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segmentation in colon histology images: The glas challenge contest","{\"openalex\":\"W2288892845\",\"doi\":\"10.1016/j.media.2016.08.008\"}","openalex","2026-05-14T16:09:07.186006985","2026-05-21T19:28:33.185576867","doi","10.1016/j.media.2016.08.008","arxivId","openalexId","W2288892845","title","abstractText","publishedDate","2016-09-10","publishedYear",2016,"venue","Medical Image Analysis","citationCount",951,"authors","keywords",[140,30,141],"pdfUrl","https://research.tue.nl/nl/publications/273e63c6-8226-4239-80f8-d77e1d54a288","topVenue","AI in cancer detection","Radiomics and Machine Learning in Medical Imaging","paper_W3035901421","History of artificial intelligence in medicine","{\"openalex\":\"W3035901421\",\"doi\":\"10.1016/j.gie.2020.06.040\"}","2026-05-14T15:40:09.192562051","2026-05-21T18:48:17.432659697","10.1016/j.gie.2020.06.040","W3035901421","2020-06-18",2020,"Gastrointestinal Endoscopy",966,[30,140,141],"http://www.giejournal.org/article/S0016510720344667/pdf","paper_W2005506998","Breast cancer screening with mammography: overview of Swedish randomised trials","{\"openalex\":\"W2005506998\",\"doi\":\"10.1016/0140-6736(93)91067-v\"}","2026-05-14T15:03:10.350140875","2026-05-21T18:42:31.186497327","10.1016/0140-6736(93)91067-v","W2005506998","1993-04-01",1993,"The Lancet",977,[167,30,140],"Global Cancer Incidence and Screening","paper_W2514628397","Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features","{\"openalex\":\"W2514628397\",\"doi\":\"10.1038/ncomms12474\"}","2026-05-14T13:57:20.19840691","2026-05-21T17:49:18.182999702","10.1038/ncomms12474","W2514628397","Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma (P<0.003) or squamous cell carcinoma (P=0.023) in the TCGA data set. We validate the survival prediction framework with the TMA cohort (P<0.036 for both tumour types). Our results suggest that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology. Our methods are extensible to histopathology images of other organs.","2016-08-16","Nature Communications",1011,[140,141,30],"https://www.nature.com/articles/ncomms12474.pdf",true,"paper_W2266464013","EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos","{\"openalex\":\"W2266464013\",\"doi\":\"10.1109/tmi.2016.2593957\"}","2026-05-14T13:35:14.283871195","2026-05-21T17:28:11.190442631","10.1109/tmi.2016.2593957","W2266464013","Surgical workflow recognition has numerous potential medical applications, such as the automatic indexing of surgical video databases and the optimization of real-time operating room scheduling, among others. As a result, surgical phase recognition has been studied in the context of several kinds of surgeries, such as cataract, neurological, and laparoscopic surgeries. In the literature, two types of features are typically used to perform this task: visual features and tool usage signals. However, the used visual features are mostly handcrafted. Furthermore, the tool usage signals are usually collected via a manual annotation process or by using additional equipment. In this paper, we propose a novel method for phase recognition that uses a convolutional neural network (CNN) to automatically learn features from cholecystectomy videos and that relies uniquely on visual information. In previous studies, it has been shown that the tool usage signals can provide valuable information in performing the phase recognition task. Thus, we present a novel CNN architecture, called EndoNet, that is designed to carry out the phase recognition and tool presence detection tasks in a multi-task manner. To the best of our knowledge, this is the first work proposing to use a CNN for multiple recognition tasks on laparoscopic videos. Experimental comparisons to other methods show that EndoNet yields state-of-the-art results for both tasks.","2016-07-22","IEEE Transactions on Medical Imaging",1026,[195,30,196],"https://hal.science/hal-03511473v1/document","Surgical Simulation and Training","Medical Image Segmentation Techniques","paper_W2285968993","Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information","{\"openalex\":\"W2285968993\",\"doi\":\"10.1109/tmi.2015.2487997\"}","2026-05-14T12:09:21.216254284","2026-05-21T15:49:18.184334885","10.1109/tmi.2015.2487997","W2285968993","This paper presents the culmination of our research in designing a system for computer-aided detection (CAD) of polyps in colonoscopy videos. Our system is based on a hybrid context-shape approach, which utilizes context information to remove non-polyp structures and shape information to reliably localize polyps. Specifically, given a colonoscopy image, we first obtain a crude edge map. Second, we remove non-polyp edges from the edge map using our unique feature extraction and edge classification scheme. Third, we localize polyp candidates with probabilistic confidence scores in the refined edge maps using our novel voting scheme. The suggested CAD system has been tested using two public polyp databases, CVC-ColonDB, containing 300 colonoscopy images with a total of 300 polyp instances from 15 unique polyps, and ASU-Mayo database, which is our collection of colonoscopy videos containing 19,400 frames and a total of 5,200 polyp instances from 10 unique polyps. We have evaluated our system using free-response receiver operating characteristic (FROC) analysis. At 0.1 false positives per frame, our system achieves a sensitivity of 88.0% for CVC-ColonDB and a sensitivity of 48% for the ASU-Mayo database. In addition, we have evaluated our system using a new detection latency analysis where latency is defined as the time from the first appearance of a polyp in the colonoscopy video to the time of its first detection by our system. At 0.05 false positives per frame, our system yields a polyp detection latency of 0.3 seconds.","2015-10-08",2015,1088,[30,209,210],"Image Retrieval and Classification Techniques","Advanced Image and Video Retrieval Techniques","paper_W3092344722","PraNet: Parallel Reverse Attention Network for Polyp Segmentation","{\"openalex\":\"W3092344722\",\"doi\":\"10.1007/978-3-030-59725-2_26\"}","2026-05-14T08:07:07.303882161","2026-05-21T11:22:01.183883766","10.1007/978-3-030-59725-2_26","W3092344722","2020-01-01","Lecture notes in computer science",1347,[210,209,30],"paper_W2113180829","A review of content-based image retrieval systems in medical applications—clinical benefits and future directions","{\"openalex\":\"W2113180829\",\"doi\":\"10.1016/j.ijmedinf.2003.11.024\"}","2026-05-14T07:01:38.220360074","2026-05-21T09:49:16.185379536","10.1016/j.ijmedinf.2003.11.024","W2113180829","2004-02-01",2004,"International Journal of Medical Informatics",1491,[209,30,234],"Biomedical Text Mining and Ontologies","paper_W2008359794","WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians","{\"openalex\":\"W2008359794\",\"doi\":\"10.1016/j.compmedimag.2015.02.007\"}","2026-05-14T04:51:53.246192088","2026-05-21T07:08:17.197876554","10.1016/j.compmedimag.2015.02.007","W2008359794","2015-04-11","Computerized Medical Imaging and Graphics",1923,[210,209,30],"paper_W4233026002","Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","{\"openalex\":\"W4233026002\",\"doi\":\"10.1136/bmj.g7594\"}","2026-05-14T01:57:26.303765706","2026-05-21T03:40:43.182366895","10.1136/bmj.g7594","W4233026002","Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web based survey and revised during a three day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). To encourage dissemination of the TRIPOD Statement, this article is freely accessible on the Annals of Internal Medicine Web site (www.annals.org) and will be also published in BJOG, British Journal of Cancer, British Journal of Surgery, BMC Medicine, The BMJ, Circulation, Diabetic Medicine, European Journal of Clinical Investigation, European Urology, and Journal of Clinical Epidemiology. The authors jointly hold the copyright of this article. An accompanying explanation and elaboration article is freely available only on www.annals.org; Annals of Internal Medicine holds copyright for that article.","2015-01-07","BMJ",3928,[259,30,260],"https://www.bmj.com/content/bmj/350/bmj.g7594.full.pdf","Meta-analysis and systematic reviews","Machine Learning in Healthcare",{"_58":262,"_60":263},[],[264],{"_63":265,"_65":266,"_67":267,"_69":268},1780733000323,{"_72":270,"_74":75,"_76":265,"_77":-5,"_78":26,"_79":26,"_80":26,"_81":-5,"_82":-5,"_83":84,"_35":85,"_86":87},[71,269],"[\"portal\",\"industries\"]","industries",[271,272,273,274,275,276,277,278,279,280,281,282],{"_15":342,"_29":343,"_31":-5,"_32":344,"_33":345,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":286,"_42":-5,"_44":-5,"_287":288,"_289":-5,"_290":75,"_291":292,"_293":346,"_295":26,"_296":84,"_297":26,"_101":-5,"_56":343,"_57":345},{"_15":338,"_29":339,"_31":-5,"_32":-5,"_33":340,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":286,"_42":-5,"_44":-5,"_287":288,"_289":-5,"_290":75,"_291":292,"_293":341,"_295":26,"_296":84,"_297":26,"_101":-5,"_56":339,"_57":340},{"_15":332,"_29":333,"_31":-5,"_32":334,"_33":335,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":286,"_42":-5,"_44":-5,"_287":288,"_289":-5,"_290":75,"_291":292,"_293":336,"_295":26,"_296":84,"_297":26,"_101":-5,"_56":333,"_57":335},{"_15":328,"_29":329,"_31":-5,"_32":-5,"_33":330,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":286,"_42":-5,"_44":-5,"_287":288,"_289":-5,"_290":75,"_291":292,"_293":331,"_295":26,"_296":84,"_297":26,"_101":-5,"_56":329,"_57":330},{"_15":324,"_29":325,"_31":-5,"_32":-5,"_33":326,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":286,"_42":-5,"_44":-5,"_287":288,"_289":-5,"_290":75,"_291":292,"_293":327,"_295":26,"_296":84,"_297":26,"_101":-5,"_56":325,"_57":326},{"_15":318,"_29":319,"_31":-5,"_32":320,"_33":321,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":286,"_42":-5,"_44":-5,"_287":288,"_289":-5,"_290":75,"_291":292,"_293":322,"_295":26,"_296":84,"_297":26,"_101":-5,"_56":319,"_57":321},{"_15":314,"_29":315,"_31":-5,"_32":-5,"_33":316,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":286,"_42":-5,"_44":-5,"_287":288,"_289":-5,"_290":75,"_291":292,"_293":317,"_295":26,"_296":181,"_297":26,"_101":-5,"_56":315,"_57":316},{"_15":310,"_29":311,"_31":-5,"_32":-5,"_33":312,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":286,"_42":-5,"_44":-5,"_287":288,"_289":-5,"_290":75,"_291":292,"_293":313,"_295":26,"_296":84,"_297":26,"_101":-5,"_56":311,"_57":312},{"_15":306,"_29":307,"_31":-5,"_32":-5,"_33":308,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":286,"_42":-5,"_44":-5,"_287":288,"_289":-5,"_290":75,"_291":292,"_293":309,"_295":26,"_296":84,"_297":26,"_101":-5,"_56":307,"_57":308},{"_15":302,"_29":303,"_31":-5,"_32":-5,"_33":304,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":286,"_42":-5,"_44":-5,"_287":288,"_289":-5,"_290":75,"_291":292,"_293":305,"_295":26,"_296":84,"_297":26,"_101":-5,"_56":303,"_57":304},{"_15":298,"_29":299,"_31":-5,"_32":-5,"_33":300,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":286,"_42":-5,"_44":-5,"_287":288,"_289":-5,"_290":75,"_291":292,"_293":301,"_295":26,"_296":84,"_297":26,"_101":-5,"_56":299,"_57":300},{"_15":283,"_29":284,"_31":-5,"_32":-5,"_33":285,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":286,"_42":-5,"_44":-5,"_287":288,"_289":-5,"_290":75,"_291":292,"_293":294,"_295":26,"_296":84,"_297":26,"_101":-5,"_56":284,"_57":285},"tax_ind_smartphone","智能手机","Smartphone","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/智能手机","sortOrder","isLeaf","entityCount","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算","tax_ind_blockchain","区块链",[323],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[337],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[347,348],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_351":24},"SITE_BASE_URL"]

Colorectal Cancer Screening and Detection 技术

Colorectal Cancer Screening and Detection · 相关事件