Article

MorphQ: label-free quantification and visualisation of complex morphology from standardised specimen images

2026-08-14

Abstract excerpt

1. Quantifying complex morphology from images remains difficult because predefined descriptors capture only selected traits. Yet, supervised machine learning models for images require labels and often produce task-specific features that are hard to interpret as biological traits. 2. We present MorphQ, a label-free, self-supervised method that learns a quantitative morphospace from standardised specimen images. It...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: a408593b-65f0-5b3f-8d2f-715eeefa9bb7
DOI: 10.64898/2026.08.11.744091

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

MorphQ: label-free quantification and visualisation of complex morphology from standardised specimen imagesDOI 10.64898/2026.08.11.744091Read

Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics.Semantic proximity Read

Orangutan: an R package for analyzing and visualizing phenotypic data in the context of ecology and systematicsSemantic proximity Read

LizardMorph: A generalizable machine learning framework for automated anatomical landmark detection in digital imagesSemantic proximity Read

Generative A.I. helps extract ecological meaning from the complex three dimensional shapes of bird billsSemantic proximity Read

Automated landmark and semilandmark annotation for wing geometric morphometrics in Diptera using deep learningSemantic proximity Read

Automated Landmarking via Multiple TemplatesSemantic proximity Read

Multidimensional trait morphology predicts ecology across ant lineagesSemantic proximity Read

Automated landmarking via multiple templates.Semantic proximity Read

Leveraging synthetic data produced from museum specimens to train adaptable species classification modelsSemantic proximity Read

Unsupervised machine learning for species delimitation, integrative taxonomy, and biodiversity conservation.Semantic proximity Read

Automated Specimen Triage for Dark Taxa: Deep Learning Enables Orientation, Sex Identification, and Anatomical Segmentation from Robotic ImagingSemantic proximity Read

Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning.Semantic proximity Read