Knee osteoarthritis (OA) is one of the most common diseases worldwide, leading to activity limitations and physical disability in humans. Early detection and quantitative assessment of OA using the Kellgren-Lawrence (KL) grading system on plain radiographs can help slow disease progression. Currently, computer-aided methods based on deep learning can reliably assess OA severity, but they require a large amount of labeled data to train neural networks. In this work, we propose using transfer learning to achieve fully automatic detection and classification of knee joints in plain radiographs. In addition, we propose a semi-supervised learning algorithm (SSL) based on consistency regularization to leverage both unlabeled data and labeled data to improve classification performance. On public knee X-ray datasets, compared with state-of-the-art supervised learning (SL) methods, our method can achieve great effects while greatly reducing the cost of labeling data.

An emerging optimisation problem from real-world applications, named the multi-point dynamic aggregation (MPDA) problem, has become an active research of the multi-robot system. This paper focuses on a multi-objective MPDA (MO-MPDA) problem which is to design execution plans of robots for minimising the cost of used robots and maximising the efficiency of task execution. The MOMPDA problem has the issues of conflicting objectives, redundant representation, and variable-length encoding, posing extra challenges to address the MO-MPDA problem effectively. Combining the ∊-constraint method and decomposition mechanisms, a novel multi-objective evolutionary algorithm is proposed. The proposed algorithm selects the efficiency objective as the main objective and converts the cost objective as constraints. Thus, the multi-objective problem is decomposed into a series of scalar constrained optimisation subproblems by assigning each subproblem with an upper bound constraint. All the subproblems are optimised and evolved simultaneously with the transferring knowledge from other sub-problems to solve the MO-MPDA problem parallelly and efficiently. Besides, considering the characteristics of parent individuals, this paper designs a hybrid reproduction mechanism to transmit effective information to offspring individuals for tackling the encoding redundancy and varying-length. Experimental results show that the proposed algorithm significantly outperforms the state-of-the-art algorithms in terms of most-used metrics.

3D local binary pattern (LBP) shows significant performance in many domains such as solid textures analysis, face recognition and tumor detection. In recent years, rotation invariant 3D LBP texture descriptors have received increasing attention and several variants have been proposed. However, they are sensitive to the noise present in the image. In this paper, we propose an efficient rotation invariant texture descriptor known as robust extended 3D LBP (RELBP) for volumetric texture classification. Unlike the current 3D LBP framework, our descriptor uses the information of neighboring voxels to reduce noise. First, the 3D weighted average filter is employed to process each voxel in the image, in which the center voxel is replaced by the average local gray level based on weights. Besides, equidistant points on a sphere are sampled to construct a set of rotation invariant features. Our experiments demonstrate that the RELBP proposed here shows superior classification performance in texture classification tasks and our method is highly robust to image noise on benchmark datasets.