Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering

Existing deep learning models have achieved promising performance in recognizing skin diseases from dermoscopic images. However, these models can only recognize samples from predefined categories, when they are deployed in the clinic, data from new unknown categories are constantly emerging. Therefore, it is crucial to automatically discover and identify new semantic categories from new data. In this paper, we propose a new novel class discovery framework for automatically discovering new semantic classes from dermoscopy image datasets based on the knowledge of known classes. Specifically, we first use contrastive learning to learn a robust and unbiased feature representation based on all data from known and unknown categories. We then propose an uncertainty-aware multi-view cross pseudo-supervision strategy, which is trained jointly on all categories of data using pseudo labels generated by a self-labeling strategy. Finally, we further refine the pseudo label by aggregating neighborhood information through local sample similarity to improve the clustering performance of the model for unknown categories. We conducted extensive experiments on the dermatology dataset ISIC 2019, and the experimental results show that our approach can effectively leverage knowledge from known categories to discover new semantic categories. We also further validated the effectiveness of the different modules through extensive ablation experiments. Our code will be released soon.Comment: 10 pages, 1 figure,Accepted by miccai 202

Enabling heterogeneous network function chaining

Today's data center operators deploy network policies in both physical (e.g., middleboxes, switches) and virtualized (e.g., virtual machines on general purpose servers) network function boxes (NFBs), which reside in different points of the network, to exploit their efficiency and agility respectively. Nevertheless, such heterogeneity has resulted in a great number of independent network nodes that can dynamically generate and implement inconsistent and conflicting network policies, making correct policy implementation a difficult problem to solve. Since these nodes have varying capabilities, services running atop are also faced with profound performance unpredictability. In this paper, we propose a Heterogeneous netwOrk Policy Enforcement (HOPE) scheme to overcome these challenges. HOPE guarantees that network functions (NFs) that implement a policy chain are optimally placed onto heterogeneous NFBs such that the network cost of the policy is minimized. We first experimentally demonstrate that the processing capacity of NFBs is the dominant performance factor. This observation is then used to formulate the Heterogeneous Network Policy Placement problem, which is shown to be NP-Hard. To solve the problem efficiently, an online algorithm is proposed. Our experimental results demonstrate that HOPE achieves the same optimality as Branch-and-bound optimization but is 3 orders of magnitude more efficient

3D Matting: A Soft Segmentation Method Applied in Computed Tomography

Three-dimensional (3D) images, such as CT, MRI, and PET, are common in medical imaging applications and important in clinical diagnosis. Semantic ambiguity is a typical feature of many medical image labels. It can be caused by many factors, such as the imaging properties, pathological anatomy, and the weak representation of the binary masks, which brings challenges to accurate 3D segmentation. In 2D medical images, using soft masks instead of binary masks generated by image matting to characterize lesions can provide rich semantic information, describe the structural characteristics of lesions more comprehensively, and thus benefit the subsequent diagnoses and analyses. In this work, we introduce image matting into the 3D scenes to describe the lesions in 3D medical images. The study of image matting in 3D modality is limited, and there is no high-quality annotated dataset related to 3D matting, therefore slowing down the development of data-driven deep-learning-based methods. To address this issue, we constructed the first 3D medical matting dataset and convincingly verified the validity of the dataset through quality control and downstream experiments in lung nodules classification. We then adapt the four selected state-of-the-art 2D image matting algorithms to 3D scenes and further customize the methods for CT images. Also, we propose the first end-to-end deep 3D matting network and implement a solid 3D medical image matting benchmark, which will be released to encourage further research.Comment: 12 pages, 7 figure

Cloning and Expression of irf7 Gene in Spotted Knifejaw (Oplegnathus punctatus) Under Virus Infection

Interferon regulatory factor (irf7) is an immune regulatory factor that plays an important role in the antiviral process. To explore the role of irf7 in Oplegnathus punctatus (Temminck & Schlegel, 1844) under viral infection, we cloned the coding DNA sequence (CDS) region of irf7 through PCR and analyzed the expression patterns at both tissue and cell levels. The results showed that the CDS region of Opirf7 was 1 332 bp and encoded a peptide with 443 amino acids. The predicted molecular weight was 50.5 kDa and the theoretical isoelectric point was 5.546. Protein structure analysis showed that Opirf7 has three conserved domains: the DNA binding domain (DBD), IRF-associated domain (IAD), and serine-rich domain (SRD). Amino acid similarity analysis showed that OpIRF7 had the highest similarity to the IRF7 of Lates calcarifer, which was 82.92%. The similarity of Opirf7 with the IRF7 of Larimichthys crocea, Paralichthys olivaceus, and Cynoglossus semilaevis were 81.99%, 79.95%, and 73.74%, respectively. Phylogenetic analysis showed that Opirf7 and other fish irf7 genes were clustered into one branch, and irf7s from Gallus gallus, Mus musculus, Macaca mulatta, and Homo sapiens were clustered into another. Tissues from healthy O. punctatus were collected, including the liver, spleen, kidney, head kidney, intestine, gill, skin, muscle, brain, heart, and blood. After RNA extraction and cDNA synthesis, real-time PCR (qPCR) was performed to detect the expression level of Opirf7 using the comparative CT method (2−ΔΔCT method). The results of qPCR showed that Opirf7 was expressed in different tissues of healthy individuals and its expression was highest in the liver, followed by the skin and intestines. The lowest expression was observed in the head kidney. In this study, the expression profiles of Opirf7 before and after viral infection were determined at the tissue and cell levels. For the in vivo challenge study, fish were intraperitoneally injected with spotted knifejaw iridovirus, and the expression level of Opirf7 was tested in the spleen, kidney, and liver. Compared with the control group at 0 h, the expression level of Opirf7 was 15-fold in the spleen and 3-fold in the kidney 4 days after infection, and the expression peak was at 7 days after infection. However, the expression of Opirf7 was not significantly altered in the liver. A poly I: C-infected O. punctatus brain cell model was established, and the expression profiles of Opirf7 mRNA were detected before and after infection. The expression of Opirf7 mRNA in the low and medium concentration groups (50 μg/mL and 100 μg/mL, respectively) increased by 13 to 17 times, and the expression level of Opirf7 mRNA in the high concentration group (200 μg/mL) increased by approximately 8 times. It was speculated that the high concentration of 200 μg/mL caused some damage to the cells and that the expression level in the high concentration group was lower than that in the low and medium groups. In this study, the full-length open reading frame sequence of Opirf7 was cloned and characterized for the first time. The deduced amino acid sequence displayed a structure similar to those of other vertebrates. Further functional analysis showed that Opirf7 has a significant response to viral infection at both tissue and cell levels. This study demonstrated that the Opirf7 gene might play an important role in the antiviral response of O. punctatus and provide a potential molecular marker for antivirus breeding of O. punctatus

Built-Up Area Change Detection Using Multi-Task Network with Object-Level Refinement

The detection and monitoring of changes in urban buildings, as a major place for human activities, have been considered profoundly in the field of remote sensing. In recent years, comparing with other traditional methods, the deep learning-based methods have become the mainstream methods for urban building change detection due to their strong learning ability and robustness. Unfortunately, often, it is difficult and costly to obtain sufficient samples for the change detection method development. As a result, the application of the deep learning-based building change detection methods is limited in practice. In our work, we proposed a novel multi-task network based on the idea of transfer learning, which is less dependent on change detection samples by appropriately selecting high-dimensional features for sharing and a unique decoding module. Different from other multi-task change detection networks, with the help of a high-accuracy building mask, our network can fully utilize the prior information from building detection branches and further improve the change detection result through the proposed object-level refinement algorithm. To evaluate the proposed method, experiments on the publicly available WHU Building Change Dataset were conducted. The experimental results show that the proposed method achieves F1 values of 0.8939, 0.9037, and 0.9212, respectively, when 10%, 25%, and 50% of change detection training samples are used for network training under the same conditions, thus, outperforming other methods

Unsupervised domain adaptation for medical image segmentation by selective entropy constraints and adaptive semantic alignment

Generalizing a deep learning model to new domains is crucial for computer-aided medical diagnosis systems. Most existing unsupervised domain adaptation methods have made significant progress in reducing the domain distribution gap through adversarial training. However, these methods may still produce overconfident but erroneous results on unseen target images. This paper proposes a new unsupervised domain adaptation framework for cross-modality medical image segmentation. Specifically, We first introduce two data augmentation approaches to generate two sets of semantics-preserving augmented images. Based on the model’s predictive consistency on these two sets of augmented images, we identify reliable and unreliable pixels. We then perform a selective entropy constraints: we minimize the entropy of reliable pixels to increase their confidence while maximizing the entropy of unreliable pixels to reduce their confidence. Based on the identified reliable and unreliable pixels, we further propose an adaptive semantic alignment module which performs class-level distribution adaptation by minimizing the distance between same class prototypes between domains, where unreliable pixels are removed to derive more accurate prototypes. We have conducted extensive experiments on the cross-modality cardiac structure segmentation task. The experimental results show that the proposed method significantly outperforms the state-of-the-art comparison algorithms. Our code and data are available at https://github.com/fengweie/SE ASA.</p

PCNet: Cloud Detection in FY-3D True-Color Imagery Using Multi-Scale Pyramid Contextual Information

Cloud, one of the poor atmospheric conditions, significantly reduces the usability of optical remote-sensing data and hampers follow-up applications. Thus, the identification of cloud remains a priority for various remote-sensing activities, such as product retrieval, land-use/cover classification, object detection, and especially for change detection. However, the complexity of clouds themselves make it difficult to detect thin clouds and small isolated clouds. To accurately detect clouds in satellite imagery, we propose a novel neural network named the Pyramid Contextual Network (PCNet). Considering the limited applicability of a regular convolution kernel, we employed a Dilated Residual Block (DRB) to extend the receptive field of the network, which contains a dilated convolution and residual connection. To improve the detection ability for thin clouds, the proposed new model, pyramid contextual block (PCB), was used to generate global information at different scales. FengYun-3D MERSI-II remote-sensing images covering China with 14,165 × 24,659 pixels, acquired on 17 July 2019, are processed to conduct cloud-detection experiments. Experimental results show that the overall precision rates of the trained network reach 97.1% and the overall recall rates reach 93.2%, which performs better both in quantity and quality than U-Net, UNet++, UNet3+, PSPNet and DeepLabV3+

Study on the Synthesis and Properties of Waterborne Polyurea Modified by Epoxy Resin

The most notable features of polyurea are its fast reaction, energy-saving and high efficiency. In order to meet the needs of environmental protection, waterborne polyurea (WPUA) has become a research hotspot. However, the presence of hydrophilic groups in WPUA reduces its solvent resistance, heat resistance and mechanical properties. Therefore, it is necessary and valuable to develop a high-performance WPUA. In this study, epoxy-modified waterborne polyurea (WPUAE) emulsions were prepared using epoxy resin as a modifier. Fourier transform infrared spectroscopy (FT-IR) showed that E44 was successfully introduced into the molecular chain of WPUA. The WPUAE was tested for gel fraction, adhesion, contact angle, solvent resistance, tensile properties and thermal stability. The results showed that when the E44 content was 8 wt%, the performance of WPUAE was best, the adhesion of WPUAE coating film was 1.53 MPa, the gel fraction, water contact angle, water absorption, toluene absorption, tensile strength and decomposition temperature were 96.94%, 70.3°, 16.43%, 131.04%, 9.05 MPa and 365 °C, respectively. The results showed that epoxy resin as an emulsion modifier improved the comprehensive properties of WPUA

Diagnosis of a Rabbit Hemorrhagic Disease Virus 2 (RHDV2) and the Humoral Immune Protection Effect of VP60 Vaccine

Rabbit hemorrhagic disease (RHD) is known as rabbit plague and hemorrhagic pneumonia. It is an acute, septic, and highly fatal infectious disease caused by the Lagovirus rabbit hemorrhagic disease virus (RHDV) in the family Caliciviridae that infects wild and domestic rabbits and hares (lagomorphs). At present, RHDV2 has caused huge economic losses to the commercial rabbit trade and led to a decline in the number of wild lagomorphs worldwide. We performed a necropsy and pathological observations on five dead rabbits on a rabbit farm in Tai’an, China. The results were highly similar to the clinical and pathological changes of typical RHD. RHDV2 strain was isolated and identified by RT-PCR, and partial gene sequencing and genetic evolution analysis were carried out. There were significant differences in genetic characteristics and antigenicity between RHDV2 and classical RHDV strain, and the vaccine prepared with the RHDV strain cannot effectively prevent rabbit infection with RHDV2. Therefore, we evaluated the protective efficacy of a novel rabbit hemorrhagic virus baculovirus vector inactivated vaccine (VP60) in clinical application by animal regression experiment. The result showed that VP60 could effectively induce humoral immunity in rabbits. The vaccine itself had no significant effect on the health status of rabbits. This study suggested that the clinical application of VP60 may provide new ideas for preventing the spread of RHD2

Regulation of Parkin in Cr (VI)-induced mitophagy in chicken hepatocytes

The large amount of heavy metal chromium emissions from industrial production, ore smelting and sewage treatment plants have made chromium one of the most widespread heavy metal pollutants, with Cr (VI) being the most toxic. In recent years, people have gradually recognized the great harm of heavy metal chromium pollution, but the research on its pathogenic mechanism is still not deep enough. In this study, we treated the Primary cells of chicken liver with Cr (VI) to establish a model of toxicity. The optimal treatment time and Cr (VI) concentration were screened using the CCK-8 test. The intracellular mitochondrial membrane potential (MMP) and reactive oxygen species (ROS) were measured qualitatively and quantitatively by laser confocal and flow cytometry, respectively. This result was confirmed by the fact that Cr (VI) could cause mitophagy by causing damage to mitochondria. Subsequently, this study used LMH cells to construct a Parkin silencing model to further investigate that Parkin exerts the function on the Cr (VI)-induced mitophagy in chicken hepatocytes. The results showed that the knockdown of Parkin effectively blocked p62 degradation and LC3 lipidation and that PINK1 expression was significantly inhibited in LMH cells, further suggesting that the knockdown of Parkin effectively inhibited mitophagy. Mitochondrial morphology, MMP, and ROS were observed using laser confocal. The results showed that Parkin knockdown resulted in mitochondrial fission and increased levels of reactive oxygen species, together with increased depolarization of the mitochondrial membrane potential. These changes led to increased mitochondrial damage. In conclusion, this study showed that Cr (VI) could cause the occurrence of mitophagy by damaging mitochondria, and Parkin played a crucial role in Cr (VI)-induced mitophagy in chicken hepatocytes