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Deep Learning for lesion and thrombus segmentation from cerebral MRI

Abstract : Deep learning, the world's best set of methods for identifying ob-jects on images. Stroke, a deadly disease whose treatment requiresidentifying objects on medical imaging. Sounds like an obvious com-bination yet it is not trivial to marry the two. Segmenting the lesionfrom stroke MRI has had some attention in literature but thrombussegmentation is still uncharted area. This work shows that contem-porary convolutional neural network architectures cannot reliablyidentify the thrombus on stroke MRI. Also it is demonstrated whythese models don't work on this problem. With this knowledge arecurrent neural network architecture, the logic LSTM, is developedthat takes into account the way medical doctors identify the throm-bus. Not only this architecture provides the first reliable thrombusidentification, it also provides new insights to neural network design.Especially the methods for increasing the receptive field are enrichedwith a new parameter free option. And last but not least the logicLSTM also improves the results of lesion segmentation by providinga lesion segmentation with human level performance.
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https://theses.hal.science/tel-03592570
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Submitted on : Tuesday, March 1, 2022 - 2:21:09 PM
Last modification on : Thursday, March 3, 2022 - 3:32:26 AM
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2019SACLE044.pdf
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  • HAL Id : tel-03592570, version 1

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Jonathan Kobold. Deep Learning for lesion and thrombus segmentation from cerebral MRI. Image Processing [eess.IV]. Université Paris Saclay (COmUE), 2019. English. ⟨NNT : 2019SACLE044⟩. ⟨tel-03592570⟩

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