| 11. |
- Gomariz, Alvaro, et al.
(författare)
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Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation
- 2022
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Ingår i: Medical Image Computing and Computer Assisted Intervention, MICCAI 2022, pt viii. - Cham : Springer Nature. - 9783031164521 - 9783031164514 ; , s. 351-361
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Konferensbidrag (refereegranskat)abstract
- Accurate segmentation of retinal fluids in 3D Optical Coherence Tomography images is key for diagnosis and personalized treatment of eye diseases. While deep learning has been successful at this task, trained supervised models often fail for images that do not resemble labeled examples, e.g. for images acquired using different devices. We hereby propose a novel semi-supervised learning framework for segmentation of volumetric images from new unlabeled domains. We jointly use supervised and contrastive learning, also introducing a contrastive pairing scheme that leverages similarity between nearby slices in 3D. In addition, we propose channel-wise aggregation as an alternative to conventional spatial-pooling aggregation for contrastive feature map projection. We evaluate our methods for domain adaptation from a (labeled) source domain to an (unlabeled) target domain, each containing images acquired with different acquisition devices. In the target domain, our method achieves a Dice coefficient 13.8% higher than SimCLR (a state-of-the-art contrastive framework), and leads to results comparable to an upper bound with supervised training in that domain. In the source domain, our model also improves the results by 5.4% Dice, by successfully leveraging information from many unlabeled images.
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| 12. |
- Jaume, Guillaume, et al.
(författare)
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Quantifying Explainers of Graph Neural Networks in Computational Pathology
- 2021
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Ingår i: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665445092 - 9781665445108 ; , s. 8102-8112
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Konferensbidrag (refereegranskat)abstract
- Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by pathologists. In this work, we address this by adopting biological entity-based graph processing and graph explainers enabling explanations accessible to pathologists. In this context, a major challenge becomes to discern meaningful explainers, particularly in a standardized and quantifiable fashion. To this end, we propose herein a set of novel quantitative metrics based on statistics of class separability using pathologically measurable concepts to characterize graph explainers. We employ the proposed metrics to evaluate three types of graph explainers, namely the layer-wise relevance propagation, gradient-based saliency, and graph pruning approaches, to explain Cell-Graph representations for Breast Cancer Subtyping. The proposed metrics are also applicable in other domains by using domain-specific intuitive concepts. We validate the qualitative and quantitative findings on the BRACS dataset, a large cohort of breast cancer RoIs, by expert pathologists.
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| 13. |
- Pati, Pushpak, et al.
(författare)
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Hierarchical graph representations in digital pathology
- 2022
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Ingår i: Medical Image Analysis. - : Elsevier. - 1361-8415 .- 1361-8423. ; 75
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Tidskriftsartikel (refereegranskat)abstract
- Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs, capturing the cell-microenvironment, to depict the tissue. These allow for utilizing graph theory and machine learning to map the tissue representation to tissue functionality, and quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity graph representation of tissue specimens to model the hierarchical compositions that encode histological entities as well as their intra-and inter-entity level interactions. Subsequently, a hierarchical graph neural network is proposed to operate on the hierarchical entity-graph and map the tissue structure to tissue functionality. Specifically, for input histology images, we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue ( HACT ) graph representations, and devise HACT-Net, a message passing graph neural network, to classify the HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of Haematoxylin & Eosin stained breast tumor regions-of-interest, to evaluate and benchmark our proposed methodology against pathologists and state-of-the-art computer-aided diagnostic approaches. Through comparative assessment and ablation studies, our proposed method is demonstrated to yield superior classification results compared to alternative methods as well as individual pathologists. The code, data, and models can be accessed at https://github.com/histocartography/hact-net . (c) 2021 Elsevier B.V. All rights reserved.
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| 14. |
- Pati, Pushpak, et al.
(författare)
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Weakly supervised joint whole-slide segmentation and classification in prostate cancer
- 2023
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Ingår i: Medical Image Analysis. - : Elsevier. - 1361-8415 .- 1361-8423.
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Tidskriftsartikel (refereegranskat)abstract
- The identification and segmentation of histological regions of interest can provide significant support to pathologists in their diagnostic tasks. However, segmentation methods are constrained by the difficulty in obtaining pixel-level annotations, which are tedious and expensive to collect for whole-slide images (WSI). Though several methods have been developed to exploit image-level weak-supervision for WSI classification, the task of segmentation using WSI-level labels has received very little attention. The research in this direction typically require additional supervision beyond image labels, which are difficult to obtain in real-world practice. In this study, we propose WholeSIGHT, a weakly-supervised method that can simultaneously segment and classify WSIs of arbitrary shapes and sizes. Formally, WholeSIGHT first constructs a tissue-graph representation of WSI, where the nodes and edges depict tissue regions and their interactions, respectively. During training, a graph classification head classifies the WSI and produces node-level pseudo-labels via post-hoc feature attribution. These pseudo-labels are then used to train a node classification head for WSI segmentation. During testing, both heads simultaneously render segmentation and class prediction for an input WSI. We evaluate the performance of WholeSIGHT on three public prostate cancer WSI datasets. Our method achieves state-of-the-art weakly-supervised segmentation performance on all datasets while resulting in better or comparable classification with respect to state-of-the-art weakly-supervised WSI classification methods. Additionally, we assess the generalization capability of our method in terms of segmentation and classification performance, uncertainty estimation, and model calibration. Our code is available at: https://github.com/histocartography/wholesight.
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| 15. |
- Pean, Fabien, et al.
(författare)
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Computational analysis of subscapularis tears and pectoralis major transfers on muscular activity
- 2022
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Ingår i: Clinical Biomechanics. - : Elsevier. - 0268-0033 .- 1879-1271. ; 92
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Tidskriftsartikel (refereegranskat)abstract
- Background: Pectoralis major is the most common muscle transfer procedure to restore joint function after subscapularis tears. Limited information is available on how the neuromuscular system adjusts to the new configuration, which could explain the mixed outcomes of the procedure. The purpose of this study is to assess how muscles activation patterns change after pectoralis major transfers and report their biomechanical implications.Methods: We compare how muscle activation change with subscapularis tears and after its treatment by pectoralis major transfers of the clavicular, sternal, or both these segments, during three activities of daily living and a computational musculoskeletal model of the shoulder.Findings: Our results indicate that subscapularis tears require a compensatory activation of the supraspinatus and is accompanied by a reduced co-contraction of the infraspinatus, both of which can be partially recovered after transfer. Furthermore, although the pectoralis major acts asynchronously to the subscapularis before the transfer, its activation pattern changes significantly after the transfer.Interpretation: The capability of a transferred muscle segment to activate similarly to the intact subscapularis is found to be dependent on the given motion. Differences in the activation patterns between intact subscapularis and the segments of pectoralis major may explain the difficulty in adapting psycho-motor patterns during the rehabilitation period. There by, rehabilitation programs could benefit from targeted training on specific motion and biofeedback programs. Finally, the condition of the anterior deltoid should be considered to improve joint function.
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| 16. |
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| 17. |
- Teuscher, Alina C., et al.
(författare)
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Longevity interventions modulate mechanotransduction and extracellular matrix homeostasis in C. elegans
- 2024
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- Dysfunctional extracellular matrices (ECM) contribute to aging and disease. Repairing dysfunctional ECM could potentially prevent age-related pathologies. Interventions promoting longevity also impact ECM gene expression. However, the role of ECM composition changes in healthy aging remains unclear. Here we perform proteomics and in-vivo monitoring to systematically investigate ECM composition (matreotype) during aging in C. elegans revealing three distinct collagen dynamics. Longevity interventions slow age-related collagen stiffening and prolong the expression of collagens that are turned over. These prolonged collagen dynamics are mediated by a mechanical feedback loop of hemidesmosome-containing structures that span from the exoskeletal ECM through the hypodermis, basement membrane ECM, to the muscles, coupling mechanical forces to adjust ECM gene expression and longevity via the transcriptional co-activator YAP-1 across tissues. Our results provide in-vivo evidence that coordinated ECM remodeling through mechanotransduction is required and sufficient to promote longevity, offering potential avenues for interventions targeting ECM dynamics.
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| 18. |
- Thandiackal, Kevin, et al.
(författare)
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Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images
- 2022
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Ingår i: COMPUTER VISION, ECCV 2022, PT XXI. - Cham : Springer Nature. - 9783031198021 - 9783031198038 ; , s. 699-715
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Konferensbidrag (refereegranskat)abstract
- Multiple Instance Learning (MIL) methods have become increasingly popular for classifying gigapixel-sized Whole-Slide Images (WSIs) in digital pathology. Most MIL methods operate at a single WSI magnification, by processing all the tissue patches. Such a formulation induces high computational requirements and constrains the contextualization of the WSI-level representation to a single scale. Certain MIL methods extend to multiple scales, but they are computationally more demanding. In this paper, inspired by the pathological diagnostic process, we propose ZoomMIL, a method that learns to perform multi-level zooming in an end-to-end manner. ZoomMIL builds WSI representations by aggregating tissue-context information from multiple magnifications. The proposed method outperforms the state-of-the-art MIL methods in WSI classification on two large datasets, while significantly reducing computational demands with regard to Floating-Point Operations (FLOPs) and processing time by 40-50x. Our code is available at: https://github. com/histocartography/zoommil.
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| 19. |
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| 20. |
- Thandiackal, Kevin, et al.
(författare)
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Multi-scale Feature Alignment for Continual Learning of Unlabeled Domains
- 2024
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Ingår i: IEEE Transactions on Medical Imaging. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0062 .- 1558-254X.
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Tidskriftsartikel (refereegranskat)abstract
- Methods for unsupervised domain adaptation (UDA) help to improve the performance of deep neural networks on unseen domains without any labeled data. Especially in medical disciplines such as histopathology, this is crucial since large datasets with detailed annotations are scarce. While the majority of existing UDA methods focus on the adaptation from a labeled source to a single unlabeled target domain, many real-world applications with a long life cycle involve more than one target domain. Thus, the ability to sequentially adapt to multiple target domains becomes essential. In settings where the data from previously seen domains cannot be stored, e.g., due to data protection regulations, the above becomes a challenging continual learning problem. To this end, we propose to use generative feature-driven image replay in conjunction with a dual-purpose discriminator that not only enables the generation of images with realistic features for replay, but also promotes feature alignment during domain adaptation. We evaluate our approach extensively on a sequence of three histopathological datasets for tissue-type classification, achieving state-of-the-art results. We present detailed ablation experiments studying our proposed method components and demonstrate a possible use-case of our continual UDA method for an unsupervised patch-based segmentation task given high-resolution tissue images. Our code is available at: https://github.com/histocartography/multi-scale-feature-alignment.
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