Bone age assessment (BAA) based on hand X-ray imaging is a common clinical practice for investigating disorders and predicting the adult height of a child. However, the traditional manual method is time consuming and prone to obverse variability. There is an urgent need for a fully automatic framework based on deep learning with high performance and efficiency. We propose an end-to-end BAA model based on lossless image compression and a squeeze-and-excitation deep residual network (SE-ResNet). First, we apply the compression module to compress the raw image without losing important features. Second, the SE-ResNet-based model extracts features of the compressed images. Furthermore, the regression model with improved loss function predicts bone age. The experiments on a public dataset reveal that our method outperforms the baseline models. In conclusion, the presented method is a fully automatic and effective solution to process hand X-ray images for BAAs.

The object detection, which has been widely applied in the biomedical field already, is of real significance but technically challenging. In practice, the object detection accuracy is vulnerable to labeling quality, which is usually not a big headache for simple algorithm or model verification since there are a bunch of ideal public available datasets whose classes and tags are all well-marked. However, in real scenarios, image data is often partially or even incorrectly labelled. Particularly, in cell detection, this becomes a thorny issue since the labelling of the dataset is incomplete and inac- curate. To address this issue, we propose a data-augmentation algorithm that can generate full labelled cell image data from incomplete labeled ones. First of all, we randomly extract the labeled objects from raw cell images, and mean- while, keep their corresponding position information. Next, we employ the framework of cycle-consistent adversarial network, but significantly distinguished from the original one, to generate fully labeled data including both objects and backgrounds. We conduct extensive experiments on a blood cell classification dataset called BCCD to evaluate our model, and experimental results show that our proposed method can successfully address the weak annotation problem and improve the performance of object detection.

Currently, discourse relation recognition (DRR), which is not directly marked with connectives, is a challenging task. Traditional approaches for implicit DRR in Chinese have focused on exploring the concepts and features of words; however, these approaches have only yielded slow progress. Moreover, the lack of Chinese labeled data makes it more difficult to complete this task with high accuracy. To address this issue, we propose a novel hybrid DRR model combining a pretrained language model, namely bidirectional encoder representations from transformers (BERT), with recurrent neural networks. We use BERT as a text representation and pretraining model. In addition, we apply a tree structure to the implicit DRR in Chinese to produce hierarchical classes. The 19-class F1 score of our proposed method can reach 74.47% on the HIT-CIR Chinese discourse relation corpus. The attained results showed that the use of BERT and the proposed tree structure forms a novel and precise method that can automatically recognize the implicit relations of Chinese discourse.

Semantic classification of Chinese long discourses is an important and challenging task. Discourse text is high‐dimensional and sparse. Furthermore, when the number of classes of dataset is large, the data distribution will be seriously imbalanced. In solving these problems, we propose a novel end‐to‐end model called CRAFL, which is based on the convolutional layer with attention mechanism, recurrent neural networks, and improved focal loss function. First, the residual network (ResNet) extracts phrase semantic representations from word embedding vectors and reduces the dimensionality of the input matrix. Then, the attention mechanism differentiates the focus on the output of ResNet, and the long short‐term memory layer learns the features of the sequences. Lastly but most significantly, we apply an improved focal loss function to mitigate the problem of data class imbalance. Our model is compared with other state‐of‐the‐art models on the long discourse dataset, and CRAFL model has proven be more efficient for this task.