Performance evaluation of the conventional and revised LiDRL methods.

Performance evaluation of the conventional and revised LiDRL methods.

<p>Performance evaluation of the conventional and revised LiDRL methods.</p>

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Bibliographic Details
Main Author:	Yoshitomi Harada (22311475) (author)
Other Authors:	Haruto Nishida (13920772) (author), Keiko Matsuura (212913) (author)
Published:	2025
Subjects:	Biochemistry Cell Biology Computational Biology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified similar sizes across lipid droplet detection lipid droplet characteristics pathological tissue images enabling consistent extraction pathological images lipid droplets xlink "> side functions revised method reinforcement learning rank levels previously proposed method using limited dataset improvements increased heat map grayscale contrast could serve analysis facilitates
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