Re-operations and revisions are often performed in patients who have undergone total shoulder arthroplasty (TSA) and reverse total shoulder arthroplasty (RTSA). This necessitates an accurate recognition of the implant model and manufacturer to set the correct apparatus and procedure according to the patient’s anatomy as personalized medicine. Owing to unavailability and ambiguity in the medical data of a patient, expert surgeons identify the implants through a visual comparison of X-ray images. False steps cause heedlessness, morbidity, extra monetary weight, and a waste of time. Despite significant advancements in pattern recognition and deep learning in the medical field, extremely limited research has been conducted on classifying shoulder implants. To overcome these problems, we propose a robust deep learning-based framework comprised of an ensemble of convolutional neural networks (CNNs) to classify shoulder implants in X-ray images of different patients. Through our rotational invariant augmentation, the size of the training dataset is increased 36-fold. The modified ResNet and DenseNet are then combined deeply to form a dense residual ensemble-network (DRE-Net). To evaluate DRE-Net, experiments were executed on a 10-fold cross-validation on the openly available shoulder implant X-ray dataset. The experimental results showed that DRE-Net achieved an accuracy, F1-score, precision, and recall of 85.92%, 84.69%, 85.33%, and 84.11%, respectively, which were higher than those of the state-of-the-art methods. Moreover, we confirmed the generalization capability of our network by testing it in an open-world configuration, and the effectiveness of rotational invariant augmentation.

Accurate nuclear segmentation in histopathology images plays a key role in digital pathology. It is considered a prerequisite for the determination of cell phenotype, nuclear morphometrics, cell classification, and the grading and prognosis of cancer. However, it is a very challenging task because of the different types of nuclei, large intraclass variations, and diverse cell morphologies. Consequently, the manual inspection of such images under high-resolution microscopes is tedious and time-consuming. Alternatively, artificial intelligence (AI)-based automated techniques, which are fast and robust, and require less human effort, can be used. Recently, several AI-based nuclear segmentation techniques have been proposed. They have shown a significant performance improvement for this task, but there is room for further improvement. Thus, we propose an AI-based nuclear segmentation technique in which we adopt a new nuclear segmentation network empowered by residual skip connections to address this issue. Experiments were performed on two publicly available datasets: (1) The Cancer Genome Atlas (TCGA), and (2) Triple-Negative Breast Cancer (TNBC). The results show that our proposed technique achieves an aggregated Jaccard index (AJI) of 0.6794, Dice coefficient of 0.8084, and F1-measure of 0.8547 on TCGA dataset, and an AJI of 0.7332, Dice coefficient of 0.8441, precision of 0.8352, recall of 0.8306, and F1-measure of 0.8329 on the TNBC dataset. These values are higher than those of the state-of-the-art methods.

Because fish are vital to marine ecosystems, monitoring and accurate detection are crucial for assessing the potential for fisheries in these environments. Conventionally, fish-related assessment is conducted manually, which makes it labor-intensive and time-consuming. In addition, the assessments are challenging owing to underwater visibility limitations, which leads to poor detection accuracy. To overcome these problems, we propose two novel architectures for the automatic and high-performance segmentation of fish populations. In this study, the efficient fish segmentation network (EFS-Net) and multi-level feature accumulation-based segmentation network (MFAS-Net) are the base and final networks, respectively. In deep convolutional neural networks, the initial layers usually contain potential spatial information. Therefore, the EFS-Net employs a series of convolution layers in the early stage of the network for optimal feature extraction. To boost segmentation accuracy, the MFAS-Net uses an initial feature refinement and transfer block to refine potential low-level information and subsequently transfers it to the deep stages of the network. Moreover, the MFAS-Net employs multi-level feature accumulation that improves pixel-wise prediction for fish that are indistinct. The proposed networks are evaluated using two publicly available datasets, namely DeepFish and semantic segmentation of underwater imagery (SUIM), both of which contain challenging underwater fish segmentation images. The experimental results reveal that mean intersection-over-unions of 76.42% and 92.0% are attained by the proposed method for the DeepFish and SUIM datasets, respectively; these values are higher than those by the state-of-the-art methods such as A-LCFCN+PM and DPANet. In addition, high segmentation performance is achieved without compromising the computational efficiency of the networks. The MFAS-Net requires only 3.57 million trainable parameters to be fully trained. The proposed model and the complete code will be made available¹.