Zeinab Sherkatghanad

BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averaging

Zeinab Sherkatghanad

Moloud Abdar

Mohammadreza Bakhtyari

Vladimir Makarenkov

Test-time augmentation (TTA) is a well-known technique employed during the testing phase of computer vision tasks. It involves aggregating m… (voir plus)ultiple augmented versions of input data. Combining predictions using a simple average formulation is a common and straightforward approach after performing TTA. This paper introduces a novel framework for optimizing TTA, called BayTTA (Bayesian-based TTA), which is based on Bayesian Model Averaging (BMA). First, we generate a model list associated with different variations of the input data created through TTA. Then, we use BMA to combine model predictions weighted by their respective posterior probabilities. Such an approach allows one to take into account model uncertainty, and thus to enhance the predictive performance of the related machine learning or deep learning model. We evaluate the performance of BayTTA on various public data, including three medical image datasets comprising skin cancer, breast cancer, and chest X-ray images and two well-known gene editing datasets, CRISPOR and GUIDE-seq. Our experimental results indicate that BayTTA can be effectively integrated into state-of-the-art deep learning models used in medical image analysis as well as into some popular pre-trained CNN models such as VGG-16, MobileNetV2, DenseNet201, ResNet152V2, and InceptionRes-NetV2, leading to the enhancement in their accuracy and robustness performance.

2024-06-25

ArXiv (prépublication)

doi.org

arxiv.org

A self-attention-based CNN-Bi-LSTM model for accurate state-of-charge estimation of lithium-ion batteries

Zeinab Sherkatghanad

Amin Ghazanfari

Vladimir Makarenkov

2024-05-01

Journal of Energy Storage (publié)

doi.org

A self-attention-based CNN-Bi-LSTM model for accurate state-of-charge estimation of lithium-ion batteries