BACKGROUND: Multidrug resistance in gastric cancer greatly impedes the efficacy of chemotherapy. OBJECTIVE: To explore the efficacy of microRNA-21 (mir-21) in distinguishing metastatic gastric cancer (MGC) with chemoresistance. METHODS: From April 20

Deep hashing algorithms have demonstrated considerable success in recent years, particularly in cross-modal retrieval tasks. Although hash-based cross-modal retrieval methods have demonstrated considerable efficacy, the vulnerability of deep networks to adversarial examples represents a significant challenge for the hash retrieval. In the absence of target semantics, previous non-targeted attack methods attempt to attack depth models by adding disturbance to the input data, yielding some positive outcomes. Nevertheless, they still lack specific instancelevel hash codes and fail to consider the diversity and semantic association of different modalities, which is insufficient to meet the attacker's expectations. In response, we present a novel Primary code Guided Targeted Attack (PGTA) against crossmodal hashing retrieval. Specifically, we integrate cross-modal instances and labels to obtain well-fused target semantics, thereby enhancing cross-modal interaction. Secondly, the primary code is designed to generate discriminable information with fine-grained semantics for target labels. Benign samples and target semantics collectively generate adversarial examples under the guidance of primary codes, thereby enhancing the efficacy of targeted attacks. Extensive experiments demonstrate that our PGTA outperforms the most advanced methods on three datasets, achieving stateof-the-art targeted attack performance.

It can be a very challenging task when using level set method segmenting natural images with high intensity inhomogeneity and complex background scenes. A new synthesis level set method for robust image segmentation based on the combination of Retinex‐corrected saliency region information and edge information is proposed in this work. First, the Retinex theory is introduced to correct the saliency information extraction. Second, the Retinex‐corrected saliency information is embedded into the level set method due to its advantageous quality which makes a foreground object stand out relative to the backgrounds. Combined with the edge information, the boundary of segmentation will be more precise and smooth. Experiments indicate that the proposed segmentation algorithm is efficient, fast, reliable, and robust.