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References from Potentials and limitations in the application of Convolutional Neural Networks for mosquito species identification using wing images. Local targets link to admitted publications; unresolved targets remain external evidence.
The effect of global change on mosquito-borne disease
2019 · External reference
Effects of climate change and human activities on vector-borne diseases
10.1038/s41579-024-01026-0 · 2024 · External reference
Dissecting vectorial capacity for mosquito-borne viruses
10.1016/j.coviro.2015.10.003 · 2015 · External reference
10.1353/book.79680
10.1353/book.79680 · 2021 · External reference
Nextgen Vector Surveillance Tools: sensitive, specific, cost-effective and epidemiologically relevant
10.1186/s12936-020-03494-0 · 2020 · External reference
Unresolved reference
2021 · External reference
Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification
10.1038/s41598-022-24522-w · 2022 · External reference
Species identification of phlebotomine sandflies using deep learning and wing interferential pattern (WIP)
10.1038/s41598-023-48685-2 · 2023 · External reference
Image-based species identification of wild bees using convolutional neural networks
10.1016/j.ecoinf.2019.101017 · 2020 · External reference
A Swin Transformer-based model for mosquito species identification
10.1038/s41598-022-21017-6 · 2022 · External reference
Robust mosquito species identification from diverse body and wing images using deep learning
10.1186/s13071-024-06459-3 · 2024 · External reference
Towards transforming malaria vector surveillance using VectorBrain: a novel convolutional neural network for mosquito species, sex, and abdomen status identifications
10.1038/s41598-024-71856-8 · 2024 · External reference
Why do deep convolutional networks generalize so poorly to small image transformations?
2019 · External reference
Recognition in Terra Incognita.
10.1007/978-3-030-01270-0_28 · 2018 · External reference
Underspecification presents challenges for credibility in modern machine learning
2022 · External reference
Shortcut learning in deep neural networks
10.1038/s42256-020-00257-z · 2020 · External reference
Image-based taxonomic classification of bulk insect biodiversity samples using deep learning and domain adaptation
10.1111/syen.12583 · 2023 · External reference
Automated tick classification using deep learning and its associated challenges in citizen science
10.1038/s41598-025-10265-x · 2025 · External reference
A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images
10.1038/s41598-024-53631-x · 2024 · External reference
Comprehensive Mosquito Wing Image Repository for Advancing Research on Geometric Morphometric- and AI-Based Identification
10.1038/s41597-025-05043-3 · 2025 · External reference
Unresolved reference
2018 · External reference
scikit-image: image processing in Python
10.7717/peerj.453 · 2014 · External reference
Adaptive grey level assignment in CT scan display
1984 · External reference
Unresolved reference
2009 · External reference
Unresolved reference
2019 · External reference
10.1186/s13071-024-06459-3
10.1186/s13071-024-06459-3 · External reference
Unresolved reference
2019 · External reference
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
10.1007/s11263-019-01228-7 · 2020 · External reference
10.1109/wacv.2018.00097
10.1109/wacv.2018.00097 · External reference
UMAP: Uniform Manifold Approximation and Projection
10.21105/joss.00861 · 2018 · External reference
Unresolved reference
2018 · External reference
10.1109/iccv48922.2021.00125
10.1109/iccv48922.2021.00125 · External reference
Unresolved reference
External reference
Unresolved reference
2021 · External reference
Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest
10.1038/s41598-023-44945-3 · 2023 · External reference
Application of wings interferential patterns (WIPs) and deep learning (DL) to classify some Culex. spp (Culicidae) of medical or veterinary importance
10.1038/s41598-025-08667-y · 2025 · External reference
Assessment of expertise in morphological identification of mosquito species (Diptera, Culicidae) using photomicrographs
10.1051/parasite/2022045 · 2022 · External reference
Unresolved reference
2021 · External reference
Unresolved reference
External reference
Generalized Out-of-Distribution Detection: A Survey
10.1007/s11263-024-02117-4 · 2024 · External reference
Unresolved reference
2022 · External reference
Unresolved reference
2018 · External reference
Delimiting cryptic morphological variation among human malaria vector species using convolutional neural networks
10.1371/journal.pntd.0008904 · 2020 · External reference
Distribution chart for Euro-Mediterranean mosquitoes (western Palaearctic region)
2019 · External reference
Unresolved reference
2018 · External reference
Genetic and phenotypic variation in central and northern European populations of Aedes (Aedimorphus) vexans (Meigen, 1830) (Diptera, Culicidae)
10.1111/jvec.12208 · 2016 · External reference
Geometric morphometric analysis of Colombian Anopheles albimanus (Diptera: Culicidae) reveals significant effect of environmental factors on wing traits and presence of a metapopulation
10.1016/j.actatropica.2014.03.020 · 2014 · External reference
Genetic diversity and wing geometric morphometrics among four populations of Aedes aegypti (Diptera: Culicidae) from Benin
10.1186/s13071-023-05943-6 · 2023 · External reference
Effective surveillance systems for vector-borne diseases in urban settings and translation of the data into action: a scoping review
10.1186/s40249-018-0473-9 · 2018 · External reference
Species identification of phlebotomine sandflies using deep learning and Wing Interferential Pattern (WIP)
2023 · External reference
