Dr. Kristina Lång Profile

Dr. Kristina Lång

at Lund Univ

SPIE Involvement:

Author

Publications (8)

Proceedings Article | 7 April 2023 Presentation + Paper

Classification of point-of-care ultrasound in breast imaging using deep learning

Jennie Karlsson, Ida Arvidsson, Freja Sahlin, Kalle Åström, Niels Christian Overgaard, Kristina Lång, Anders Heyden

Proceedings Volume 12465, 124650Y (2023) https://doi.org/10.1117/12.2654251

KEYWORDS: Data modeling, Ultrasonography, Breast cancer, Breast, Image classification, Point-of-care devices, Breast imaging, Diagnostics, Cross validation

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SPIE Journal Paper | 8 February 2023 Open Access

Malmö Breast ImaginG database: objectives and development

Victor Dahlblom, Magnus Dustler, Anetta Bolejko, Predrag R. Bakic, Henrik Granberg, Kristin Johnson, Daniel Förnvik, Kristina Lång, Anders Tingberg, Sophia Zackrisson

JMI, Vol. 10, Issue 06, 061402, (February 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.6.061402

KEYWORDS: Databases, Mammography, Cancer, Breast cancer, Diagnostics, Breast, Breast imaging, Digital breast tomosynthesis, Artificial intelligence, Tumors

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Proceedings Article | 4 April 2022 Poster + Paper

Machine learning algorithm for classification of breast ultrasound images

Jennie Karlsson, Jennifer Ramkull, Ida Arvidsson, Anders Heyden, Kalle Åström, Niels Christian Overgaard, Kristina Lång

Proceedings Volume 12033, 120331T (2022) https://doi.org/10.1117/12.2611824

KEYWORDS: Data modeling, Ultrasonography, Breast, Tumor growth modeling, Breast cancer, Performance modeling, Machine learning, Diagnostics, Image classification, Convolutional neural networks

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Breast cancer is the most common type of cancer globally. Early detection is important for reducing the morbidity and mortality of breast cancer. The aim of this study was to evaluate the performance of different machine learning models to classify malignant or benign lesions on breast ultrasound images. Three different convolutional neural network approaches were implemented: (a) Simple convolutional neural network, (b) transfer learning using pre-trained InceptionV3, ResNet50V2, VGG19 and Xception and (c) deep feature networks based on combinations of the four transfer networks in (b). The data consisted of two breast ultrasound image data sets: (1) an open, single-vendor, data set collected by Cairo University at Baheya Hospital, Egypt, consisting of 437 benign lesions and 210 malignant lesions, where 10% was set to be a test set and the rest was used for training and validation (development) and (2) An in-house, multi-vendor data set collected at Unilabs Mammography Unit, Skåne University Hospital, Sweden, consisting of 13 benign lesions and 265 malignant lesions, was used as an external test set. Both test sets were used for evaluating the networks. The performance measures used were area under the receiver operating characteristic curve (AUC), sensitivity, specificity and weighted accuracy. Holdout, i.e. the splitting of the development data into training and validation data sets just once, was used to find a model with as good performance as possible. 10-fold cross-validation was also performed to provide uncertainty estimates. For the transfer networks which were obtained with holdout, Gradient-weighted Class Activation Mapping was used to generate heat maps indicating which part of the image contributed to the network’s decision. For 10-fold cross-validation it was possible to achieve a mean AUC of 92% and mean sensitivity of 95% for the transfer network based on Xception when testing on the first data set. When testing on the second data set it was possible to obtain a mean AUC of 75% and mean sensitivity of 86% for the combination of ResNet50V2 and Xception.

Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Realism of mammography tissue patches simulated using Perlin noise: a forced choice reading study

Magnus Dustler, Predrag Bakic, Debra Ikeda, Kristina Lång, Reyer Zwiggelaar

Proceedings Volume 11595, 115954X (2021) https://doi.org/10.1117/12.2582094

KEYWORDS: Tissues, Mammography, Quantum electronics, Breast imaging, Breast, Skin, Computer simulations

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Proceedings Article | 22 May 2020 Paper

Can AI serve as an independent second reader of mammograms? a simulation study

Alejandro Rodríguez-Ruiz, Kristina Lång, Albert Gubern-Merida, Mireille Broeders, Gisella Gennaro, Paola Clauser, Thomas Helbich, Thomas Mertelmeier, Margarita Chevalier, Matthew Wallis, Ingvar Andersson, Sophia Zackrisson, R. Mann, I. Sechopoulos

Proceedings Volume 11513, 115130O (2020) https://doi.org/10.1117/12.2564114

KEYWORDS: Artificial intelligence, Mammography, Databases, Cancer, Breast, Intelligence systems, Breast cancer, Evolutionary algorithms, Digital mammography, Oncology

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SPIE Journal Paper | 5 February 2019 Open Access

Volumetric breast density measurement for personalized screening: accuracy, reproducibility, consistency, and agreement with visual assessment

Andreas Fieselmann, Daniel Förnvik, Hannie Förnvik, Kristina Lång, Hanna Sartor, Sophia Zackrisson, Steffen Kappler, Ludwig Ritschl, Thomas Mertelmeier

JMI, Vol. 6, Issue 03, 031406, (February 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.3.031406

KEYWORDS: Breast, Digital breast tomosynthesis, Tissues, Visualization, Mammography, Breast cancer, Medicine, Magnetic resonance imaging, X-ray imaging, X-rays

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