# On the use of uncertainty in classifying Aedes Albopictus mosquitoes

arXiv: https://arxiv.org/abs/2110.15912 · HTML: https://mlsanity.com/paper/2110.15912/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 48 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference](https://mlsanity.com/paper/1506.02158/) (2015)
- [BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors](https://mlsanity.com/paper/1903.03838/) (2019)
- [U-CAM: Visual Explanation using Uncertainty based Class Activation Maps](https://mlsanity.com/paper/1908.06306/) (2019)
- [Dropout Inference in Bayesian Neural Networks with Alpha-divergences](https://mlsanity.com/paper/1703.02914/) (2017)
- [How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks](https://mlsanity.com/paper/2006.09000/) (2020)
- [Performance measures for classification systems with rejection](https://mlsanity.com/paper/1504.02763/) (2015)
- [On uncertainty estimation in active learning for image segmentation](https://mlsanity.com/paper/2007.06364/) (2020)
- [Uncertainty Modeling of Contextual-Connections between Tracklets for Unconstrained Video-based Face Recognition](https://mlsanity.com/paper/1905.02756/) (2019)
- [Detecting Aedes Aegypti Mosquitoes through Audio Classification with Convolutional Neural Networks](https://mlsanity.com/paper/2008.09024/) (2020)
- [A Framework based on Deep Neural Networks to Extract Anatomy of Mosquitoes from Images](https://mlsanity.com/paper/2007.11052/) (2020)

## Built on by (most cited first, 4 of 4)

- [Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs](https://mlsanity.com/paper/2212.14806/) (2022)
- [Deep Learning and Computer Vision for Glaucoma Detection: A Review](https://mlsanity.com/paper/2307.16528/) (2023)
- [ADVISE: ADaptive Feature Relevance and VISual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/2203.01289/) (2022)
- [Acoustic Identification of Ae. aegypti Mosquitoes using Smartphone Apps and Residual Convolutional Neural Networks](https://mlsanity.com/paper/2306.10091/) (2023)

## Abstract (arXiv)

The re-emergence of mosquito-borne diseases (MBDs), which kill hundreds of thousands of people each year, has been attributed to increased human population, migration, and environmental changes. Convolutional neural networks (CNNs) have been used by several studies to recognise mosquitoes in images provided by projects such as Mosquito Alert to assist entomologists in identifying, monitoring, and managing MBD. Nonetheless, utilising CNNs to automatically label input samples could involve incorrect predictions, which may mislead future epidemiological studies. Furthermore, CNNs require large numbers of manually annotated data. In order to address the mentioned issues, this paper proposes using the Monte Carlo Dropout method to estimate the uncertainty scores in order to rank the classified samples to reduce the need for human supervision in recognising Aedes albopictus mosquitoes. The estimated uncertainty was also used in an active learning framework, where just a portion of the data from large training sets was manually labelled. The experimental results show that the proposed classification method with rejection outperforms the competing methods by improving overall performance and reducing entomologist annotation workload. We also provide explainable visualisations of the different regions that contribute to a set of samples' uncertainty assessment.