Top Papers: KI in der Krebserkennung (2004)

Die 50 meistzitierten Arbeiten zu KI in der Krebserkennung aus dem Jahr 2004 (von 887 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.

2002 2003 2004 2005

#	Paper	Zitationen
1	Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation Simon K. Warfield, Kelly H. Zou, William M. Wells IEEE Transactions on Medical Imaging	2.038
2	Predicting breast cancer survivability: a comparison of three data mining methods Dursun Delen, Glenn Walker, Amit Kadam Artificial Intelligence in Medicine	1.223
3	Surveillance of <EMPH TYPE="ITAL">BRCA1</EMPH> and <EMPH TYPE="ITAL">BRCA2</EMPH> Mutation Carriers With Magnetic Resonance Imaging, Ultrasound, Mammography, and Clinical Breast Examination Ellen Warner JAMA	1.164
4	Computed Tomographic Colonography (Virtual Colonoscopy) Peter B. Cotton, Valerie Durkalski, Benoit C. Pineau et al. JAMA	672
5	Quantitative Assessment of Mammographic Breast Density: Relationship with Breast Cancer Risk Jennifer A. Harvey, Marit L. Bovbjerg Radiology	490
6	An Evidence-based Staging System for Cutaneous Melanoma Charles M. Balch, Seng-Jaw Soong, Michael B. Atkins et al. CA A Cancer Journal for Clinicians	390
7	Affordable image analysis using NIH Image/ImageJ A. Vijayalakshmi, V. Girish Indian Journal of Cancer	372
8	Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections Carolina Wählby, Ida‐Maria Sintorn, Fredrik Erlandsson et al. Journal of Microscopy	356
9	Changes in Breast Cancer Detection and Mammography Recall Rates After the Introduction of a Computer-Aided Detection System David Gur, Jules H. Sumkin, Howard E. Rockette et al. JNCI Journal of the National Cancer Institute	306
10	Foundations of Image Science Anthony B. Wolbarst Health Physics	297
11	A Similarity Learning Approach to Content-Based Image Retrieval: Application to Digital Mammography I. El-Naqa, Yan Yang, N.P. Galatsanos et al. IEEE Transactions on Medical Imaging	285
12	The Bethesda System for Reporting Cervical Cytology —	269
13	Computer-Aided Diagnosis of Solid Breast Nodules: Use of an Artificial Neural Network Based on Multiple Sonographic Features Segyeong Joo, Yi Yang, W.K. Moon et al. IEEE Transactions on Medical Imaging	252
14	Stereotactic vacuum‐assisted breast biopsy in 2874 patients Ute Kettritz, Kerstin Rotter, I. Schreer et al. Cancer	246
15	Characterization of Palmprints by Wavelet Signatures via Directional Context Modeling Lei Zhang, David Zhang IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)	225
16	Accuracy of Screening Mammography Interpretation by Characteristics of Radiologists W. E. Barlow, Chi Chen, P. A. Carney et al. JNCI Journal of the National Cancer Institute	215
17	Automatic Identification of the Pectoral Muscle in Mammograms Ricardo J. Ferrari, Rangaraj M. Rangayyan, J. E. Leo Desautels et al. IEEE Transactions on Medical Imaging	211
18	Atlas-Based Segmentation of Pathological MR Brain Images Using a Model of Lesion Growth Meritxell Bach Cuadra, Claudio Pollo, Anton Bardera et al. IEEE Transactions on Medical Imaging	208
19	Virtual microscopy for learning and assessment in pathology Rakesh Kumar, Gary M. Velan, Sami O Korell et al. The Journal of Pathology	204
20	Watershed segmentation for breast tumor in 2-D sonography Yu-Len Huang, Dar‐Ren Chen Ultrasound in Medicine & Biology	199
21	Bitewing and Digital Bitewing Radiography for Detection of Caries Lesions Anne Wenzel Journal of Dental Research	196
22	Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms Hamid Soltanian‐Zadeh, Farshid Rafiee-Rad, Siamak P. Nejad‐Davarani Pattern Recognition	193
23	Flux driven automatic centerline extraction Sylvain Bouix, Kaleem Siddiqi, Allen Tannenbaum Medical Image Analysis	185
24	Efficient Energies and Algorithms for Parametric Snakes Mathews Jacob, Thierry Blu, Michaël Unser IEEE Transactions on Image Processing	184
25	Factors Predictive of Tumor-Positive Nonsentinel Lymph Nodes After Tumor-Positive Sentinel Lymph Node Dissection for Melanoma Jonathan Lee, Richard Essner, Hitoe Torisu‐Itakura et al. Journal of Clinical Oncology	173
26	Medical Image Understanding Technology Studies in fuzziness and soft computing	170
27	The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia James J. Diamond, Neil H. Anderson, Peter H. Bartels et al. Human Pathology	166
28	A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography Sheila Timp, Nico Karssemeijer Medical Physics	166
29	“Good Old” clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers Patrik Edén, Cecilia Ritz, Carsten Rose et al. European Journal of Cancer	156
30	Automatic Pectoral Muscle Segmentation on Mediolateral Oblique View Mammograms S.M. Kwok, Ramachandran Chandrasekhar, Y. Attikiouzel et al. IEEE Transactions on Medical Imaging	153
31	RUN-LENGTH ENCODING FOR VOLUMETRIC TEXTURE Dong-Hui Xu, Arati S. Kurani, Jacob Furst et al. —	147
32	Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening1 Hidetaka Arimura, Shigehiko Katsuragawa, Kenji Suzuki et al. Academic Radiology	147
33	Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines Aristotelis-Angelos Papadopoulos, Dimitrios I. Fotiadis, Aristidis Likas Artificial Intelligence in Medicine	147
34	Melanoma Computer-Aided Diagnosis Marco Burroni, Rosamaria Corona, Giordana Dell’Eva et al. Clinical Cancer Research	147
35	Measurement of Trabecular Bone Thickness in the Limited Resolution Regime of In Vivo MRI by Fuzzy Distance Transform Punam K. Saha, Félix W. Wehrli IEEE Transactions on Medical Imaging	146
36	Melanomas detected with the aid of total cutaneous photography N.E. Feit, Stephen W. Dusza, Ashfaq A. Marghoob British Journal of Dermatology	145
37	A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering Kan Jiang, Qingmin Liao, Shengyang Dai —	143
38	Linear Structures in Mammographic Images: Detection and Classification Reyer Zwiggelaar, Susan Astley, Caroline Boggis et al. IEEE Transactions on Medical Imaging	141
39	Recognizing Pitfalls in Early and Late Migration of Clip Markers after Imaging-guided Directional Vacuum-assisted Biopsy Lisa E. Esserman, Marco Cura, Darlene DʼAcosta Radiographics	136
40	Detection or decision errors? Missed lung cancer from the posteroanterior chest radiograph David Manning, Susan C. Ethell, Tim Donovan British Journal of Radiology	134
41	Improving the reproducibility of diagnosing micrometastases and isolated tumor cells Gábor Cserni, Simonetta Bianchi, Werner Boecker et al. Cancer	128
42	A novel featureless approach to mass detection in digital mammograms based on support vector machines R. Campanini, Danilo Nicola Dongiovanni, Emiro Iampieri et al. Physics in Medicine and Biology	128
43	Automatic classification of mammographic parenchymal patterns: a statistical approach Styliani Petroudi, Timor Kadir, Michael Brady —	128
44	Breast density as a determinant of interval cancer at mammographic screening Stefano Ciatto, Carmen Beatriz Visioli, Euǵenio Paci et al. British Journal of Cancer	124
45	Automation of differential blood count Neelam Sinha, A. G. Ramakrishnan —	123
46	Sonographic Appearance of Mucinous Carcinoma of the Breast W. W. M. Lam, Chiu‐Wing Winnie Chu, Gary M. Tse et al. American Journal of Roentgenology	122
47	Can Computer-aided Detection with Double Reading of Screening Mammograms Help Decrease the False-Negative Rate? Initial Experience Stamatia Destounis, Patricia DiNitto, Wende Logan-Young et al. Radiology	122
48	Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography Eugenio Alberdi, Andrey Povyakalo, Lorenzo Strigini et al. Academic Radiology	122
49	Micromorphometric Features of Positive Sentinel Lymph Nodes Predict Involvement of Nonsentinel Nodes in Patients With Melanoma Richard A. Scolyer, Ling-Xi L. Li, Stanley W. McCarthy et al. American Journal of Clinical Pathology	119
50	Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks Leonardo Bocchi, Giuseppe Coppini, Jacopo Nori et al. Medical Engineering & Physics	118

Top Papers: KI in der Krebserkennung (2004)

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