YOLO-MED : Multi-Task Interaction Network for Biomedical Images

Object detection and semantic segmentation are pivotal components in biomedical image analysis. Current single-task networks exhibit promising outcomes in both detection and segmentation tasks. Multi-task networks have gained prominence due to their capability to simultaneously tackle segmentation and detection tasks, while also accelerating the segmentation inference. Nevertheless, recent multi-task networks confront distinct limitations such as the difficulty in striking a balance between accuracy and inference speed. Additionally, they often overlook the integration of cross-scale features, which is especially important for biomedical image analysis. In this study, we propose an efficient end-to-end multi-task network capable of concurrently performing object detection and semantic segmentation called YOLO-Med. Our model employs a backbone and a neck for multi-scale feature extraction, complemented by the inclusion of two task-specific decoders. A cross-scale task-interaction module is employed in order to facilitate information fusion between various tasks. Our model exhibits promising results in balancing accuracy and speed when evaluated on the Kvasir-seg dataset and a private biomedical image dataset.Comment: Accepted by ICASSP 202

Diagnosis and segmentation effect of the ME-NBI-based deep learning model on gastric neoplasms in patients with suspected superficial lesions - a multicenter study

BackgroundEndoscopically visible gastric neoplastic lesions (GNLs), including early gastric cancer and intraepithelial neoplasia, should be accurately diagnosed and promptly treated. However, a high rate of missed diagnosis of GNLs contributes to the potential risk of the progression of gastric cancer. The aim of this study was to develop a deep learning-based computer-aided diagnosis (CAD) system for the diagnosis and segmentation of GNLs under magnifying endoscopy with narrow-band imaging (ME-NBI) in patients with suspected superficial lesions.MethodsME-NBI images of patients with GNLs in two centers were retrospectively analysed. Two convolutional neural network (CNN) modules were developed and trained on these images. CNN1 was trained to diagnose GNLs, and CNN2 was trained for segmentation. An additional internal test set and an external test set from another center were used to evaluate the diagnosis and segmentation performance.ResultsCNN1 showed a diagnostic performance with an accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 90.8%, 92.5%, 89.0%, 89.4% and 92.2%, respectively, and an area under the curve (AUC) of 0.928 in the internal test set. With CNN1 assistance, all endoscopists had a higher accuracy than for an independent diagnosis. The average intersection over union (IOU) between CNN2 and the ground truth was 0.5837, with a precision, recall and the Dice coefficient of 0.776, 0.983 and 0.867, respectively.ConclusionsThis CAD system can be used as an auxiliary tool to diagnose and segment GNLs, assisting endoscopists in more accurately diagnosing GNLs and delineating their extent to improve the positive rate of lesion biopsy and ensure the integrity of endoscopic resection

馬一浮六藝論析論

Ma Yi-fu, a great 20th-century master of Confucian studies, established the “Theory of the Six Arts” (“Liu Yi Lun”), a paradigm of Modern Confucianism. Yet Liu Yi Lun, up till now, has not been fully studied. This dissertation focuses mainly on the detailed content of Liu Yi Lun. With interpretation in the dissertation, Liu Yi Lun is viewed as a matrix of the emergence of meaning. In Chapter One, the outline of Liu Yi Lun is explained, then I conclude that the Six Arts, in Ma Yi-fu’s Liu Yi Lun, are constituted by four constituent characteristics: the Six Arts as ontological substances as a whole (quan-ti), the Six Arts as great functions (da-yong), the Six Arts as self-cultivations (gong-fu), and the Six Arts as teaching taxonomies (pan-jiao). The previous three characteristics, quan-ti, da-yong and gong-fu, being inclusive and interactive with each other, constitute the general structure of Six Arts as a matrix of meaning. Chapter Two, “The Original Foundation of the Lun Yi Lun”, reveals that the “three-changes” (“san-yi”), constituted naturally by the unchanged (bu-yi), the changes (bian-yi), and the simple changes (jian-yi), make possible the interdependent-formation of the previous three characteristics of the Six Arts. Chapter Three, “On Ma Yi-fu’s View of the Relations of Confucianism and Buddhism” and Chapter Four, “On Ma Yi-fu’s View of Western Philosophy”, are extended topics of the fourth characteristic of the Six Arts, i.e. “the Six Arts as teaching taxonomies”. From these two chapters, we can further comprehend the meaning of Liu Yi Lun and Ma Yi-fu’s philosophical concerns

Zhong Xi wenhua de duihua yu rongtong—fang Bilishi Luwen daxue Dai Kalin jiaoshou

status: publishe

Intestinal Stem Cells Damaged by Deoxycholic Acid via AHR Pathway Contributes to Mucosal Barrier Dysfunction in High-Fat Feeding Mice

High-fat exposure leads to impaired intestinal barrier function by disrupting the function of intestinal stem cells (ISCs); however, the exact mechanism of this phenomenon is still not known. We hypothesize that high concentrations of deoxycholic acid (DCA) in response to a high-fat diet (HFD) affect aryl hydrocarbon receptor (AHR) signalling in ISCs and the intestinal barrier. For this purpose, C57BL/6J mice feeding on a low-fat diet (LFD), an HFD, an HFD with the bile acid binder cholestyramine, and a LFD with the DCA were studied. We found that high-fat feeding induced an increase in faecal DCA concentrations. An HFD or DCA diet disrupted the differentiation function of ISCs by downregulating AHR signalling, which resulted in decreased goblet cells (GCs) and MUC2, and these changes were reversed by cholestyramine. In vitro experiments showed that DCA downregulated the differentiation function of ISCs, which was reversed by the AHR agonist 6-formylindolo [3,2-b]carbazole (FICZ). Mechanistically, DCA caused a reduction in indoleamine 2,3-dioxygenase 1 (IDO1) in Paneth cells, resulting in paracrine deficiency of the AHR ligand kynurenine in crypts. We demonstrated for the first time that DCA disrupts intestinal mucosal barrier function by interfering with AHR signalling in ISCs. Supplementation with AHR ligands may be a new therapeutic target for HFD-related impaired intestinal barrier function