{"id":490898,"date":"2018-06-12T23:20:13","date_gmt":"2018-06-13T06:20:13","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=490898"},"modified":"2018-10-16T22:19:42","modified_gmt":"2018-10-17T05:19:42","slug":"large-scale-tissue-histopathology-image-classification-segmentation-and-visualization-via-deep-convolutional-activation-features","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/large-scale-tissue-histopathology-image-classification-segmentation-and-visualization-via-deep-convolutional-activation-features\/","title":{"rendered":"Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features"},"content":{"rendered":"<p><strong>Background:<\/strong> Histopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis\u00a0include complex clinical representations, limited quantities of training images in a dataset, and the extremely large\u00a0size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a\u00a0histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited.<br \/>\n<strong>Results:<\/strong> In this paper, we propose leveraging deep convolutional neural network (CNN) activation features to perform classification, segmentation and visualization in large-scale tissue histopathology images. Our framework transfers features extracted from CNNs trained by a large natural image database, ImageNet, to histopathology images. We also explore the characteristics of CNN features by visualizing the response of individual neuron components in the last hidden layer. Some of these characteristics reveal biological insights that have been verified by\u00a0pathologists. According to our experiments, the framework proposed has shown state-of-the-art performance on a\u00a0brain tumor dataset from the MICCAI 2014 Brain Tumor Digital Pathology Challenge and a colon cancer histopathology image dataset.<br \/>\n<strong>Conclusions:<\/strong> The framework proposed is a simple, efficient and effective system for histopathology image automatic\u00a0analysis. We successfully transfer ImageNet knowledge as deep convolutional activation features to the classification\u00a0and segmentation of histopathology images with little training data. CNN features are significantly more powerful\u00a0than expert-designed features.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Background: Histopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Bmc Bioinformatics","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"Bmc Bioinformatics","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2017-05-01","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13553],"msr-publication-type":[193715],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-490898","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-medical-health-genomics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Bmc Bioinformatics","msr_affiliation":"","msr_published_date":"2017-05-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"Bmc Bioinformatics","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"490946","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"[2017][SCI][BMC]large scale tissue histopathology image classification segmentation and visualization via deep convolutional activation features","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2018\/06\/2017SCIBMClarge-scale-tissue-histopathology-image-classification-segmentation-and-visualization-via-deep-convolutional-activation-features.pdf","id":490946,"label_id":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Yan Xu","user_id":0,"rest_url":false},{"type":"text","value":"Zhipeng Jia","user_id":0,"rest_url":false},{"type":"text","value":"Liang-Bo Wang","user_id":0,"rest_url":false},{"type":"text","value":"Yuqing Ai","user_id":0,"rest_url":false},{"type":"text","value":"Fang Zhang","user_id":0,"rest_url":false},{"type":"text","value":"Maode Lai","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Eric Chang","user_id":31709,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Eric Chang"}],"msr_impact_theme":[],"msr_research_lab":[199560],"msr_event":[],"msr_group":[780706],"msr_project":[170702],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":170702,"post_title":"eHuatuo: Teaching Computer to Read Medical Records","post_name":"ehuatuo-teaching-computer-to-read-medical-records","post_type":"msr-project","post_date":"2011-04-10 20:16:13","post_modified":"2019-05-16 04:27:03","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/ehuatuo-teaching-computer-to-read-medical-records\/","post_excerpt":"eHuatuo is an eHealthcare project about Teaching Computer to Read Medical Records developed by Microsoft Research Asia. 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