Top Papers: KI in der Krebserkennung (2015)

Die 50 meistzitierten Arbeiten zu KI in der Krebserkennung aus dem Jahr 2015 (von 2.476 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.

2013 2014 2015 2016

#	Paper	Zitationen
1	Radiomics: Images Are More than Pictures, They Are Data Robert J. Gillies, Paul E. Kinahan, Hedvig Hricak Radiology	8.020
2	A Dataset for Breast Cancer Histopathological Image Classification Fábio Alexandre Spanhol, Luiz S. Oliveira, Caroline Petitjean et al. IEEE Transactions on Biomedical Engineering	1.690
3	Preparing a collection of radiology examinations for distribution and retrieval Dina Demner‐Fushman, Marc Kohli, Marc B. Rosenman et al. Journal of the American Medical Informatics Association	1.042
4	Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images Jun Xu, Lei Xiang, Qingshan Liu et al. IEEE Transactions on Medical Imaging	860
5	What Is Machine Learning? Issam El Naqa, Martin J. Murphy —	736
6	Diagnostic Concordance Among Pathologists Interpreting Breast Biopsy Specimens Joann G. Elmore, Gary Longton, Patricia A. Carney et al. JAMA	703
7	Multi-atlas segmentation of biomedical images: A survey Juan Eugenio Iglesias, Mert R. Sabuncu Medical Image Analysis	676
8	Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection Constance D. Lehman, Robert Wellman, Diana S.M. Buist et al. JAMA Internal Medicine	665
9	Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation Holger R. Roth, Le Lü, Jiamin Liu et al. IEEE Transactions on Medical Imaging	583
10	Multi-scale Convolutional Neural Networks for Lung Nodule Classification Wei Shen, Mu Zhou, Feng Yang et al. Lecture notes in computer science	568
11	Prediction of Breast Cancer Risk Based on Profiling With Common Genetic Variants Nasim Mavaddat, Paul D.P. Pharoah, Kyriaki Michailidou et al. JNCI Journal of the National Cancer Institute	564
12	Computer-aided classification of lung nodules on computed tomography images via deep learning technique Yu-Jen Yu-Jen Chen, Kai‐Lung Hua, Che-Hao Hsu et al. OncoTargets and Therapy	542
13	Breast cancer classification using deep belief networks Ahmed M. Abdel-Zaher, Ayman El‐Baz Expert Systems with Applications	488
14	Chest pathology detection using deep learning with non-medical training Yaniv Bar, Idit Diamant, Lior Wolf et al. —	382
15	Whole slide imaging in pathology: advantages, limitations, and emerging perspectives Liron Pantanowitz, Navid Farahani, Anil V. Parwani Pathology and Laboratory Medicine International	375
16	Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images Noel Codella, Junjie Cai, Mani Abedini et al. Lecture notes in computer science	374
17	Addressing overtreatment of screen detected DCIS; the LORIS trial Adele Francis, Jeremy Thomas, Lesley Fallowfield et al. European Journal of Cancer	369
18	An Automatic Learning-Based Framework for Robust Nucleus Segmentation Fuyong Xing, Yuanpu Xie, Lin Yang IEEE Transactions on Medical Imaging	353
19	Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion M. Jorge Cardoso, Marc Modat, Robin Wolz et al. IEEE Transactions on Medical Imaging	352
20	Mammographic Breast Density: Impact on Breast Cancer Risk and Implications for Screening Phoebe E. Freer Radiographics	340
21	Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review G Lemaître, Robert Martí, Jordi Freixenet et al. Computers in Biology and Medicine	335
22	The Pap test and Bethesda 2014 Ritu Nayar, David C. Wilbur Cancer Cytopathology	328
23	Computer Aided Melanoma Skin Cancer Detection Using Image Processing Shivangi Jain, Vandana Jagtap, Nitin Pise Procedia Computer Science	310
24	Breast cancer diagnosis using GA feature selection and Rotation Forest Emina Aličković, Abdülhamit Subaşı Neural Computing and Applications	306
25	Ultrasound as the Primary Screening Test for Breast Cancer: Analysis From ACRIN 6666 Wendie A. Berg, Andriy I. Bandos, Ellen B. Mendelson et al. JNCI Journal of the National Cancer Institute	306
26	Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks Li Wen, Fucang Jia, Qingmao Hu Journal of Computer and Communications	302
27	Screening Breast Ultrasound: Past, Present, and Future Rachel F. Brem, Megan J. Lenihan, Jennifer H. Lieberman et al. American Journal of Roentgenology	296
28	Deep learning with non-medical training used for chest pathology identification Yaniv Bar, Idit Diamant, Lior Wolf et al. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE	295
29	Stain Specific Standardization of Whole-Slide Histopathological Images Babak Ehteshami Bejnordi, Geert Litjens, N. K. Timofeeva et al. IEEE Transactions on Medical Imaging	286
30	Performance of one-view breast tomosynthesis as a stand-alone breast cancer screening modality: results from the Malmö Breast Tomosynthesis Screening Trial, a population-based study Kristina Lång, Ingvar Andersson, Aldana Rosso et al. European Radiology	284
31	Identifying Women With Dense Breasts at High Risk for Interval Cancer Karla Kerlikowske, Weiwei Zhu, Anna N.A. Tosteson et al. Annals of Internal Medicine	280
32	Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning Youyi Song, Ling Zhang, Siping Chen et al. IEEE Transactions on Biomedical Engineering	277
33	How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? Junghwan Cho, Kyewook Lee, Ellie Shin et al. arXiv (Cornell University)	276
34	Automatic cell nuclei segmentation and classification of breast cancer histopathology images Pin Wang, Xianling Hu, Yongming Li et al. Signal Processing	267
35	A Stochastic Polygons Model for Glandular Structures in Colon Histology Images Korsuk Sirinukunwattana, David Snead, Nasir Rajpoot IEEE Transactions on Medical Imaging	260
36	Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks. M. Günhan Ertosun, Daniel L. Rubin PubMed	260
37	Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy Bartosz Krawczyk, Mikel Galar, Łukasz Jeleń et al. Applied Soft Computing	260
38	Histotechnology: A Self Instructional Text Mitchell Jones Journal of Histotechnology	254
39	New approach for the diagnosis of extractions with neural network machine learning Seok‐Ki Jung, Tae‐Woo Kim American Journal of Orthodontics and Dentofacial Orthopedics	251
40	Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort Adam R. Brentnall, Elaine F. Harkness, Susan Astley et al. Breast Cancer Research	249
41	An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells Zhi Lu, Gustavo Carneiro, Andrew P. Bradley IEEE Transactions on Image Processing	247
42	Breast cancer diagnosis using Genetically Optimized Neural Network model Arpit Bhardwaj, Aruna Tiwari Expert Systems with Applications	246
43	Unregistered Multiview Mammogram Analysis with Pre-trained Deep Learning Models Gustavo Carneiro, Jacinto C. Nascimento, Andrew P. Bradley Lecture notes in computer science	241
44	Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features Rajesh Kumar Dhanaraj, Rajeev Srivastava, Subodh Srivastava Journal of Medical Engineering	234
45	Validation of digital pathology imaging for primary histopathological diagnosis David Snead, Yee‐Wah Tsang, Aisha Meskiri et al. Histopathology	232
46	Digital Breast Tomosynthesis: State of the Art Srinivasan Vedantham, Andrew Karellas, Gopal R. Vijayaraghavan et al. Radiology	230
47	Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests Neeraj Dhungel, Gustavo Carneiro, Andrew P. Bradley —	225
48	An open access thyroid ultrasound image database Lina Rojas Pedraza, Carlos Vargas, Fabián Narváez et al. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE	224
49	Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles Jocelyn Barker, Assaf Hoogi, Adrien Depeursinge et al. Medical Image Analysis	218
50	Current trends in medical image registration and fusion Fatma El-Zahraa A. El-Gamal, Mohammed Elmogy, Ahmed Atwan Egyptian Informatics Journal	217

Top Papers: KI in der Krebserkennung (2015)

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