Top Papers: KI in der Krebserkennung (2008)

Die 50 meistzitierten Arbeiten zu KI in der Krebserkennung aus dem Jahr 2008 (von 1.442 insgesamt).

Krebs frühzeitig zu erkennen kann Leben retten – und genau hier setzt KI an. Deep-Learning-Modelle erreichen inzwischen bei bestimmten Tumorarten eine Erkennungsgenauigkeit, die mit der erfahrener Pathologen vergleichbar ist. Die Forschung umfasst Hautkrebs-Screening, Brustkrebs-Mammographie, Lungennoduli-Erkennung und vieles mehr. Hier finden Sie die einflussreichsten und neuesten Studien zu diesem Thema.

2006 2007 2008 2009

#	Paper	Zitationen
1	How To Build and Interpret a Nomogram for Cancer Prognosis Alexia Iasonos, Deborah Schrag, Ganesh V. Raj et al. Journal of Clinical Oncology	3.369
2	Support vector machines combined with feature selection for breast cancer diagnosis Mehmet Fatih Akay Expert Systems with Applications	806
3	Object-Based Image Analysis Blaschke, Thomas 1965- Lecture notes in geoinformation and cartography	580
4	Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features Yefeng Zheng, Adrian Barbu, Bogdan Georgescu et al. IEEE Transactions on Medical Imaging	551
5	Using Clinical Factors and Mammographic Breast Density to Estimate Breast Cancer Risk: Development and Validation of a New Predictive Model Jeffrey A. Tice, Steven R. Cummings, Rebecca Smith‐Bindman et al. Annals of Internal Medicine	542
6	Guideline implementation for breast healthcare in low-income and middle-income countries Benjamin O. Anderson, Cheng Har Yip, Robert A. Smith et al. Cancer	534
7	An expert system for detection of breast cancer based on association rules and neural network Murat Karabatak, M. Cevdet İnce Expert Systems with Applications	495
8	Exploring feature-based approaches in PET images for predicting cancer treatment outcomes I. El Naqa, Perry W. Grigsby, Aditya Apte et al. Pattern Recognition	471
9	Diagnostic Accuracy of Digital versus Film Mammography: Exploratory Analysis of Selected Population Subgroups in DMIST Etta D. Pisano, R. Edward Hendrick, Martin J. Yaffe et al. Radiology	462
10	Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008 Metaxas, Dimitris 1962-, MICCAI 2008 New York, NY Lecture notes in computer science	429
11	Diagnostic Markers for Early Detection of Ovarian Cancer Irene Visintin, Ziding Feng, Gary Longton et al. Clinical Cancer Research	417
12	Mammographic density. Potential mechanisms of breast cancer risk associated with mammographic density: hypotheses based on epidemiological evidence Lisa J. Martin, Norman F. Boyd Breast Cancer Research	370
13	Border detection in dermoscopy images using statistical region merging M. Emre Celebi, Hassan A. Kingravi, Hitoshi Iyatomi et al. Skin Research and Technology	364
14	Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and BIRADS classification in a population of cancers with subtle mammographic findings Ingvar Andersson, Debra M. Ikeda, Sophia Zackrisson et al. European Radiology	342
15	Image Segmentation Based on 2D Otsu Method with Histogram Analysis Jun Zhang, Jinglu Hu —	329
16	Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology Shivang Naik, Scott Doyle, Shannon C. Agner et al. —	307
17	Receiver-operating characteristic curve analysis in diagnostic, prognostic and predictive biomarker research Kjetil Søreide Journal of Clinical Pathology	306
18	Anniversary Paper: History and status of CAD and quantitative image analysis: The role of<i>Medical Physics</i>and AAPM Maryellen L. Giger, Heang‐Ping Chan, John M. Boone Medical Physics	305
19	Comparison of Nomograms With Other Methods for Predicting Outcomes in Prostate Cancer: A Critical Analysis of the Literature Shahrokh F. Shariat, Pierre I. Karakiewicz, Nazareno Suardi et al. Clinical Cancer Research	299
20	Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008 Hutchison, David, Weikum, Gerhard, Vardi, Moshe Y et al. Lecture notes in computer science	289
21	Single Reading with Computer-Aided Detection for Screening Mammography Fiona J. Gilbert, Susan Astley, Maureen Gc Gillan et al. New England Journal of Medicine	289
22	Automated grading of breast cancer histopathology using spectral clusteringwith textural and architectural image features Scott Doyle, Shannon C. Agner, Anant Madabhushi et al. —	277
23	Computing average shaped tissue probability templates John Ashburner, Karl Friston NeuroImage	240
24	Mammographic density. Measurement of mammographic density Martin J. Yaffe Breast Cancer Research	238
25	BodyParts3D: 3D structure database for anatomical concepts N Mitsuhashi, Kenji Fujieda, Takuro Tamura et al. Nucleic Acids Research	238
26	Prognostic Breast Cancer Signature Identified from 3D Culture Model Accurately Predicts Clinical Outcome across Independent Datasets Katherine J. Martin, Denis R. Patrick, Mina J. Bissell et al. PLoS ONE	235
27	A Novel Breast Tissue Density Classification Methodology Arnau Oliver, Jordi Freixenet, Robert Martí et al. IEEE Transactions on Information Technology in Biomedicine	232
28	An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm Hitoshi Iyatomi, Hiroshi Oka, M. Emre Celebi et al. Computerized Medical Imaging and Graphics	231
29	Quantitative Analysis of Lesion Morphology and Texture Features for Diagnostic Prediction in Breast MRI Ke Nie, Jeon‐Hor Chen, Hon J. Yu et al. Academic Radiology	229
30	A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all? Benjamin Haibe‐Kains, Christine Desmedt, Christos Sotiriou et al. Bioinformatics	222
31	Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing Arianna Mencattini, Marcello Salmeri, R. Lojacono et al. IEEE Transactions on Instrumentation and Measurement	216
32	Wndchrm – an open source utility for biological image analysis Lior Shamir, Nikita Orlov, D. Mark Eckley et al. Source Code for Biology and Medicine	215
33	Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development Olcay Sertel, Jun Kong, Hiroyuki Shimada et al. Pattern Recognition	209
34	Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models Zhuowen Tu, Katherine L. Narr, Piotr Dollár et al. IEEE Transactions on Medical Imaging	189
35	Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading Olcay Sertel, Jun Kong, Ümit V. Çatalyürek et al. Journal of Signal Processing Systems	189
36	Coregistered FDG PET/CT-Based Textural Characterization of Head and Neck Cancer for Radiation Treatment Planning Huan Yu, Curtis Caldwell, Kandice Mah et al. IEEE Transactions on Medical Imaging	188
37	Cost-Effectiveness of Digital Mammography Breast Cancer Screening Anna N.A. Tosteson, Natasha K. Stout, Dennis G. Fryback et al. Annals of Internal Medicine	186
38	The “Laboratory” Effect: Comparing Radiologists' Performance and Variability during Prospective Clinical and Laboratory Mammography Interpretations David Gur, Andriy I. Bandos, Cathy S. Cohen et al. Radiology	186
39	Breast Imaging Reporting and Data System (BI-RADS); Albert L. Baert —	180
40	A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans Laurent Massoptier, Sergio Casciaro European Radiology	176
41	Development of a quantitative method for analysis of breast density based on three‐dimensional breast MRI Ke Nie, Jeon‐Hor Chen, Si‐Wa Chan et al. Medical Physics	170
42	3D Segmentation in the Clinic: A Grand Challenge II: MS lesion segmentation Martin Styner, Joohwi Lee, Brian Chin et al. —	169
43	Fuzzy Local Binary Patterns for Ultrasound Texture Characterization Dimitris K. Iakovidis, Eystratios G. Keramidas, Dimitris Maroulis Lecture notes in computer science	166
44	ROC analysis with multiple classes and multiple tests: methodology and its application in microarray studies Jialiang Li, Jason P. Fine Biostatistics	164
45	Basic Physics and Doubts about Relationship between Mammographically Determined Tissue Density and Breast Cancer Risk Daniel B. Kopans Radiology	162
46	Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network Neeraj Sharma, AmitK Ray, Shiru Sharma et al. Journal of Medical Physics	161
47	Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation Jun Kong, Olcay Sertel, Hiroyuki Shimada et al. Pattern Recognition	160
48	Computer aids and human second reading as interventions in screening mammography: Two systematic reviews to compare effects on cancer detection and recall rate Paul Taylor, Henry Potts European Journal of Cancer	159
49	The Bethesda System for Reporting Cervical Cytology Ritu Nayar, David C. Wilbur, Diane Solomon Elsevier eBooks	155
50	Digital Breast Tomosynthesis: A Pilot Observer Study Walter F. Good, Gordon S. Abrams, Victor J. Catullo et al. American Journal of Roentgenology	152

Top Papers: KI in der Krebserkennung (2008)

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