interactive visualization
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Author(s):  
Saptashwa Mitra ◽  
Daniel Rammer ◽  
Shrideep Pallickara ◽  
Sangmi Lee Pallickara

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Martin Dyrba ◽  
Moritz Hanzig ◽  
Slawek Altenstein ◽  
Sebastian Bader ◽  
Tommaso Ballarini ◽  
...  

Abstract Background Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer’s disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge. Methods We trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection. Results Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC ≥ 0.91) and moderate accuracy for amnestic MCI versus controls (AUC ≈ 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson’s r ≈ −0.86, p < 0.001). Conclusion The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relevance scores as well as the high performance achieved in independent samples support the validity of the CNN models in the detection of AD-related MRI abnormalities. The presented data-driven and hypothesis-free CNN modeling approach might provide a useful tool to automatically derive discriminative features for complex diagnostic tasks where clear clinical criteria are still missing, for instance for the differential diagnosis between various types of dementia.


2021 ◽  
Vol 41 (6) ◽  
pp. 7-12
Author(s):  
Emma Beauxis-Aussalet ◽  
Michael Behrisch ◽  
Rita Borgo ◽  
Duen Horng Chau ◽  
Christopher Collins ◽  
...  

Author(s):  
Mochamad Nizar Palefi Ma'Ady ◽  
Shinta Amalia Kusuma Wardhani

2021 ◽  
Author(s):  
Claudio Scheer ◽  
Renato B. Hoffmann ◽  
Dalvan Griebler ◽  
Isabel H. Manssour ◽  
Luiz G. Fernandes

Profiling tools are essential to understand the behavior of parallel applications and assist in the optimization process. However, tools such as Perf generate a large amount of data. This way, they require significant storage space, which also complicates reasoning about this large volume of data. Therefore, we propose VisPerf: a tool-chain and an interactive visualization dashboard for Perf data. The VisPerf tool-chain profiles the application and pre-processes the data, reducing the storage space required by about 50 times. Moreover, we used the visualization dashboard to quickly understand the performance of different events and visualize specific threads and functions of a real-world application.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Qihua Liang ◽  
Stefano Lonardi

Abstract Background The pan-genome of a species is the union of the genes and non-coding sequences present in all individuals (cultivar, accessions, or strains) within that species. Results Here we introduce PGV, a reference-agnostic representation of the pan-genome of a species based on the notion of consensus ordering. Our experimental results demonstrate that PGV enables an intuitive, effective and interactive visualization of a pan-genome by providing a genome browser that can elucidate complex structural genomic variations. Conclusions The PGV software can be installed via conda or downloaded from https://github.com/ucrbioinfo/PGV. The companion PGV browser at http://pgv.cs.ucr.edu can be tested using example bed tracks available from the GitHub page.


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