge of these methods is the difficulty of passing the gradientfrom the ... ...

    Abstract Although GAN-based methods have received many achievements in the last few years, they have not been entirelysuccessful in generating discrete data. The most crucial challenge of these methods is the difficulty of passing the gradientfrom the discriminator to the generator when the generator outputs are discrete. Despite the fact that several attemptshave been made to alleviate this problem, none of the existing GAN-based methods have improved the performance oftext generation compared with the maximum likelihood approach in terms of both the quality and the diversity. In thispaper, we proposed a new framework for generating discrete data by an adversarial approach in which there is no need topass the gradient to the generator. The proposed method has an iterative manner in which each new generator is definedbased on the last discriminator. It leverages the discreteness of data and the last discriminator to model the real datadistribution implicitly. Moreover, the method is supported with theoretical guarantees, and experimental results generallyshow the superiority of the proposed DGSAN method compared to the other popular or recent methods in generatingdiscrete sequential data.
    Keywords Computer Science - Machine Learning ; Statistics - Machine Learning
    Publishing date 2019-08-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Jointly Measuring Diversity and Quality in Text Generation Models

    Montahaei, Ehsan / Alihosseini, Danial / Baghshah, Mahdieh Soleymani

    2019  

    Abstract: Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text generation methods, each of them has its own shortcomings. The most widely used metrics ...

    Abstract Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text generation methods, each of them has its own shortcomings. The most widely used metrics such as BLEU only consider the quality of generated sentences and neglect their diversity. For example, repeatedly generation of only one high quality sentence would result in a high BLEU score. On the other hand, the more recent metric introduced to evaluate the diversity of generated texts known as Self-BLEU ignores the quality of generated texts. In this paper, we propose metrics to evaluate both the quality and diversity simultaneously by approximating the distance of the learned generative model and the real data distribution. For this purpose, we first introduce a metric that approximates this distance using n-gram based measures. Then, a feature-based measure which is based on a recent highly deep model trained on a large text corpus called BERT is introduced. Finally, for oracle training mode in which the generator's density can also be calculated, we propose to use the distance measures between the corresponding explicit distributions. Eventually, the most popular and recent text generation models are evaluated using both the existing and the proposed metrics and the preferences of the proposed metrics are determined.
    Keywords Computer Science - Machine Learning ; Computer Science - Computation and Language ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2019-04-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin-Eosin Whole-Slide Imaging.

    Conde-Sousa, Eduardo / Vale, João / Feng, Ming / Xu, Kele / Wang, Yin / Della Mea, Vincenzo / La Barbera, David / Montahaei, Ehsan / Baghshah, Mahdieh / Turzynski, Andreas / Gildenblat, Jacob / Klaiman, Eldad / Hong, Yiyu / Aresta, Guilherme / Araújo, Teresa / Aguiar, Paulo / Eloy, Catarina / Polónia, Antonio

    Journal of imaging

    2022  Volume 8, Issue 8

    Abstract: Breast cancer is the most common malignancy in women worldwide, and is responsible for more than half a million deaths each year. The appropriate therapy depends on the evaluation of the expression of various biomarkers, such as the human epidermal ... ...

    Abstract Breast cancer is the most common malignancy in women worldwide, and is responsible for more than half a million deaths each year. The appropriate therapy depends on the evaluation of the expression of various biomarkers, such as the human epidermal growth factor receptor 2 (HER2) transmembrane protein, through specialized techniques, such as immunohistochemistry or in situ hybridization. In this work, we present the HER2 on hematoxylin and eosin (HEROHE) challenge, a parallel event of the 16th European Congress on Digital Pathology, which aimed to predict the HER2 status in breast cancer based only on hematoxylin-eosin-stained tissue samples, thus avoiding specialized techniques. The challenge consisted of a large, annotated, whole-slide images dataset (509), specifically collected for the challenge. Models for predicting HER2 status were presented by 21 teams worldwide. The best-performing models are presented by detailing the network architectures and key parameters. Methods are compared and approaches, core methodologies, and software choices contrasted. Different evaluation metrics are discussed, as well as the performance of the presented models for each of these metrics. Potential differences in ranking that would result from different choices of evaluation metrics highlight the need for careful consideration at the time of their selection, as the results show that some metrics may misrepresent the true potential of a model to solve the problem for which it was developed. The HEROHE dataset remains publicly available to promote advances in the field of computational pathology.
    Language English
    Publishing date 2022-07-31
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2824270-1
    ISSN 2313-433X ; 2313-433X
    ISSN (online) 2313-433X
    ISSN 2313-433X
    DOI 10.3390/jimaging8080213
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: HEROHE Challenge

    Conde-Sousa, Eduardo / Vale, João / Feng, Ming / Xu, Kele / Wang, Yin / Della Mea, Vincenzo / La Barbera, David / Montahaei, Ehsan / Baghshah, Mahdieh Soleymani / Turzynski, Andreas / Gildenblat, Jacob / Klaiman, Eldad / Hong, Yiyu / Aresta, Guilherme / Araújo, Teresa / Aguiar, Paulo / Eloy, Catarina / Polónia, António

    assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization

    2021  

    Abstract: Breast cancer is the most common malignancy in women, being responsible for more than half a million deaths every year. As such, early and accurate diagnosis is of paramount importance. Human expertise is required to diagnose and correctly classify ... ...

    Abstract Breast cancer is the most common malignancy in women, being responsible for more than half a million deaths every year. As such, early and accurate diagnosis is of paramount importance. Human expertise is required to diagnose and correctly classify breast cancer and define appropriate therapy, which depends on the evaluation of the expression of different biomarkers such as the transmembrane protein receptor HER2. This evaluation requires several steps, including special techniques such as immunohistochemistry or in situ hybridization to assess HER2 status. With the goal of reducing the number of steps and human bias in diagnosis, the HEROHE Challenge was organized, as a parallel event of the 16th European Congress on Digital Pathology, aiming to automate the assessment of the HER2 status based only on hematoxylin and eosin stained tissue sample of invasive breast cancer. Methods to assess HER2 status were presented by 21 teams worldwide and the results achieved by some of the proposed methods open potential perspectives to advance the state-of-the-art.
    Keywords Quantitative Biology - Quantitative Methods ; Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing
    Subject code 616
    Publishing date 2021-11-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge.

    Verma, Ruchika / Kumar, Neeraj / Patil, Abhijeet / Kurian, Nikhil Cherian / Rane, Swapnil / Graham, Simon / Vu, Quoc Dang / Zwager, Mieke / Raza, Shan E Ahmed / Rajpoot, Nasir / Wu, Xiyi / Chen, Huai / Huang, Yijie / Wang, Lisheng / Jung, Hyun / Brown, G Thomas / Liu, Yanling / Liu, Shuolin / Jahromi, Seyed Alireza Fatemi /
    Khani, Ali Asghar / Montahaei, Ehsan / Baghshah, Mahdieh Soleymani / Behroozi, Hamid / Semkin, Pavel / Rassadin, Alexandr / Dutande, Prasad / Lodaya, Romil / Baid, Ujjwal / Baheti, Bhakti / Talbar, Sanjay / Mahbod, Amirreza / Ecker, Rupert / Ellinger, Isabella / Luo, Zhipeng / Dong, Bin / Xu, Zhengyu / Yao, Yuehan / Lv, Shuai / Feng, Ming / Xu, Kele / Zunair, Hasib / Hamza, Abdessamad Ben / Smiley, Steven / Yin, Tang-Kai / Fang, Qi-Rui / Srivastava, Shikhar / Mahapatra, Dwarikanath / Trnavska, Lubomira / Zhang, Hanyun / Narayanan, Priya Lakshmi / Law, Justin / Yuan, Yinyin / Tejomay, Abhiroop / Mitkari, Aditya / Koka, Dinesh / Ramachandra, Vikas / Kini, Lata / Sethi, Amit

    IEEE transactions on medical imaging

    2021  Volume 40, Issue 12, Page(s) 3413–3423

    Abstract: Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can ... ...

    Abstract Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public.
    MeSH term(s) Algorithms ; Cell Nucleus ; Humans ; Image Processing, Computer-Assisted
    Language English
    Publishing date 2021-11-30
    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.2021.3085712
    Database MEDical Literature Analysis and Retrieval System OnLINE

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