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Jaw cyst is a fluid-containing cystic lesion that can occur in any part of the jaw and cause facial swelling, dental lesions, jaw fractures, and other associated issues. Due to the diversity and complexity of jaw images, existing deep-learning methods still have challenges in segmentation. To this end, we propose MARes-Net, an innovative multi-scale attentional residual network architecture. Firstly, the residual connection is used to optimize the encoder-decoder process, which effectively solves the gradient disappearance problem and improves the training efficiency and optimization ability. Secondly, the scale-aware feature extraction module (SFEM) significantly enhances the network’s perceptual abilities by extending its receptive field across various scales, spaces, and channel dimensions. Thirdly, the multi-scale compression excitation module (MCEM) compresses and excites the feature map, and combines it with contextual information to obtain better model performance capabilities. Furthermore, the introduction of the attention gate module marks a significant advancement in refining the feature map output. Finally, rigorous experimentation conducted on the original jaw cyst dataset provided by Quzhou People’s Hospital to verify the validity of MARes-Net architecture. The experimental data showed that precision, recall, IoU and F1-score of MARes-Net reached 93.84%, 93.70%, 86.17%, and 93.21%, respectively. Compared with existing models, our MARes-Net shows its unparalleled capabilities in accurately delineating and localizing anatomical structures in the jaw cyst image segmentation.

Clinicians typically use semantic features to judge the malignant status of nodules, while artificial intelligence systems (AI) tend to extract unknown features to diagnose nodules. The former relies on clinical knowledge, while the latter explores AI knowledge. Although many studies indicate that fusing clinical and AI knowledge can help computer-aided diagnosis (CAD) systems improve diagnostic accuracy and gain clinician approval, how to effectively fuse them is still an open question. This paper proposes a simple and effective pipeline (abbreviated as CKAK), which fuses clinical and AI knowledge at both feature and decision levels for accurate lung nodule malignancy classification and semantic attributes characterization. The feature-level fusion can retain rich information in high-dimensional features and improve the model's accuracy; the decision-level fusion can provide some interpretability for the model's decision-making process, which is expected in clinical applications. Specifically, the proposed CKAK consists of two sequential stages: (i) the initial prediction stage (IPS); and (i i) the prediction refine stage (PRS). The IPS predicts eight radiologist-interpreted semantic attributes and an initial malignancy diagnosis in parallel. Then, these results are fed to the subsequent PRS to refine the diagnosis further by fully fusing them at feature and decision levels. Besides, to enhance the ability of feature learning, we propose a novel scale-aware feature extraction block (SAFE). It integrates multi-scale contextual features with a lightweight Transformer rather than adding or concatenating them roughly. Extensive experiments at the LIDC-IDRI data set show that the proposed CKAK can achieve superior benign-malignant classification accuracy with minor radiologist-interpreted semantic scores error, meeting the need for a reliable CAD system. • We fully fuse clinical and AI knowledge at the feature and decision-making levels. • We integrate multi-scale information to improve model's feature learning ability. • Experiments show our method has reliable performances in lung nodule diagnosis. [ABSTRACT FROM AUTHOR]