| 1. |
- Cutas, Daniela, 1978, et al.
(författare)
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Legal imperialism in the regulation of stem cell research and therapy: the problem of extraterritorial jurisdiction
- 2010
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Ingår i: Capps BJ & Campbell AV (eds.). CONTESTED CELLS: Global Perspectives on the Stem Cell Debate. - London : Imperial College Press. - 9781848164376 ; , s. 95-119
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Countries worldwide have very different national regulations on human embryonic stem (ES) cell research, informed by a range of ethical values. Some countries find reason to extend the applicability of their regulations on such research to its citizens when they visit other countries. Extraterritorial jurisdiction has recently been identified as a potential challenge towards global regulation of ES cell research. This chapter explores the implications and impact of extraterritorial jurisdiction and global regulation of ES cell research on researchers, clinicians and national health systems, and how this may affect patients. The authors argue that it would make ethical sense for ES cell restrictive countries to extend its regulations on ES cell research beyond its borders, because, if these countries really consider embryo destruction to be objectionable on the basis on the status of the embryo, then they ought to count it morally on par with murder (and thus have a moral imperative to protect embryos from the actions of its own citizens). However, doing so could lead to a legal situation that would result in substantial harm to central values in areas besides research, such as health care, the job market, basic freedom of movement, and strategic international finance and politics. Thus, it seems that restrictive extraterritorial jurisdiction in respect to ES cell research would be deeply problematic, given that the ethical permissibility of ES cell research is characterised by deep and wide disagreement.
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| 2. |
- Ali, Muhaddisa Barat, 1986, et al.
(författare)
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A novel federated deep learning scheme for glioma and its subtype classification
- 2023
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Ingår i: Frontiers in Neuroscience. - 1662-4548 .- 1662-453X. ; 17
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Tidskriftsartikel (refereegranskat)abstract
- Background: Deep learning (DL) has shown promising results in molecular-based classification of glioma subtypes from MR images. DL requires a large number of training data for achieving good generalization performance. Since brain tumor datasets are usually small in size, combination of such datasets from different hospitals are needed. Data privacy issue from hospitals often poses a constraint on such a practice. Federated learning (FL) has gained much attention lately as it trains a central DL model without requiring data sharing from different hospitals. Method: We propose a novel 3D FL scheme for glioma and its molecular subtype classification. In the scheme, a slice-based DL classifier, EtFedDyn, is exploited which is an extension of FedDyn, with the key differences on using focal loss cost function to tackle severe class imbalances in the datasets, and on multi-stream network to exploit MRIs in different modalities. By combining EtFedDyn with domain mapping as the pre-processing and 3D scan-based post-processing, the proposed scheme makes 3D brain scan-based classification on datasets from different dataset owners. To examine whether the FL scheme could replace the central learning (CL) one, we then compare the classification performance between the proposed FL and the corresponding CL schemes. Furthermore, detailed empirical-based analysis were also conducted to exam the effect of using domain mapping, 3D scan-based post-processing, different cost functions and different FL schemes. Results: Experiments were done on two case studies: classification of glioma subtypes (IDH mutation and wild-type on TCGA and US datasets in case A) and glioma grades (high/low grade glioma HGG and LGG on MICCAI dataset in case B). The proposed FL scheme has obtained good performance on the test sets (85.46%, 75.56%) for IDH subtypes and (89.28%, 90.72%) for glioma LGG/HGG all averaged on five runs. Comparing with the corresponding CL scheme, the drop in test accuracy from the proposed FL scheme is small (−1.17%, −0.83%), indicating its good potential to replace the CL scheme. Furthermore, the empirically tests have shown that an increased classification test accuracy by applying: domain mapping (0.4%, 1.85%) in case A; focal loss function (1.66%, 3.25%) in case A and (1.19%, 1.85%) in case B; 3D post-processing (2.11%, 2.23%) in case A and (1.81%, 2.39%) in case B and EtFedDyn over FedAvg classifier (1.05%, 1.55%) in case A and (1.23%, 1.81%) in case B with fast convergence, which all contributed to the improvement of overall performance in the proposed FL scheme. Conclusion: The proposed FL scheme is shown to be effective in predicting glioma and its subtypes by using MR images from test sets, with great potential of replacing the conventional CL approaches for training deep networks. This could help hospitals to maintain their data privacy, while using a federated trained classifier with nearly similar performance as that from a centrally trained one. Further detailed experiments have shown that different parts in the proposed 3D FL scheme, such as domain mapping (make datasets more uniform) and post-processing (scan-based classification), are essential.
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| 3. |
- Ge, Chenjie, 1991, et al.
(författare)
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Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification
- 2020
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Ingår i: IEEE Access. - 2169-3536 .- 2169-3536. ; 8:1, s. 22560-22570
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses issues of brain tumor subtype classification using Magnetic Resonance Images (MRIs) from different scanner modalities like T1 weighted, T1 weighted with contrast-enhanced, T2 weighted and FLAIR images. Currently most available glioma datasets are relatively moderate in size, and often accompanied with incomplete MRIs in different modalities. To tackle the commonly encountered problems of insufficiently large brain tumor datasets and incomplete modality of image for deep learning, we propose to add augmented brain MR images to enlarge the training dataset by employing a pairwise Generative Adversarial Network (GAN) model. The pairwise GAN is able to generate synthetic MRIs across different modalities. To achieve the patient-level diagnostic result, we propose a post-processing strategy to combine the slice-level glioma subtype classification results by majority voting. A two-stage course-to-fine training strategy is proposed to learn the glioma feature using GAN-augmented MRIs followed by real MRIs. To evaluate the effectiveness of the proposed scheme, experiments have been conducted on a brain tumor dataset for classifying glioma molecular subtypes: isocitrate dehydrogenase 1 (IDH1) mutation and IDH1 wild-type. Our results on the dataset have shown good performance (with test accuracy 88.82%). Comparisons with several state-of-the-art methods are also included.
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| 4. |
- Bayadsi, Haytham, 1987-, et al.
(författare)
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The correlation between small papillary thyroid cancers and gamma radionuclides Cs-137, Th-232, U-238 and K-40 using spatially-explicit, register-based methods
- 2023
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Ingår i: Spatial and Spatio-Temporal Epidemiology. - : Elsevier. - 1877-5845 .- 1877-5853. ; 47
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Tidskriftsartikel (refereegranskat)abstract
- A steep increase of small papillary thyroid cancers (sPTCs) has been observed globally. A major risk factor for developing PTC is ionizing radiation. The aim of this study is to investigate the spatial distribution of sPTC in Sweden and the extent to which prevalence is correlated to gamma radiation levels (Caesium-137 (Cs-137), Thorium-232 (Th-232), Uranium-238 (U-238) and Potassium-40 (K-40)) using multiple geospatial and geo-statistical methods. The prevalence of metastatic sPTC was associated with significantly higher levels of Gamma radiation from Th-232, U-238 and K-40. The association is, however, inconsistent and the prevalence is higher in densely populated areas. The results clearly indicate that sPTC has causative factors that are neither evenly distributed among the population, nor geographically, calling for further studies with bigger cohorts. Environ-mental factors are believed to play a major role in the pathogenesis of the disease.
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| 5. |
- Ali, Muhaddisa Barat, 1986, et al.
(författare)
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Multi-stream Convolutional Autoencoder and 2D Generative Adversarial Network for Glioma Classification
- 2019
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 11678 LNCS, s. 234-245
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Konferensbidrag (refereegranskat)abstract
- Diagnosis and timely treatment play an important role in preventing brain tumor growth. Deep learning methods have gained much attention lately. Obtaining a large amount of annotated medical data remains a challenging issue. Furthermore, high dimensional features of brain images could lead to over-fitting. In this paper, we address the above issues. Firstly, we propose an architecture for Generative Adversarial Networks to generate good quality synthetic 2D MRIs from multi-modality MRIs (T1 contrast-enhanced, T2, FLAIR). Secondly, we propose a deep learning scheme based on 3-streams of Convolutional Autoencoders (CAEs) followed by sensor information fusion. The rational behind using CAEs is that it may improve glioma classification performance (as comparing with conventional CNNs), since CAEs offer noise robustness and also efficient feature reduction hence possibly reduce the over-fitting. A two-round training strategy is also applied by pre-training on GAN augmented synthetic MRIs followed by refined-training on original MRIs. Experiments on BraTS 2017 dataset have demonstrated the effectiveness of the proposed scheme (test accuracy 92.04%). Comparison with several exiting schemes has provided further support to the proposed scheme.
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| 6. |
- Aulin, Cecilia, 1979-
(författare)
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Extracellular Matrix Based Materials for Tissue Engineering
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The extracellular matrix is (ECM) is a network of large, structural proteins and polysaccharides, important for cellular behavior, tissue development and maintenance. Present thesis describes work exploring ECM as scaffolds for tissue engineering by manipulating cells cultured in vitro or by influencing ECM expression in vivo. By culturing cells on polymer meshes under dynamic culture conditions, deposition of a complex ECM could be achieved, but with low yields. Since the major part of synthesized ECM diffused into the medium the rate limiting step of deposition was investigated. This quantitative analysis showed that the real rate limiting factor is the low proportion of new proteins which are deposited as functional ECM. It is suggested that cells are pre-embedded in for example collagen gels to increase the steric retention and hence functional deposition. The possibility to induce endogenous ECM formation and tissue regeneration by implantation of growth factors in a carrier material was investigated. Bone morphogenetic protein-2 (BMP-2) is a growth factor known to be involved in growth and differentiation of bone and cartilage tissue. The BMP-2 processing and secretion was examined in two cell systems representing endochondral (chondrocytes) and intramembranous (mesenchymal stem cells) bone formation. It was discovered that chondrocytes are more efficient in producing BMP-2 compared to MSC. The role of the antagonist noggin was also investigated and was found to affect the stability of BMP-2 and modulate its effect. Finally, an injectable gel of the ECM component hyaluronan has been evaluated as delivery vehicle in cartilage regeneration. The hyaluronan hydrogel system showed promising results as a versatile biomaterial for cartilage regeneration, could easily be placed intraarticulary and can be used for both cell based and cell free therapies.
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| 7. |
- Sarve, Hamid, et al.
(författare)
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Quantification of bone remodeling in the proximity of implants
- 2007
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Ingår i: Proceedings of the 12th International Conference on Computer Analysis of Images and Patterns (CAIP07). - Berlin : Springer. - 9783540742715 ; , s. 253-260, s. 253-260
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Konferensbidrag (refereegranskat)abstract
- In histomorphometrical investigations of bone tissue modeling around screw-shaped implants, the manual measurements of bone area and bone-implant contact length around the implant are time consuming and subjective. In this paper we propose an automatic image analysis method for such measurements. We evaluate different discriminant analysis methods and compare the automatic method with the manual one. The results show that the principal difference between the two methods occurs in length estimation, whereas the area measurement does not differ significantly. A major factor behind the dissimilarities in the results is believed to be misclassification of staining artifacts by the automatic method.
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| 8. |
- Ge, Chenjie, 1991, et al.
(författare)
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Cross-Modality Augmentation of Brain Mr Images Using a Novel Pairwise Generative Adversarial Network for Enhanced Glioma Classification
- 2019
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Ingår i: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880.
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Konferensbidrag (refereegranskat)abstract
- © 2019 IEEE. Brain Magnetic Resonance Images (MRIs) are commonly used for tumor diagnosis. Machine learning for brain tumor characterization often uses MRIs from many modalities (e.g., T1-MRI, Enhanced-T1-MRI, T2-MRI and FLAIR). This paper tackles two issues that may impact brain tumor characterization performance from deep learning: insufficiently large training dataset, and incomplete collection of MRIs from different modalities. We propose a novel pairwise generative adversarial network (GAN) architecture for generating synthetic brain MRIs in missing modalities by using existing MRIs in other modalities. By improving the training dataset, we aim to mitigate the overfitting and improve the deep learning performance. Main contributions of the paper include: (a) propose a pairwise generative adversarial network (GAN) for brain image augmentation via cross-modality image generation; (b) propose a training strategy to enhance the glioma classification performance, where GAN-augmented images are used for pre-training, followed by refined-training using real brain MRIs; (c) demonstrate the proposed method through tests and comparisons of glioma classifiers that are trained from mixing real and GAN synthetic data, as well as from real data only. Experiments were conducted on an open TCGA dataset, containing 167 subjects for classifying IDH genotypes (mutation or wild-type). Test results from two experimental settings have both provided supports to the proposed method, where glioma classification performance has consistently improved by using mixed real and augmented data (test accuracy 81.03%, with 2.57% improvement).
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| 9. |
- Almirón Santa-Bárbara, Rafael, et al.
(författare)
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New technologies for the classification of proximal humeral fractures : Comparison between Virtual Reality and 3D printed models—a randomised controlled trial
- 2023
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Ingår i: Virtual Reality. - : Springer Science and Business Media Deutschland GmbH. - 1359-4338 .- 1434-9957. ; 27:3, s. 1623-1634
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Tidskriftsartikel (refereegranskat)abstract
- Correct classification of fractures according to their patterns is critical for developing a treatment plan in orthopaedic surgery. Unfortunately, for proximal humeral fractures (PHF), methods for proper classification have remained a jigsaw puzzle that has not yet been fully solved despite numerous proposed classifications and diagnostic methods. Recently, many studies have suggested that three-dimensional printed models (3DPM) can improve the interobserver agreement on PHF classifications. Moreover, Virtual Reality (VR) has not been properly studied for classification of shoulder injuries. The current study investigates the PHF classification accuracy relative to an expert committee when using either 3DPM or equivalent models displayed in VR among 36 orthopaedic surgery residents from different hospitals. We designed a multicentric randomised controlled trial in which we created two groups: a group exposed to a total of 34 3DPM and another exposed to VR equivalents. Association between classification accuracy and group assignment (VR/3DPM) was assessed using mixed effects logistic regression models. The results showed VR can be considered a non-inferior technology for classifying PHF when compared to 3DPM. Moreover, VR may be preferable when considering possible time and resource savings along with potential uses of VR for presurgical planning in orthopaedics.
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| 10. |
- Ge, Chenjie, 1991, et al.
(författare)
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Deep semi-supervised learning for brain tumor classification
- 2020
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Ingår i: BMC Medical Imaging. - : Springer Science and Business Media LLC. - 1471-2342. ; 20:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: This paper addresses issues of brain tumor, glioma, classification from four modalities of Magnetic Resonance Image (MRI) scans (i.e., T1 weighted MRI, T1 weighted MRI with contrast-enhanced, T2 weighted MRI and FLAIR). Currently, many available glioma datasets often contain some unlabeled brain scans, and many datasets are moderate in size. Methods: We propose to exploit deep semi-supervised learning to make full use of the unlabeled data. Deep CNN features were incorporated into a new graph-based semi-supervised learning framework for learning the labels of the unlabeled data, where a new 3D-2D consistent constraint is added to make consistent classifications for the 2D slices from the same 3D brain scan. A deep-learning classifier is then trained to classify different glioma types using both labeled and unlabeled data with estimated labels. To alleviate the overfitting caused by moderate-size datasets, synthetic MRIs generated by Generative Adversarial Networks (GANs) are added in the training of CNNs. Results: The proposed scheme has been tested on two glioma datasets, TCGA dataset for IDH-mutation prediction (molecular-based glioma subtype classification) and MICCAI dataset for glioma grading. Our results have shown good performance (with test accuracies 86.53% on TCGA dataset and 90.70% on MICCAI dataset). Conclusions: The proposed scheme is effective for glioma IDH-mutation prediction and glioma grading, and its performance is comparable to the state-of-the-art.
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