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Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks
- Publication Year :
- 2023
-
Abstract
- Discovering evolutionary traits that are heritable across species on the tree of life (also referred to as a phylogenetic tree) is of great interest to biologists to understand how organisms diversify and evolve. However, the measurement of traits is often a subjective and labor-intensive process, making trait discovery a highly label-scarce problem. We present a novel approach for discovering evolutionary traits directly from images without relying on trait labels. Our proposed approach, Phylo-NN, encodes the image of an organism into a sequence of quantized feature vectors -- or codes -- where different segments of the sequence capture evolutionary signals at varying ancestry levels in the phylogeny. We demonstrate the effectiveness of our approach in producing biologically meaningful results in a number of downstream tasks including species image generation and species-to-species image translation, using fish species as a target example.
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer Vision and Pattern Recognition (cs.CV)
Image and Video Processing (eess.IV)
FOS: Electrical engineering, electronic engineering, information engineering
Computer Science - Computer Vision and Pattern Recognition
Electrical Engineering and Systems Science - Image and Video Processing
Machine Learning (cs.LG)
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....7a88e82b7484f5396c90145aa9f5ddd7