Prof. Clarisa I. Sánchez-Gutiérrez Profile

Prof. Clarisa I. Sánchez-Gutiérrez

Full Professor at Univ of Amsterdam

SPIE Involvement:

Conference Program Committee | Author

Publications (9)

Proceedings Article | 3 April 2024 Presentation + Paper

Towards automation in non-invasive measurement of knee implant displacement

Caroline Magg, Maaike ter Wee, George Buijs, Arthur Kievit, Dennis Krap, Johannes G. Dobbe, Geert Streekstra, Leendert Blankevoort, Clara Sánchez

Proceedings Volume 12927, 129270R (2024) https://doi.org/10.1117/12.3008090

KEYWORDS: Image segmentation, Bone, Computed tomography, 3D modeling, Image registration, Error analysis, Reproducibility, Biological imaging, Automation, Process modeling

Read Abstract +

Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Deep MRI reconstruction with radial subsampling

George Yiasemis, Chaoping Zhang, Clara Sánchez, Jan-Jakob Sonke, Jonas Teuwen

Proceedings Volume 12031, 1203136 (2022) https://doi.org/10.1117/12.2609876

KEYWORDS: Magnetic resonance imaging, Data acquisition, Brain, Visualization, Neuroimaging, Image restoration, Medical image reconstruction, Inverse problem on medical image

Read Abstract +

Proceedings Article | 4 April 2022 Presentation + Paper

Mammary duct detection using self-supervised encoders

Shannon Doyle, Francesco Dal Canton, Jelle Wesseling, Clara Sánchez, Jonas Teuwen

Proceedings Volume 12033, 120330U (2022) https://doi.org/10.1117/12.2612838

KEYWORDS: Computer programming, Image classification, Tissues, Breast, Scanners, Medical imaging

Read Abstract +

Ductal Carcinoma in Situ (DCIS) constitutes 20–25% of all diagnosed breast cancers and is a well known potential precursor for invasive breast cancer.1 The gold standard method for diagnosing DCIS involves the detection of calcifications and abnormal cell proliferation in mammary ducts in Hematoxylin and Eosin (H&E) stained whole-slide images (WSIs). Automatic duct detection may facilitate this task as well as downstream applications that currently require tedious, manual annotation of ducts. Examples of these are grading of DCIS lesions2 and prediction of local recurrence of DCIS.3 Several methods have been developed for object detection in the field of deep learning. Such models are typically initialised using ImageNet transfer-learning features, as the limited availability of annotated medical images has hindered the creation of domain-specific encoders. Novel techniques such as self-supervised learning (SSL) promise to overcome this problem by utilising unlabelled data to learn feature encoders. SSL encoders trained on unlabelled ImageNet have demonstrated SSL’s capacity to produce meaningful representations, scoring higher than supervised features on the ImageNet 1% classification task.4 In the domain of histopathology, feature encoders (Histo encoders) have been developed.5, 6 In classification experiments with linear regression, frozen features of these encoders outperformed those of ImageNet encoders. However, when models initialised with histopathology and ImageNet encoders were fine-tuned on the same classification tasks, there were no differences in performance between the encoders.5, 6 Furthermore, the transferability of SSL encodings to object detection is poorly understood.4 These findings show that more research is needed to develop training strategies for SSL encoders that can enhance performance in relevant downstream tasks. In our study, we investigated whether current state-of-the-art SSL methods can provide model initialisations that outperform ImageNet pre-training on the task of duct detection in WSIs of breast tissue resections. We compared the performance of these SSL-based histopathology encodings (Histo-SSL) with ImageNet pre-training (supervised and self-supervised) and training from scratch. Additionally, we compared the performance of our Histo-SSL encodings with published Histo encoders by Ciga5 and Mormont6 on the same task.

Proceedings Article | 24 February 2017 Presentation + Paper

Fovea detection in optical coherence tomography using convolutional neural networks

Bart Liefers, Freerk Venhuizen, Thomas Theelen, Carel Hoyng, Bram van Ginneken, Clara Sánchez

Proceedings Volume 10133, 1013302 (2017) https://doi.org/10.1117/12.2254301

KEYWORDS: Fovea centralis, Convolutional neural networks, Databases, Optical coherence tomography, Neural networks, Optical tomography, Tomography, Image processing, Convolution, 3D image processing, Retina, Visualization, Network architectures

Read Abstract +

Proceedings Article | 20 March 2015 Paper

Automated age-related macular degeneration classification in OCT using unsupervised feature learning

Freerk Venhuizen, Bram van Ginneken, Bart Bloemen, Mark van Grinsven, Rick Philipsen, Carel Hoyng, Thomas Theelen, Clara Sánchez

Proceedings Volume 9414, 94141I (2015) https://doi.org/10.1117/12.2081521

KEYWORDS: Optical coherence tomography, Retina, Feature extraction, Image processing, Pathology, Eye, Image segmentation, Visualization, Principal component analysis, Receivers

Read Abstract +

Proceedings Article | 20 March 2014 Paper

Multiple-instance learning for computer-aided detection of tuberculosis

J. Melendez, C. Sánchez, R. H. H. Philipsen, P. Maduskar, B. van Ginneken

Proceedings Volume 9035, 90351J (2014) https://doi.org/10.1117/12.2043018

KEYWORDS: CAD systems, Computer aided design, Lung, Chest imaging, Databases, Image segmentation, Diagnostics, Computer aided diagnosis and therapy, Pattern recognition

Read Abstract +

Proceedings Article | 18 March 2013 Paper

Automatic age-related macular degeneration detection and staging

Mark J. J. P. van Grinsven, Yara T. E. Lechanteur, Johannes P. H. van de Ven, Bram van Ginneken, Thomas Theelen, Clara Sánchez

Proceedings Volume 8670, 86700M (2013) https://doi.org/10.1117/12.2007563

KEYWORDS: Computer aided design, CAD systems, Image segmentation, Cameras, Medical imaging, Detection and tracking algorithms, Receivers, Retina, Feature extraction, Computer programming

Read Abstract +

Proceedings Article | 3 March 2009 Paper

Active learning approach for detection of hard exudates, cotton wool spots, and drusen in retinal images

Clara Sánchez, Meindert Niemeijer, Thessa Kockelkorn, Michael Abràmoff, Bram van Ginneken

Proceedings Volume 7260, 72601I (2009) https://doi.org/10.1117/12.813679

KEYWORDS: Visualization, Computer aided diagnosis and therapy, CAD systems, Computer aided design, Machine learning, Image filtering, Feature selection, Computing systems, Image processing, Classification systems

Read Abstract +

Proceedings Article | 3 March 2009 Paper

Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images

Clara Sánchez, Roberto Hornero, Agustín Mayo, María García

Proceedings Volume 7260, 72601M (2009) https://doi.org/10.1117/12.812088

KEYWORDS: Image segmentation, Model-based design, Visualization, Statistical modeling, RGB color model, Statistical analysis, Databases, Blood vessels, Visual process modeling, Computer aided diagnosis and therapy

Read Abstract +

Showing 5 of 9 publications

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years