• 890 Citations
20112019
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Research Output 2011 2019

2019

Approximation of a pipeline of unsupervised retina image analysis methods with a CNN

Heslinga, F., Pluim, J., Dasht Bozorg, B., Berendschot, T., Houben, A. J. H. M., Henry, R. M. A. & Veta, M., 1 Mar 2019, Image Processing: SPIE Medical Imaging, 2019, San Diego, California, United States. Angelini, E. D. & Landman, B. A. (eds.). Bellingham: SPIE, 7 p. 109491N. (Proceedings of SPIE; vol. 10949)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Biomarkers
Image analysis
Pipelines
Neural networks
Medical problems

Automated clear cell renal carcinoma grade classification with prognostic significance

Tian, K., Rubadue, C. A., Lin, D. I., Veta, M., Pyle, M. E., Irshad, H. & Heng, Y. J., 1 Jan 2019, In : PLoS ONE. 14, 10, 16 p., e0222641

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
File
kidney cells
Renal Cell Carcinoma
carcinoma
Atlases
Hazards

Automatic cardiac landmark localization by a recurrent neural network

van Zon, M., Veta, M. & Li, S., 1 Jan 2019, Medical Imaging 2019: Image Processing. Landman, B. A. & Angelini, E. D. (eds.). Bellingham: SPIE, 13 p. 1094916. (Proceedings of SPIE; vol. 10949)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

landmarks
Recurrent neural networks
Mitral Valve
Magnetic Resonance Spectroscopy
Magnetic resonance

Convolutional neural networks for segmentation of the left atrium from gadolinium-enhancement MRI images

de Vente, C., Veta, M., Razeghi, O., Niederer, S., Pluim, J., Rhode, K. & Karim, R., 14 Feb 2019, Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges - 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers. Li, S., McLeod, K., Young, A., Rhode, K., Pop, M., Zhao, J., Mansi, T. & Sermesant, M. (eds.). Cham: Springer, p. 348-356 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11395 LNCS)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Gadolinium
Magnetic resonance
Convolution
Magnetic resonance imaging
Segmentation

Deep learning networks to segment and detect breast terminal duct lobular units, acini, and adipose tissue: a step toward the automated analysis of lobular involution as a marker for breast cancer risk

Onken, A., Wetstein, S., Pyle, M., Pluim, J., Schnitt, S., Baker, G., Collins, L., Tamimi, R., Veta, M. & Heng, Y., 2019, (Accepted/In press) United States and Canadian Academy of Pathology (USCAP).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

Adipose Tissue
Breast
Learning
Breast Neoplasms
Physiological Phenomena

Detection of acini in histopathology slides: towards automated prediction of breast cancer risk

Wetstein, S., Onken, A., Baker, G., Pyle, M., Pluim, J., Tamimi, R., Heng, Y. & Veta, M., 18 Mar 2019, Medical Imaging 2019: Digital Pathology. Tomaszewski, J. E. & Ward, A. D. (eds.). Bellingham, 7 p. 109560Q. (Proceedings of SPIE; vol. 10956)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Ducts
Labels
Masks
Health
Biomarkers

Learning domain-invariant representations of histological images

Lafarge, M. W., Pluim, J. P. W., Eppenhof, K. A. J. & Veta, M., 16 Jul 2019, In : Frontiers in Medicine. 6, 11 p., 162

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
File
5 Citations (Scopus)

Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge

Veta, M., Heng, Y. J., Stathonikos, N., Bejnordi, B. E., Beca, F., Wollmann, T., Rohr, K., Shah, M. A., Wang, D., Rousson, M., Hedlund, M., Tellez, D., Ciompi, F., Zerhouni, E., Lanyi, D., Viana, M., Kovalev, V., Liauchuk, V., Phoulady, H. A., Qaiser, T. & 13 othersGraham, S., Rajpoot, N., Sjöblom, E., Molin, J., Paeng, K., Hwang, S., Park, S., Jia, Z., Chang, E. I-C., Xu, Y., Beck, A. H., van Diest, P. J. & Pluim, J. P. W., 27 Feb 2019, In : Medical Image Analysis. 54, p. 111-121 11 p.

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
File
Tumors
Breast Neoplasms
Neoplasms
Mitosis
Image analysis

Preface

Bankhead, P., Janowczyk, A., Reyes-Aldasoro, C. C., Sirinukunwattana, K. & Veta, M., 1 Jan 2019, Digital Pathology. Reyes-Aldasoro, C. C., Veta, M., Janowczyk, A., Bankhead, P. & Sirinukunwattana, K. (eds.). Cham: Springer, p. v-vi 2 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11435)

Research output: Chapter in Book/Report/Conference proceedingForeword/postscriptAcademic

Open Access

Retinal microvascular biomarker extraction on fundus images from the Maastricht study using supervised deep learning

Heslinga, F., Pluim, J., Dasht Bozorg, B., Berendschot, T., Houben, A. J. H. M. & Veta, M., Apr 2019, In : Journal of Vascular Research. 56, suppl. 1, p. 120-120 1 p., 261

Research output: Contribution to journalMeeting AbstractAcademic

Open Access
2018

COMPAY 2018 preface

Ciompi, F., van der Laak, J., Rajpoot, N., McKenna, S., Veta, M. & Snead, D., 1 Jan 2018, In : Lecture Notes in Computer Science. 11039, p. VII

Research output: Contribution to journalEditorialAcademicpeer-review

7 Citations (Scopus)

Deformable image registration using convolutional neural networks

Eppenhof, K. A. J., Lafarge, M. W., Moeskops, P., Veta, M. & Pluim, J. P. W., 15 Mar 2018, Medical Imaging 2018 Image Processing. Bellingham: SPIE, 6 p. 105740S. (Proceedings of SPIE; vol. 10574)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Open Access
File
Image registration
Parameterization
Neural networks
Three-Dimensional Imaging
parameterization

Inferring a third spatial dimension from 2D histological images

Lafarge, M. W., Pluim, J. P. W., Eppenhof, K. A. J., Moeskops, P. & Veta, M., 10 Jan 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Piscataway: IEEE Computer Society, Vol. 2018-April, p. 586-589 4 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Tissue
Decomposition
Coloring Agents
Learning
Staining and Labeling
5 Citations (Scopus)

Roto-translation covariant convolutional networks for medical image analysis

Bekkers, E. J., Lafarge, M. W., Veta, M., Eppenhof, K. A. J., Pluim, J. P. W. & Duits, R., 1 Jan 2018, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. Schnabel, J. A., Davatzikos, C., Alberola-López, C., Fichtinger, G. & Frangi, A. F. (eds.). Cham: Springer, p. 440-448 9 p. (Lecture Notes in Computer Science; vol. 11070)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Medical Image Analysis
Image analysis
Neural networks
Convolution
Neural Networks
File
Image analysis
Neural networks
Convolution
Electron microscopy
Imaging techniques
2017
21 Citations (Scopus)

Adversarial training and dilated convolutions for brain MRI segmentation

Moeskops, P., Veta, M., Lafarge, M. W., Eppenhof, K. A. J. & Pluim, J. P. W., 2017, Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 3rd International Workshop, DLMIA 2017 and 7th International Workshop, ML-CDS 2017 Held in Conjunction with MICCAI 2017, Proceedings. Springer, p. 56-64 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10553 LNCS)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Convolution
Magnetic resonance imaging
Brain
Segmentation
Neural networks
228 Citations (Scopus)

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Bejnordi, B. E., Veta, M., van Diest, P. J., Van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., Hermsen, M., Manson, Q. F., Balkenhol, M., Geessink, O., Stathonikos, N., Van Dijk, M. C. R. F., Bult, P., Beca, F., Beck, A. H., Wang, D., Khosla, A., Gargeya, R., Irshad, H. & 31 othersZhong, A., Dou, Q., Li, Q., Chen, H., Lin, H. J., Heng, P. A., Haß, C., Bruni, E., Wong, Q., Halici, U., Öner, M. Ü., Cetin-Atalay, R., Berseth, M., Khvatkov, V., Vylegzhanin, A., Kraus, O., Shaban, M., Rajpoot, N., Awan, R., Sirinukunwattana, K., Qaiser, T., Tsang, Y. W., Tellez, D., Annuscheit, J., Hufnagl, P., Valkonen, M., Kartasalo, K., Latonen, L., Ruusuvuori, P., Liimatainen, K. & CAMELYON16 Consortium, 12 Dec 2017, In : JAMA Neurology. 318, 22, p. 2199-2210 12 p.

Research output: Contribution to journalArticleAcademicpeer-review

Lymph Nodes
Learning
Breast Neoplasms
Neoplasm Metastasis
Area Under Curve
15 Citations (Scopus)

Domain-adversarial neural networks to address the appearance variability of histopathology images

Lafarge, M. W., Pluim, J. P. W., Eppenhof, K. A. J., Moeskops, P. & Veta, M., 2017, Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 3rd International Workshop, DLMIA 2017 and 7th International Workshop, ML-CDS 2017 Held in Conjunction with MICCAI 2017, Proceedings. Springer, p. 83-91 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10553 LNCS)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Pathology
Image analysis
Neural Networks
Tissue
Color

Exploring the similarity of medical imaging classification problems

Cheplygina, V., Moeskops, P., Veta, M., Dashtbozorg, B. & Pluim, J. P. W., 2017, Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis: 6th Joint International Workshops, CVII-STENT 2017 and 2nd International Workshop, LABELS 2017 Held in Conjunction with MICCAI 2017, Proceedings. Cardoso, M. J., Arbel, T. & et. al. (eds.). Cham: Springer, p. 59-66 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10552 LNCS)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Medical Imaging
Medical imaging
Classification Problems
Meta-learning
Medical Image Analysis
2 Citations (Scopus)

Long-term prognosis of young breast cancer patients (≤40 years) who did not receive adjuvant systemic treatment: protocol for the PARADIGM initiative cohort study

Dackus, G. M., Ter Hoeve, N. D., Opdam, M., Vreuls, W., Varga, Z., Koop, E., Willems, S. M., Van Deurzen, C. H., Groen, E. J., Cordoba, A., Bart, J., Mooyaart, A. L., van den Tweel, J. G., Zolota, V., Wesseling, J., Sapino, A., Chmielik, E., Ryska, A., Amant, F., Broeks, A. & 13 othersKerkhoven, R., Stathonikos, N., Veta, M., Voogd, A., Jozwiak, K., Hauptmann, M., Hoogstraat, M., Schmidt, M. K., Sonke, G., van der Wall, E., Siesling, S., van Diest, P. J. & Linn, S. C., 14 Nov 2017, In : BMJ open. 7, 11, p. e017842 7 p., e017842

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
File
Clinical Protocols
Cohort Studies
Breast Neoplasms
Netherlands
Registries
2016
7 Citations (Scopus)

Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation

Veta, M., van Diest, P. J. & Pluim, J. P. W., 2016, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer, p. 632-639 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9901)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Nucleus
Tumors
Segmentation
Learning systems
Statistics
28 Citations (Scopus)

Mitosis counting in breast cancer: object-level interobserver agreement and comparison to an automatic method

Veta, M., van Diest, P. J., Jiwa, M., Al-Janabi, S. & Pluim, J. P. W., 16 Aug 2016, In : PLoS ONE. 11, 8, p. 1-13 e0161286

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
File
Pathology
Mitosis
breast neoplasms
mitosis
Breast Neoplasms
2015
136 Citations (Scopus)

Assessment of algorithms for mitosis detection in breast cancer histopathology images

Veta, M., Diest, van, P. J., Willems, S. M., Wang, H., Madabhushi, A. A., Cruz-Roa, A. A., González, F., Larsen, A. B. L. A., Vestergaard, J. S. J., Dahl, A. B. A., Ciresan, D. C. D., Schmidhuber, J., Giusti, A. A., Gambardella, L. M. L., Tek, F. B., Walter, T. T., Wang, C-W., Kondo, S., Matuszewski, B. J. B., Precioso, F. F. & 9 othersSnell, V. V., Kittler, J., de Campos, T. E., Khan, A. M., Rajpoot, N. M., Arkoumani, E., Lacle, M. M., Viergever, M. A. & Pluim, J. P. W., 20 Mar 2015, In : Medical Image Analysis. 20, 1, p. 237-248 12 p.

Research output: Contribution to journalArticleAcademicpeer-review

2014

Breast cancer histopathology image analysis

Veta, M., 2014, Utrecht: Utrecht University. 131 p.

Research output: ThesisPhd Thesis 4 Research NOT TU/e / Graduation NOT TU/e)Academic

202 Citations (Scopus)

Breast cancer histopathology image analysis : a review

Veta, M., Pluim, J. P. W., Diest, van, P. J. & Viergever, M. A., 2014, In : IEEE Transactions on Biomedical Engineering. 61, 5, p. 1400-1411 1400

Research output: Contribution to journalArticleAcademicpeer-review

Analog to digital conversion
Image analysis
Tissue
Computer aided diagnosis
Pathology
2 Citations (Scopus)

Corrections to "Breast cancer histopathology image analysis": a review [May 14 1400-1411]

Veta, M., Pluim, J. P. W., Diest, van, P. J. & Viergever, M. A., Nov 2014, In : IEEE Transactions on Biomedical Engineering. 61, 11, p. 2819-2819 1 p.

Research output: Contribution to journalArticleAcademicpeer-review

2013
140 Citations (Scopus)

Automatic nuclei segmentation in H&E stained breast cancer histopathology images

Veta, M., Diest, van, P. J., Kornegoor, R., Huisman, A., Viergever, M. A. & Pluim, J. P. W., 2013, In : PLoS ONE. 8, 7, 12 p., 70221

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
File
histopathology
breast neoplasms
Breast Neoplasms
scanners
Pathology
24 Citations (Scopus)

Detecting mitotic figures in breast cancer histopathology images

Veta, M., Diest, van, P. J. & Pluim, J. P. W., 2013, SPIE Medical Imaging Symposium 2013: Digital Pathology, 10 - 11 February 2013, Lake Buena Vista, Florida, USA. Gurcan, M. N. & Madabhushi, X. (eds.). Washington: SPIE, p. 867607-1/7 (Proceedings of SPIE; vol. 8676)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Microscopes
Imaging techniques
Histology
Testing
Pathology
2012
34 Citations (Scopus)

Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer

Veta, M., Kornegoor, R., Huisman, A., Verschuur-Maes, A. H. J., Viergever, M. A., Pluim, J. P. W. & Diest, van, P. J., 2012, In : Modern Pathology. 25, 12, p. 1559-1565

Research output: Contribution to journalArticleAcademicpeer-review

Male Breast Neoplasms
Pathology
Equipment and Supplies
Workflow
Survival Analysis
2011
34 Citations (Scopus)

Marker-controlled watershed segmentation of nuclei in H&E stained breast cancer biopsy images

Veta, M., Huisman, A., Viergever, M. A., Diest, van, P. J. & Pluim, J. P. W., 2011, Proceedings of the 8th IEEE International Symposium on Biomedical Imaging : From Nano to Macro ( ISBI'11), 30 March 2011 through 2 April 2011, Chicago, IL. Piscataway: Institute of Electrical and Electronics Engineers, p. 618-621

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Biopsy
Watersheds
Pathology
Deconvolution
Tissue