# Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges
## 5 Applications
### Intelligent Medical Assisted Diagnosis  
In the medical field, the development of deep learning has brought revolutionary development to many aspects, including diagnosis [125; 126]. However, the above data-driven methods inevitably require a large amount of labeled data [127; 128]. However, labeling medical images is time-consuming and labor-intensive, which also requires specific professional knowledge [129; 130]. Therefore, it is efficient to use active learning to select samples that are difficult to predict by the model for selective labeling. There is much research studying active learning in the medical field. We summarized the most typical works in the Table 7.  
[PERSON] et al. [22; 26] introduced transfer learning and data enhancement into active learning. By measuring the uncertainty and diversity, the proposed AIFT framework achieved SOTA performance in the biomedical image analysis. [PERSON] et al. [131] introduced active learning into ultrasound classification for COVID19-assisted diagnosis. In order to actively reduce the labeling efforts, the proposed method combined least confidence and  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Applications** & **Comments** & **Implementation** & **Evaluation** \\ \hline Medical image & \(\rhd\) Combined active learning, incremental fine-tuning, and transfer learning. & \(\rhd\) Network: AlexNet pretrained by imagenet & \(\rhd\) poly poly detection: \(\downarrow\) 86\% labels. \(\rhd\) \\ Medical image & \(\rhd\) Combined active learning, incremental fine-tuning, and transfer learning. & \(\rhd\) Selection strategy: & \(\rhd\) pulmonary embolism detection: \(\downarrow\) 80\% labels. \(\rhd\) colonoscopy frame classification: \(\downarrow\) 82\% labels. \(\rhd\) scene classification: \(\downarrow\) 35\% labels. \\ \hline COVID-19 & \(\rhd\) The first work that introduced active learning into ultrasound classification [131] & \(\rhd\) Score-function: Least confidence: \(\rhd\) COVID19-LUSMS v1: \(\downarrow\) 80\% labels. \\ \hline Brain tumor & \(\rhd\) Sampling candidates by discarding subsets of training samples with the highest and lowest uncertainty scores. & \(\rhd\) Backbone: ResNet50 pretrained by imagenet \\ \hline Diabetic retinopathy classification [132] & \(\rhd\) The first work that introduced active learning into lane detection in autonomous driving. & \(\rhd\) Score-function: Combined entropy and Kullback-Leibler(KL) divergence: \(E(X)=-\sum_{i=1}^{N}P(x_{i})\log P(x_{i})\)\(\mathrm{D}(\rho\|\mathbf{q})=\sum_{i=1}^{n}P(x_{i})\log\frac{P(x_{i})}{Q(x_{i})}\) \\ \hline Diabetic retinopathy classification [136] & \(\rhd\) The first work that introduced active learning into lane detection in autonomous driving. & \(\rhd\) Bayesian convolutional neural network (BCNN): Monte-Carlo drop-out & \(\rhd\) APTOS 2019 [137]: AUC = 0.99 (multi-class classification) \\ \hline \multirow{4}{*}{Histo- pathology image analysis [138]} & \(\rhd\) The first work that proposed an AL framework (PathAL) to dynamically identify important samples to annotate and to distinguish noisy from hard ones, and selected the most set. & \(\rhd\) Backbone: EfficientNet-B0 [139] \\ \hline \multirow{4}{*}{Gastric adenocarcinoma and colorectal cancer [143] cancer [143]} & \(\rhd\) Noisy sample detector: & \(\rhd\) Noisy sample detector: O2U-Net [140] \\  & & & \\ \cline{1-1} \cline  
entropy selection strategies. [PERSON] et al. [132] combined entropy and Kullback-Leibler(KL) divergence for uncertainty-based sampling. Apart from the active learning, they also carried out transfer learning from imagenet-pretrained AlexNet [146] to MRI (MICCAI BRATS 2019 dataset) [133; 134; 135]. The proposed transfer learning framework reduced the annotation cost while maintaining the stability and robustness of the model performance for brain tumor classification. [PERSON] et al. [136] integrated Bayesian-based CNN and uncertainty-based active learning method, where active learning was applied to the pool-based sampling and query by committee scenarios.