22 research outputs found
Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study
Computer-aided detection systems based on deep learning have shown great
potential in breast cancer detection. However, the lack of domain
generalization of artificial neural networks is an important obstacle to their
deployment in changing clinical environments. In this work, we explore the
domain generalization of deep learning methods for mass detection in digital
mammography and analyze in-depth the sources of domain shift in a large-scale
multi-center setting. To this end, we compare the performance of eight
state-of-the-art detection methods, including Transformer-based models, trained
in a single domain and tested in five unseen domains. Moreover, a single-source
mass detection training pipeline is designed to improve the domain
generalization without requiring images from the new domain. The results show
that our workflow generalizes better than state-of-the-art transfer
learning-based approaches in four out of five domains while reducing the domain
shift caused by the different acquisition protocols and scanner manufacturers.
Subsequently, an extensive analysis is performed to identify the covariate
shifts with bigger effects on the detection performance, such as due to
differences in patient age, breast density, mass size, and mass malignancy.
Ultimately, this comprehensive study provides key insights and best practices
for future research on domain generalization in deep learning-based breast
cancer detection
Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation
Cervell; Imatge per ressonà ncia magnètica; Aprenentatge transductiuCerebro; Imagen de resonancia magnética; Aprendizaje transductivoBrain; Magnetic resonance imaging; Transductive learningSegmentation of brain images from Magnetic Resonance Images (MRI) is an indispensable step in clinical practice. Morphological changes of sub-cortical brain structures and quantification of brain lesions are considered biomarkers of neurological and neurodegenerative disorders and used for diagnosis, treatment planning, and monitoring disease progression. In recent years, deep learning methods showed an outstanding performance in medical image segmentation. However, these methods suffer from generalisability problem due to inter-centre and inter-scanner variabilities of the MRI images. The main objective of the study is to develop an automated deep learning segmentation approach that is accurate and robust to the variabilities in scanner and acquisition protocols. In this paper, we propose a transductive transfer learning approach for domain adaptation to reduce the domain-shift effect in brain MRI segmentation. The transductive scenario assumes that there are sets of images from two different domains: (1) source—images with manually annotated labels; and (2) target—images without expert annotations. Then, the network is jointly optimised integrating both source and target images into the transductive training process to segment the regions of interest and to minimise the domain-shift effect. We proposed to use a histogram loss in the feature level to carry out the latter optimisation problem. In order to demonstrate the benefit of the proposed approach, the method has been tested in two different brain MRI image segmentation problems using multi-centre and multi-scanner databases for: (1) sub-cortical brain structure segmentation; and (2) white matter hyperintensities segmentation. The experiments showed that the segmentation performance of a pre-trained model could be significantly improved by up to 10%. For the first segmentation problem it was possible to achieve a maximum improvement from 0.680 to 0.799 in average Dice Similarity Coefficient (DSC) metric and for the second problem the average DSC improved from 0.504 to 0.602. Moreover, the improvements after domain adaptation were on par or showed better performance compared to the commonly used traditional unsupervised segmentation methods (FIRST and LST), also achieving faster execution time. Taking this into account, this work presents one more step toward the practical implementation of deep learning algorithms into the clinical routine.KK holds FI-DGR2017 grant from the Catalan Government with reference number 2017FI_B00372. This work has been supported by DPI2017-86696-R from the Ministerio de Ciencia y Tecnologia
Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools
The vast amount of data produced by today's medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial intelligence (AI) is emerging as one of the most prominent solutions, promising to revolutionise every day clinical practice and medical research. The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions. Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms. The main steps involved in a typical medical image preparation pipeline include: (i) image acquisition at clinical sites, (ii) image de-identification to remove personal information and protect patient privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation. There exists a plethora of open access tools to perform each of the aforementioned tasks and are hereby reviewed. Furthermore, we detail medical image repositories covering different organs and diseases. Such repositories are constantly increasing and enriched with the advent of big data. Lastly, we offer directions for future work in this rapidly evolving field
High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection
Computer-aided detection systems based on deep learning have shown good
performance in breast cancer detection. However, high-density breasts show
poorer detection performance since dense tissues can mask or even simulate
masses. Therefore, the sensitivity of mammography for breast cancer detection
can be reduced by more than 20% in dense breasts. Additionally, extremely dense
cases reported an increased risk of cancer compared to low-density breasts.
This study aims to improve the mass detection performance in high-density
breasts using synthetic high-density full-field digital mammograms (FFDM) as
data augmentation during breast mass detection model training. To this end, a
total of five cycle-consistent GAN (CycleGAN) models using three FFDM datasets
were trained for low-to-high-density image translation in high-resolution
mammograms. The training images were split by breast density BI-RADS
categories, being BI-RADS A almost entirely fatty and BI-RADS D extremely dense
breasts. Our results showed that the proposed data augmentation technique
improved the sensitivity and precision of mass detection in high-density
breasts by 2% and 6% in two different test sets and was useful as a domain
adaptation technique. In addition, the clinical realism of the synthetic images
was evaluated in a reader study involving two expert radiologists and one
surgical oncologist.Comment: 9 figures, 3 table