Author
Jack Zeineh
Bio: Jack Zeineh is an academic researcher from Icahn School of Medicine at Mount Sinai. The author has contributed to research in topics: Medicine & Breast cancer. The author has an hindex of 7, co-authored 14 publications receiving 374 citations.
Topics: Medicine, Breast cancer, Pathology, Prostate cancer, Internal medicine
Papers
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TL;DR: The Grand Challenge on Breast Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018) as mentioned in this paper.
335 citations
TL;DR: It is found that the deep learning framework is capable of identifying and quantifying NFT with a range of staining intensities and diverse morphologies, and is efficient and well suited for the practical application of WSIs with average processing times of 45 min per WSI per GPU.
76 citations
Patent•
01 Aug 2011
TL;DR: In this paper, the authors present a method for segmentation, processing, and feature extraction from tissue images such as, for example, images of nuclei and/or cytoplasm.
Abstract: Apparatus, methods, and computer-readable media are provided for segmentation, processing (e.g., preprocessing and/or postprocessing), and/or feature extraction from tissue images such as, for example, images of nuclei and/or cytoplasm. Tissue images processed by various embodiments described herein may be generated by Hematoxylin and Eosin (H&E) staining, immunofluorescence (IF) detection, immunohistochemistry (IHC), similar and/or related staining processes, and/or other processes. Predictive features described herein may be provided for use in, for example, one or more predictive models for treating, diagnosing, and/or predicting the occurrence (e.g., recurrence) of one or more medical conditions such as, for example, cancer or other types of disease.
61 citations
Seoul National University Hospital1, Ulsan National Institute of Science and Technology2, Seoul Metropolitan Government3, Indian Institute of Technology Madras4, Sichuan University5, Sejong University6, Alibaba Group7, Icahn School of Medicine at Mount Sinai8, The Chinese University of Hong Kong9, University of Hamburg10, Shanghai Jiao Tong University11, École Pour l'Informatique et les Techniques Avancées12, Tianjin Urban Construction Institute13, University of Maine14, Korea University15
TL;DR: The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assurance Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets and has the potential to aid the development and benchmarking of cancer diagnosis and segmentation.
48 citations
27 Jun 2018
TL;DR: This work proposes an automated classification method for identifying micro-architectures in breast cancer histopathology slides using an ensemble of convolutional neural networks, constructed by combining multiple networks, trained using different data subset sampling and image perturbation models.
Abstract: Accurate analysis of tissue structures in breast cancer histopathology slides is crucial for staging treatments and predicting outcome. Such analysis depends on identification of tissue architecture in different regions, and determining the different types of cancer morphology which includes in-situ carcinoma, invasive tumor, and benign tumor. We propose an automated classification method for identifying these micro-architectures using an ensemble of convolutional neural networks. This ensemble is constructed by combining multiple networks, trained using different data subset sampling and image perturbation models. Our proposed approach results in a high performing detector with robustness to data variations.
19 citations
Cited by
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TL;DR: The Grand Challenge on Breast Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018) as mentioned in this paper.
335 citations
TL;DR: A comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis can be found in this paper, where a survey of over 130 papers is presented.
260 citations
TL;DR: This paper proposes a new hybrid convolutional and recurrent deep neural network for breast cancer histopathological image classification that outperforms the state-of-the-art method and releases a dataset that covers as many different subclasses spanning different age groups as possible, thus providing enough data diversity to alleviate the problem of relatively low classification accuracy of benign images.
180 citations
TL;DR: The different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology are reviewed.
Abstract: There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology. The translation of AI into clinical practice will require applications to be embedded seamlessly within digital pathology workflows, driving an integrated approach to diagnostics and providing pathologists with new tools that accelerate workflow and improve diagnostic consistency and reduce errors. The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI. AI and computational pathology will continue to mature as researchers, clinicians, industry, regulatory organizations and patient advocacy groups work together to innovate and deliver new technologies to health care providers: technologies which are better, faster, cheaper, more precise, and safe.
153 citations
TL;DR: This paper reviews work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlights the different ideas underlying these methodologies.
Abstract: The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artifacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.
148 citations