DeSAM: Decoupling Segment Anything Model for Generalizable Medical Image Segmentation

Deep learning based automatic medical image segmentation models often suffer from domain shift, where the models trained on a source domain do not generalize well to other unseen domains. As a vision foundation model with powerful generalization capabilities, Segment Anything Model (SAM) shows potential for improving the cross-domain robustness of medical image segmentation. However, SAM and its fine-tuned models performed significantly worse in fully automatic mode compared to when given manual prompts. Upon further investigation, we discovered that the degradation in performance was related to the coupling effect of poor prompts and mask segmentation. In fully automatic mode, the presence of inevitable poor prompts (such as points outside the mask or boxes significantly larger than the mask) can significantly mislead mask generation. To address the coupling effect, we propose the decoupling SAM (DeSAM). DeSAM modifies SAM's mask decoder to decouple mask generation and prompt embeddings while leveraging pre-trained weights. We conducted experiments on publicly available prostate cross-site datasets. The results show that DeSAM improves dice score by an average of 8.96% (from 70.06% to 79.02%) compared to previous state-of-the-art domain generalization method. Moreover, DeSAM can be trained on personal devices with entry-level GPU since our approach does not rely on tuning the heavyweight image encoder. The code is publicly available at https://github.com/yifangao112/DeSAM.Comment: 12 pages. The code is available at https://github.com/yifangao112/DeSA

NIL Is not nothing: Recognition of Chinese network informal language expressions

Informal language is actively used in network-mediated communication, e.g. chat room, BBS, email and text message. We refer the anomalous terms used in such context as network informal language (NIL) expressions. For example, “�(ou3) ” is used to replace “�(wo3) ” in Chinese ICQ. Without unconventional resource, knowledge and techniques, the existing natural language processing approaches exhibit less effectiveness in dealing with NIL text. We propose to study NIL expressions with a NIL corpus and investigate techniques in processing NIL expressions. Two methods for Chinese NIL expressio

Multi-View Clustering via Semi-non-negative Tensor Factorization

Multi-view clustering (MVC) based on non-negative matrix factorization (NMF) and its variants have received a huge amount of attention in recent years due to their advantages in clustering interpretability. However, existing NMF-based multi-view clustering methods perform NMF on each view data respectively and ignore the impact of between-view. Thus, they can't well exploit the within-view spatial structure and between-view complementary information. To resolve this issue, we present semi-non-negative tensor factorization (Semi-NTF) and develop a novel multi-view clustering based on Semi-NTF with one-side orthogonal constraint. Our model directly performs Semi-NTF on the 3rd-order tensor which is composed of anchor graphs of views. Thus, our model directly considers the between-view relationship. Moreover, we use the tensor Schatten p-norm regularization as a rank approximation of the 3rd-order tensor which characterizes the cluster structure of multi-view data and exploits the between-view complementary information. In addition, we provide an optimization algorithm for the proposed method and prove mathematically that the algorithm always converges to the stationary KKT point. Extensive experiments on various benchmark datasets indicate that our proposed method is able to achieve satisfactory clustering performance

Microscopic Investigation of a Copper Molten Mark by Optical Microscopy (OM) and Atomic Force Microscopy (AFM)

AbstractA wide variety of physical and chemical detecting methods have been proposed for discriminating between and electric arc bead that caused a fire, versus one that was caused by the fire itself. The simplest proposed method claims that examination of the molten marks in a bead under a microscope will suffice to make the distinction. Generally, copper molten marks of the bead are examined by using optical (OM) and scanning electron microscopy (SEM). In this paper, OM and AFM were employed to investigate a molten mark formed in laboratory. AFM observation reveals that AFM could be an auxiliary method to investigate the copper molten mark formed in the fire in order to confirm the reasons of the fire

Evaluation of the differences of myocardial fibers between acute and chronic myocardial infarction: Application of diffusion tensor magnetic resonance imaging in a rhesus monkey model

Objective: To understand microstructural changes after myocardial infarction (MI), we evaluated myocardial fibers of rhesus monkeys during acute or chronic MI, and identified the differences of myocardial fibers between acute and chronic MI. Materials and Methods: Six fixed hearts of rhesus monkeys with left anterior descending coronary artery ligation for 1 hour or 84 days were scanned by diffusion tensor magnetic resonance imaging (MRI) to measure apparent diffusion coefficient (ADC), fractional anisotropy (FA) and helix angle (HA). Results: Comparing with acute MI monkeys (FA: 0.59 +/- 0.02; ADC: 5.0 +/- 0.6 x 10(-4) mm(2)/s; HA: 94.5 +/- 4.4 degrees), chronic MI monkeys showed remarkably decreased FA value (0.26 +/- 0.03), increased ADC value (7.8 +/- 0.8 x 10(-4)mm(2)/s), decreased HA transmural range (49.5 +/- 4.6 degrees) and serious defects on endocardium in infarcted regions. The HA in infarcted regions shifted to more components of negative left-handed helix in chronic MI monkeys (-38.3 +/- 5.0 degrees-11.2 +/- 4.3 degrees) than in acute MI monkeys (-41.4 +/- 5.1 degrees-53.1 +/- 3.7 degrees), but the HA in remote regions shifted to more components of positive right-handed helix in chronic MI monkeys (-43.8 +/- 2.7 degrees-66.5 +/- 4.9 degrees) than in acute MI monkeys (-59.5 +/- 3.4 degrees-64.9 +/- 4.3 degrees). Conclusion: Diffusion tensor MRI method helps to quantify differences of mechanical microstructure and water diffusion of myocardial fibers between acute and chronic MI monkey's models.National Natural Science Foundation of China [81130027, 81301196]SCI(E)[email protected]

Bis[(1-methyl-1H-tetra­zol-5-yl)sulfan­yl]methane

The mol­ecule of the title compound, C5H8N8S2, lies on a twofold rotation axis that relates on 1-methyl­tetra­zolyl group to the other; the five-membered rings are twisted by 53.1 (1)°