The image represents the results of passing the first slice of the Kasthuri++ testing data through trained M1 and M2 models to obtain their respective predicted outputs.

<p>In (a), the predicted outputs from both models exhibit false negatives, indicated by the yellow circles within the image. (b) shows the one found false negative object. To address this limitation, we propose an ensemble learning weight averaging fusion method, shown in (c), which combines t...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Yubraj Gupta (7465394) (author)
مؤلفون آخرون:	Rainer Heintzmann (112278) (author), Carlos Costa (410100) (author), Rui Jesus (20329654) (author), Eduardo Pinho (14312032) (author)
منشور في:	2024
الموضوعات:	Biochemistry Cell Biology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified weighted fusion technique slowing disease progression proposed fusion method produce adenosine triphosphate mean jaccard index mean error rate including neurodegenerative disorders data acquisition techniques containing numerous cells 3d cnn algorithms sem images using primary mitochondrial diseases xlink "> mitochondria efficiently segment mitochondria minimal user intervention overall segmentation results minimal intervention segmentation task pipelines using mitochondrial dna urocell dataset urgent need recent advances predicted outputs predict regions potentially aid pixel accuracy nutrition molecules methodology minimizes intracellular organelles interest specific individual predictions high score four datasets early detection dice coefficient deep learning cellular fuel become feasible automated tools article proposes architecture combines
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The image represents the results of passing the first slice of the Kasthuri++ testing data through trained M1 and M2 models to obtain their respective predicted outputs.

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