Abstract
Background and Aim: Passive hip laxity (PHL) is considered the primary risk factor for canine hip dysplasia (HD) and is estimated, in stress hip radiographs, using the distraction index (DI). The study aimed to associate the early PHL using the hip Distractor of University of Tras-os-Montes and Alto Douro (DisUTAD) and the late HD grades. Materials and Methods: A total of 41 dogs (82 hips) were submitted to a follow-up study. First, between 4 and 12 months of age, dogs were radiographed using the DisUTAD hip distractor and were determined the DI for each hip joint. Then, after 12 months of age, dogs were reevaluated for HD using the conventional hip ventrodorsal projection and hips were evaluated for HD using the Federation Cynologique Internationale (FCI) scoring system. Results: Hips of dogs' in the second examination with FCI grades of A (n=28), B (n=11), C (n=22), and D and E (n=21) had an early DI of 0.32 +/- 0.1, 0.380.08, 0.50 +/- 0.12, and 0.64 +/- 0.11, respectively. Statistical analysis using the general linear model univariate, with the DI as dependent variable and the FCI grades, side and sex as fixed factors, and the post hoc Bonferroni correction test showed significant differences among FCI grades (p<0.05). Conclusion: These results show the association between early DI and the late FCI HD grades and the DisUTAD is recommended for the early canine HD diagnosis.

Abstract
Melanoma is considered the deadliest type of skin cancer and in the last decade, the incidence rate has increased substantially. However, automatic melanoma classification has been widely used to aid the detection of lesions as well as prevent eventual death. Therefore, in this paper we decided to investigate how an attention mechanism combined with a classical backbone network would affect the classification of melanomas. This mechanism is known as triplet attention, a lightweight method that allows to capture cross-domain interactions. This characteristic helps to acquire rich discriminative feature representations. The different experiments demonstrate the effectiveness of the model in five different datasets. The model was evaluated based on sensitivity, specificity, accuracy, and F1-Score. Even though it is a simple method, this attention mechanism shows that its application could be beneficial in classification tasks.

Abstract
X-ray bone semantic segmentation is one crucial task in medical imaging. Due to deep learning's emergence, it was possible to build high-precision models. However, these models require a large quantity of annotated data. Furthermore, semantic segmentation requires pixel-wise labeling, thus being a highly time-consuming task. In the case of hip joints, there is still a need for increased anatomic knowledge due to the intrinsic nature of the femur and acetabulum. Active learning aims to maximize the model's performance with the least possible amount of data. In this work, we propose and compare the use of different queries, including uncertainty and diversity-based queries. Our results show that the proposed methods permit state-of-the-art performance using only 81.02% of the data, with O(1) time complexity.

Abstract
Artificial intelligence is emerging in the field of veterinary medical imaging. The development of this area in medicine has introduced new concepts and scientific terminologies that professionals must be able to have some understanding of, such as the following: machine learning, deep learning, convolutional neural networks, and transfer learning. This paper offers veterinary professionals an overview of artificial intelligence, machine learning, and deep learning focused on imaging diagnosis. A review is provided of the existing literature on artificial intelligence in veterinary imaging of small animals, together with a brief conclusion.Artificial intelligence and machine learning have been increasingly used in the medical imaging field in the past few years. The evaluation of medical images is very subjective and complex, and therefore the application of artificial intelligence and deep learning methods to automatize the analysis process would be very beneficial. A lot of researchers have been applying these methods to image analysis diagnosis, developing software capable of assisting veterinary doctors or radiologists in their daily practice. This article details the main methodologies used to develop software applications on machine learning and how veterinarians with an interest in this field can benefit from such methodologies. The main goal of this study is to offer veterinary professionals a simple guide to enable them to understand the basics of artificial intelligence and machine learning and the concepts such as deep learning, convolutional neural networks, transfer learning, and the performance evaluation method. The language is adapted for medical technicians, and the work already published in this field is reviewed for application in the imaging diagnosis of different animal body systems: musculoskeletal, thoracic, nervous, and abdominal.

Abstract
This paper proposes a new robust approach to nonlinear clustering based on the Principal Component Analysis (PCA) approach. A robust c-means partition is derived by using the natural PCA noise-rejection mechanism and the nonlinearity captured by a sliding process of the clusters prototype. A non-linear extension of PCA has been developed for detecting the lower-dimensional representation of real world data sets. For these cases local linear approaches are used widely because of their computational simplicity and understandability. We will present a new method that joins (merges) the fuzzy clustering algorithm with a local sliding PCA analysis. With this strategy it is possible to identify the non-linear relations and obtain morphological information of the data. The Sliding PCA-Fuzzy cluster algorithm (SPCA-FCA) is a fuzzy clustering algorithm that estimates local principal component vectors as the vectors spanning prototypes of clusters, performed on the neighborhood of the center of cluster and normal approximations in order to estimate a tangent surface that characterizes the trend and curvature of the data points or contours region. Numerical experiments demonstrate that the proposed method is useful for capturing cluster cores by rejecting noise samples, and we can easily assess cluster validity by using cluster-crossing curves.

Abstract