# Special Issue: Artificial Intelligence Technology in Medical Image Analysis
###### Abstract
The primary objective of the study is to augment the accuracy of machine learning methods in detecting tumor-damaged tissues in histopathological whole slide images. The most promising results were attained through the utilization of a multi-model ensemble, characterized by an impressive AUC (area under the curve) value of 0.97. In the sixth paper, [PERSON] et al. introduce a foundational multimodality thalamus segmentation pipeline that incorporates diffusion MRI (dMRI) and T1-weighted images through a CNN approach, and can obtain remarkable segmentation accuracy. This marks a significant contribution to the automated diagnosis of various diseases like multiple sclerosis and Parkinson's, where the segmentation and shape assessment of the thalamus play a crucial role. Additionally, the authors established an open benchmark, featuring a substantial, preprocessed, publicly available dataset. This dataset includes co-registered T1-weighted and dMRI images, manual thalamic masks, masks generated by three distinct automated methods, and a STAPLE consensus of the masks. As a result, this initiative opens up new possibilities for advancing research in the field. In the seventh paper, [PERSON] and [PERSON], in response to the dependence of deep neural networks on the large amount of reliable image data, propose a semi-supervised framework for medical image classification that integrates semi-supervised classification with unsupervised deep clustering. The methodology involves the iterative execution of two tasks--semi-supervised classification and unsupervised deep clustering--to attach label information to unlabeled data. This iterative process assists the model in extracting semantic information from unlabeled data, while mitigating the risk of overfitting to the limited labeled data available. To validate their proposed method, the authors conduct a comparative experiment using public benchmark medical image datasets. Compared to earlier solutions, this method enhances model robustness and mitigates the impact of outliers. In the eighth paper, [PERSON] et al. introduce a method for optimizing hyperparameters in deep networks employed for automated classification of histopathological images. This method is based on model fusion within the weight space. The authors utilize the cyclical learning rate strategy to fine-tune individual models and suggest a ranking strategy, considering accuracy and diversity, for the selection of candidate models. The fusion of weights from these constituent models is aimed at producing a model with performance levels better aligned with the expected value, potentially enhancing the model's generalization ability. Furthermore, the proposed strategy significantly reduces the proposed model's testing cost. Experiments conducted on two datasets of histopathological images reveal the effectiveness of the proposed model compared to baseline architectures like ResNet, VGG, DenseNet, and their ensemble versions. In the ninth paper, [PERSON] et al. propose a methodology for the effective and accurate classification of lung diseases, specifically targeting COVID-19, pneumonia, and normal cases, based on chest X-ray images. The research focuses on the optimal usage of CNN and LSTM architectures, from the perspective of evaluation metrics and training efficiency. Data augmentation is deployed to overcome the adverse effects of imbalanced datasets. The best performing model, featuring five convolutional blocks and two LSTM layers needs no augmentation and achieves remarkable Dice scores of up to 0.99. In the tenth paper, [PERSON] et al. propose a segmentation method for the thoracic cavity in dynamic contrast-enhanced NRI data, to assist with the correct diagnosis of breast cancer. The main challenge is the fact that various internal organs can have very similar appearances to malignant breast lesions after the injection of the contrast agent. The proposed approach employs various CNN-based architectures, assessing the effect of several enhancements given to the baseline models.