_applied_  
_sciences_  
_Editorial_  
**Special Issue on Image Processing Techniques for**  
**Biomedical Applications**  
**[PERSON] 1,*\({}^{\copyright\copyright\)  
techniques employed, the results obtained, and the conclusions derived. The purpose is to identify the main strengths and weaknesses in the overview of this topic. In [8], a review of early and recent studies on the quantification of liver fibrosis is described, presenting the datasets used for the analysis, the employed image processing techniques, the obtained results, and the derived conclusions. The purpose is to identify the major strengths and gray areas of this topic. A deep-learning model with metric embeddings was proposed in [9]. The authors achieved cell nuclei segmentation and experimented on a collection of large-scale, multi-tissue microscopy images. The proposed method showed outstanding performance, particularly for H&E-stained images. In [10], the authors addressed the problem of melanoma detection and classification from dermatologic images. They exploited hand-created features and a multilayer perceptron to distinguish between malignant and benign melanoma and proposed a novel, robust, and efficient method. Based on the results, it was shown that its use would improve reliability levels compared with conventional methods. The authors of [11] proposed a novel method for recognizing white blood cells from microscopic blood images. The purpose was to classify them based on whether they were healthy or affected by leukemia. The system achieved near-perfect results in all tested datasets; therefore, it can reduce the workload and subjectivity of operators and improve diagnostic results. A method based on fully convolutional dense networks, or U-Nets, is described in [12]. The goal is to realize semantic segmentation of the spinal cord in medical ultrasound images. Finally, the authors state that the proposal can be applied to real-time images of the spinal cord to confirm spinal cord decompression, or occasionally to evaluate a tumor adjacent to the spinal cord. A strategy for converting human biomedical image datasets, such as X-rays and computed tomography (CT) scans, into 3D printable files by manually highlighting anatomical subregions of a given structure and cost-effectively 3D printing the resulting models in multicolor is proposed in [13]. A transfer learning method based on the combination of XDAWN spatial filter and Riemannian Geometry classifier (RGC) has been proposed to reduce cross-subject variability from electroencephalogram (EEG) signals. The experimental results demonstrated the proposed algorithm's potential, making the data from different subjects comparable and therefore generalizing a fixed classification method for all subjects [14]. In the work [15], a parallel microwave image reconstruction algorithm based on Apache Spark on high-performance computing and Google Cloud Platform was proposed. It achieved an average speedup increase of 28.56 times on four homogeneous computation nodes, and therefore was able to produce images in an acceptable timeframe. The segmentation of retinal fundus images was addressed in [16]. The authors proposed a novel multi-path recurrent U-Net architecture and performed the experimental evaluation on the optic disc, optic cup, and retinal vessel segmentation, demonstrating outstanding performance and robustness to the presence of pathological regions. A novel technique is used to improve the overall accuracy and performance of the pretrained networks for breast cancer classification [17]. It is based on the so-called double-shot transfer learning (DSTL), that, before the common fine-tuning on the target dataset, updates the learnable parameters of the pretrained network on an intermediate dataset similar to the target one. Such an approach has demonstrated significant performance improvements, making pretrained networks more suitable for medical imaging. A noise reduction algorithm for confocal laser scanning microscopy (CLSM) images was developed [18].