Sulaiman Vesal
Sulaiman Vesal, M. Sc.
Research Focus
- Multi-modality cardiac image analysis and fusion
- Computer-aided detection and diagnosis of breast cancer
- Lesion classification in 3D MRI
- Quantitative analysis of breast imaging modalities
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Large-scale breast image screening and analysis
Academic CV
- Since 01/2017
Ph.D. candidate at the Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg - 08/2011 to 06/2013
M. Sc. in Computer Science at South Asian University, India - 04/2007 to 12/2010
B.Sc. in Computer Science at Kabul University, Afghanistan
Projects
Development of multi-modal, multi-scale imaging framework for the early diagnosis of breast cancer
(FAU Funds)
Project leader:
Project members:
Start date: March 1, 2017
End date: October 31, 2019
Extension Date: June 30, 2020
Abstract:
Breast cancer is the leading cause of cancer related deaths in women, the second most common cancer worldwide. The development and progression of breast cancer is a dynamic biological and evolutionary process. It involves a composite organ system, with transcriptome shaped by gene aberrations, epigenetic changes, the cellular biological context, and environmental influences. Breast cancer growth and response to treatment has a number of characteristics that are specific to the individual patient, for example the response of the immune system and the interaction with the neighboring tissue. The overall complexity of breast cancer is the main cause for the current, unsatisfying understanding of its development and the patient’s therapy response. Although recent precision medicine approaches, including genomic characterization and immunotherapies, have shown clear improvements with regard to prognosis, the right treatment of this disease remains a serious challenge. The vision of the BIG-THERA team is to improve individualized breast cancer diagnostics and therapy, with the ultimate goal of extending the life expectancy of breast cancer sufferers. Our primary contribution in this regard is developing a multi-modal, multi-scale framework for the early diagnosis of the molecular sub-types of breast cancer, in a manner that supplements the clinical diagnostic workflow and enables the early identification of patients compatible with specific immunotherapeutic solutions.
Publications:
Classification of Breast Cancer Histology Images Using Transfer Learning
International Conference Image Analysis and Recognition (, June 12, 2018)
In: ICIAR 2018: Image Analysis and Recognition 2018
DOI: 10.1007/978-3-319-93000-8_92
BibTeX: Download
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Mammographic breast density classification using a deep neural network: assessment based on inter-observer variability
Conference on Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (San Diego, CA, February 20, 2019 - February 21, 2019)
In: MEDICAL IMAGING 2019: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT, BELLINGHAM: 2019
DOI: 10.1117/12.2513420
BibTeX: Download
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Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation
IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) (Atlanta, Georgia, USA)
In: 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) 2017
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2017/Vesal17-SAF.pdf
BibTeX: Download
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Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses
In: PLoS ONE 1-15 (2020), p. 1-15
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0228446
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Publications
2022
Journal Articles
Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge
In: Medical Image Analysis 81 (2022), Article No.: 102528
ISSN: 1361-8415
DOI: 10.1016/j.media.2022.102528
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Conference Contributions
Few-shot Unsupervised Domain Adaptation for Multi-modal Cardiac Image Segmentation
German Workshop on Medical Image Computing, 2022 (Heidelberg, DEU, June 26, 2022 - June 28, 2022)
In: Klaus Maier-Hein, Thomas M. Deserno, Heinz Handels, Andreas Maier, Christoph Palm, Thomas Tolxdorff (ed.): Informatik aktuell 2022
DOI: 10.1007/978-3-658-36932-3_5
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2021
Journal Articles
Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac Image Segmentation
In: IEEE transactions on medical imaging (2021)
ISSN: 1558-254X
DOI: 10.1109/TMI.2021.3066683
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A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging
In: Medical Image Analysis 67 (2021), Article No.: 101832
ISSN: 1361-8415
DOI: 10.1016/j.media.2020.101832
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Conference Contributions
Ultrasound Breast Lesion Detection using Extracted Attention Maps from a Weakly Supervised Convolutional Neural Network
Bildverarbeitung für die Medizin 2021 (Regensburg, March 7, 2021 - March 9, 2021)
In: Palm, Christoph; Deserno, Thomas M.; Handels, Heinz;Maier, Andreas; Maier-Hein, Klaus;Tolxdorff, Thomas. (ed.): Bildverarbeitung für die Medizin 2021, Wiesbaden: 2021
DOI: 10.1007/978-3-658-33198-6
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Age Estimation on Panoramic Dental X-ray Images using Deep Learning
German Workshop on Medical Image Computing, 2021 (Regensburg, March 7, 2021 - March 9, 2021)
In: Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (ed.): Informatik aktuell 2021
DOI: 10.1007/978-3-658-33198-6_46
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2020
Journal Articles
Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks
In: Journal of Imaging 6 (2020), Article No.: 65
ISSN: 2313-433X
DOI: 10.3390/jimaging6070065
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Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses
In: PLoS ONE 1-15 (2020), p. 1-15
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0228446
BibTeX: Download
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Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification
In: IEEE Journal of Biomedical and Health Informatics (2020)
ISSN: 2168-2194
DOI: 10.1109/JBHI.2020.3046449
URL: https://ieeexplore.ieee.org/document/9302580
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Conference Contributions
COPD classification in CT images using a 3D convolutional neural network
International workshop on Algorithmen - Systeme - Anwendungen, 2020 (Berlin, March 15, 2020 - March 17, 2020)
In: Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (ed.): Informatik aktuell 2020
DOI: 10.1007/978-3-658-29267-6_8
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Deep learning-based denoising of mammographic images using physics-driven data augmentation
International workshop on Algorithmen - Systeme - Anwendungen, 2020 (Berlin, March 15, 2020 - March 17, 2020)
In: Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (ed.): Informatik aktuell 2020
DOI: 10.1007/978-3-658-29267-6_21
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The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks
45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020) (Barcelona, Spain, May 4, 2020 - May 8, 2020)
DOI: 10.1109/ICASSP40776.2020.9053800
URL: https://ieeexplore.ieee.org/document/9053800
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2019
Conference Contributions
Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation
In: STACOM 2019 2019
DOI: 10.1007/978-3-030-39074-7_32
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A 2D dilated residual U-net for multi-organ segmentation in thoracic CT
2019 SegTHOR Challenge: Segmentation of THoracic Organs at Risk in CT Images, SegTHOR 2019 (Venice, April 10, 2019)
In: Su Ruan, Caroline Petitjean, Bernard Dubray, Bernard Dubray, Zoe Lambert (ed.): CEUR Workshop Proceedings 2019
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Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI
International Workshop on Statistical Atlases and Computational Models of the Heart
In: STACOM 2018: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges 2019
DOI: 10.1007/978-3-030-12029-0_35
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Dilated deeply supervised networks for hippocampus segmentation in MRI
Workshop on Bildverarbeitung fur die Medizin, 2019 (Lübeck, March 17, 2019 - March 19, 2019)
In: Thomas M. Deserno, Andreas Maier, Christoph Palm, Heinz Handels, Klaus H. Maier-Hein, Thomas Tolxdorff (ed.): Informatik aktuell 2019
DOI: 10.1007/978-3-658-25326-4_18
BibTeX: Download
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Mammographic breast density classification using a deep neural network: assessment based on inter-observer variability
Conference on Medical Imaging: Image Perception, Observer Performance, and Technology Assessment (San Diego, CA, February 20, 2019 - February 21, 2019)
In: MEDICAL IMAGING 2019: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT, BELLINGHAM: 2019
DOI: 10.1117/12.2513420
BibTeX: Download
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2018
Journal Articles
Ein klinisch anwendbares Neuronales Netzwerk zur Klassifikation suspekter Läsionen in der Mamma-MRT
In: Fortschritte auf dem Gebiete der Röntgenstrahlen 190 (2018), p. WISS 401.2
ISSN: 0367-2239
DOI: 10.1055/s-0038-1641379
BibTeX: Download
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Conference Contributions
Classification of Breast Cancer Histology Images Using Transfer Learning
International Conference Image Analysis and Recognition (, June 12, 2018)
In: ICIAR 2018: Image Analysis and Recognition 2018
DOI: 10.1007/978-3-319-93000-8_92
BibTeX: Download
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A Multi-task Framework for Skin Lesion Detection and Segmentation
International Workshop on Skin Image Analysis(ISIC 2018, Held in Conjunction with MICCAI 2018) (Granada, Spain, September 16, 2018 - September 20, 2018)
In: OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis 2018
DOI: 10.1007/978-3-030-01201-4_31
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2018/Vesal18-AMF.pdf
BibTeX: Download
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Comparative analysis of unsupervised algorithms for breast MRI lesion segmentation
Workshop on Bildverarbeitung fur die Medizin, 2018 (Erlangen, DEU, March 11, 2018 - March 13, 2018)
In: Heinz Handels, Thomas Tolxdorff, Thomas M. Deserno, Klaus H. Maier-Hein, Andreas Maier, Christoph Palm (ed.): Informatik aktuell 2018
DOI: 10.1007/978-3-662-56537-7_68
BibTeX: Download
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Hyper-Hue and EMAP on Hyperspectral Images for Supervised Layer Decomposition of Old Master Drawings
International Conference on Image Processing (ICIP) (Athens, Greece, October 7, 2018 - October 10, 2018)
In: International Conference on Image Processing (ICIP) 2018
DOI: 10.1109/ICIP.2018.8451768
URL: http://arxiv.org/abs/1801.09472
BibTeX: Download
(Techreport)
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SkinNet: A Deep Learning Framework for Skin Lesion Segmentation
2018 IEEE Nuclear Science Symposium and Medical Imaging (NSS/MIC) (Sydney, Australia)
In: IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) 2018
DOI: 10.1109/nssmic.2018.8824732
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2018/Vesal18-SAD.pdf
BibTeX: Download
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2017
Conference Contributions
GMM Supervectors for Limited Training Data in Hyperspectral Remote Sensing Image Classification
17th International Conference on Computer Analysis of Images and Patterns (Ystad, August 22, 2017 - August 24, 2017)
In: Proceedings of the 17th International Conference on Computer Analysis of Images and Patterns Part II 2017
DOI: 10.1007/978-3-319-64698-5_25
BibTeX: Download
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Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation
IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) (Atlanta, Georgia, USA)
In: 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) 2017
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2017/Vesal17-SAF.pdf
BibTeX: Download
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2014
Journal Articles
An Effective and Fast Hybrid Framework for Color Image Retrieval
In: Sensing and Imaging 15 (2014), p. 1-23
ISSN: 1557-2064
DOI: 10.1007/s11220-014-0093-9
BibTeX: Download
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