Clean Label Disentangling for Medical Image Segmentation with Noisy Labels

Current methods focusing on medical image segmentation suffer from incorrect annotations, which is known as the noisy label issue. Most medical image segmentation with noisy labels methods utilize either noise transition matrix, noise-robust loss functions or pseudo-labeling methods, while none of the current research focuses on clean label disentanglement. We argue that the main reason is that the severe class-imbalanced issue will lead to the inaccuracy of the selected ``clean'' labels, thus influencing the robustness of the model against the noises. In this work, we come up with a simple but efficient class-balanced sampling strategy to tackle the class-imbalanced problem, which enables our newly proposed clean label disentangling framework to successfully select clean labels from the given label sets and encourages the model to learn from the correct annotations. However, such a method will filter out too many annotations which may also contain useful information. Therefore, we further extend our clean label disentangling framework to a new noisy feature-aided clean label disentangling framework, which takes the full annotations into utilization to learn more semantics. Extensive experiments have validated the effectiveness of our methods, where our methods achieve new state-of-the-art performance. Our code is available at https://github.com/xiaoyao3302/2BDenoise.Comment: 13 pages, 6 figures, 11 table

Progressive Target-Styled Feature Augmentation for Unsupervised Domain Adaptation on Point Clouds

Unsupervised domain adaptation is a critical challenge in the field of point cloud analysis, as models trained on one set of data often struggle to perform well in new scenarios due to domain shifts. Previous works tackle the problem by using adversarial training or self-supervised learning for feature extractor adaptation, but ensuring that features extracted from the target domain can be distinguished by the source-supervised classifier remains challenging. In this work, we propose a novel approach called progressive target-styled feature augmentation (PTSFA). Unlike previous works that focus on feature extractor adaptation, our PTSFA approach focuses on classifier adaptation. It aims to empower the classifier to recognize target-styled source features and progressively adapt to the target domain. To enhance the reliability of predictions within the PTSFA framework and encourage discriminative feature extraction, we further introduce a new intermediate domain approaching (IDA) strategy. We validate our method on the benchmark datasets, where our method achieves new state-of-the-art performance. Our code is available at https://github.com/xiaoyao3302/PTSFA.Comment: 14 pages, 6 figures, 8 table

Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation

Semi-supervised medical image segmentation studies have shown promise in training models with limited labeled data. However, current dominant teacher-student based approaches can suffer from the confirmation bias. To address this challenge, we propose AD-MT, an alternate diverse teaching approach in a teacher-student framework. It involves a single student model and two non-trainable teacher models that are momentum-updated periodically and randomly in an alternate fashion. To mitigate the confirmation bias from the diverse supervision, the core of AD-MT lies in two proposed modules: the Random Periodic Alternate (RPA) Updating Module and the Conflict-Combating Module (CCM). The RPA schedules the alternating diverse updating process with complementary data batches, distinct data augmentation, and random switching periods to encourage diverse reasoning from different teaching perspectives. The CCM employs an entropy-based ensembling strategy to encourage the model to learn from both the consistent and conflicting predictions between the teachers. Experimental results demonstrate the effectiveness and superiority of our AD-MT on the 2D and 3D medical segmentation benchmarks across various semi-supervised settings.Comment: code:https://github.com/ZhenZHAO/AD-M

Evidence for elevated emissions from high-latitude wetlands contributing to high atmospheric CH4 concentration in the early Holocene

The major increase in atmospheric methane (CH4) concentration during the last glacial-interglacial transition provides a useful example for understanding the interactions and feedbacks among Earth\u27s climate, biosphere carbon cycling, and atmospheric chemistry. However, the causes of CH4 doubling during the last deglaciation are still uncertain and debated. Although the ice-core data consistently suggest a dominant contribution from northern high-latitude wetlands in the early Holocene, identifying the actual sources from the ground-based data has been elusive. Here we present data syntheses and a case study from Alaska to demonstrate the importance of northern wetlands in contributing to high atmospheric CH4concentration in the early Holocene. Our data indicate that new peatland formation as well as peat accumulation in northern high-latitude regions increased more than threefold in the early Holocene in response to climate warming and the availability of new habitat as a result of deglaciation. Furthermore, we show that marshes and wet fens that represent early stages of wetland succession were likely more widespread in the early Holocene. These wetlands are associated with high CH4 emissions due to high primary productivity and the presence of emergent plant species that facilitate CH4 transport to the atmosphere. We argue that early wetland succession and rapid peat accumulation and expansion (not simply initiation) contributed to high CH4 emissions from northern regions, potentially contributing to the sharp rise in atmospheric CH4 at the onset of the Holocene

Colpitts Chaotic Oscillator Coupling with a Generalized Memristor

By introducing a generalized memristor into a fourth-order Colpitts chaotic oscillator, a new memristive Colpitts chaotic oscillator is proposed in this paper. The generalized memristor is equivalent to a diode bridge cascaded with a first-order parallel RC filter. Chaotic attractors of the oscillator are numerically revealed from the mathematical model and experimentally captured from the physical circuit. The dynamics of the memristive Colpitts chaotic oscillator is investigated both theoretically and numerically, from which it can be found that the oscillator has a unique equilibrium point and displays complex nonlinear phenomena

Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation

Semi-supervised semantic segmentation (SSS) has recently gained increasing research interest as it can reduce the requirement for large-scale fully-annotated training data. The current methods often suffer from the confirmation bias from the pseudo-labelling process, which can be alleviated by the co-training framework. The current co-training-based SSS methods rely on hand-crafted perturbations to prevent the different sub-nets from collapsing into each other, but these artificial perturbations cannot lead to the optimal solution. In this work, we propose a new conflict-based cross-view consistency (CCVC) method based on a two-branch co-training framework which aims at enforcing the two sub-nets to learn informative features from irrelevant views. In particular, we first propose a new cross-view consistency (CVC) strategy that encourages the two sub-nets to learn distinct features from the same input by introducing a feature discrepancy loss, while these distinct features are expected to generate consistent prediction scores of the input. The CVC strategy helps to prevent the two sub-nets from stepping into the collapse. In addition, we further propose a conflict-based pseudo-labelling (CPL) method to guarantee the model will learn more useful information from conflicting predictions, which will lead to a stable training process. We validate our new CCVC approach on the SSS benchmark datasets where our method achieves new state-of-the-art performance. Our code is available at https://github.com/xiaoyao3302/CCVC.Comment: accepted by CVPR202

Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification

Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical industrial applications. One reason for this academic-industrial gap is the neighborhood-fetching latency incurred by data dependency in GNNs, which make it hard to deploy for latency-sensitive applications that require fast inference. Conversely, without involving any feature aggregation, MLPs have no data dependency and infer much faster than GNNs, but their performance is less competitive. Motivated by these complementary strengths and weaknesses, we propose a Graph Self-Distillation on Neighborhood (GSDN) framework to reduce the gap between GNNs and MLPs. Specifically, the GSDN framework is based purely on MLPs, where structural information is only implicitly used as prior to guide knowledge self-distillation between the neighborhood and the target, substituting the explicit neighborhood information propagation as in GNNs. As a result, GSDN enjoys the benefits of graph topology-awareness in training but has no data dependency in inference. Extensive experiments have shown that the performance of vanilla MLPs can be greatly improved with self-distillation, e.g., GSDN improves over stand-alone MLPs by 15.54\% on average and outperforms the state-of-the-art GNNs on six datasets. Regarding inference speed, GSDN infers 75X-89X faster than existing GNNs and 16X-25X faster than other inference acceleration methods

Modeling Holocene Peatland Carbon Accumulation in North America

Peatlands are a large carbon reservoir. Yet the quantification of their carbon stock still has a large uncertainty due to lacking observational data and well‐tested peatland biogeochemistry models. Here, a process‐based peatland model was calibrated using long‐term peat carbon accumulation data at multiple sites in North America. The model was then applied to quantify the peat carbon accumulation rates and stocks within North America over the last 12,000 years. We estimated that 85–174 Pg carbon was accumulated in North American peatlands over the study period including 0.37–0.76 Pg carbon in subtropical peatlands. During the period from 10,000 to 8,000 years ago, the warmer and wetter conditions might have played an important role in stimulating peat carbon accumulation by enhancing plant photosynthesis. Enhanced peat decomposition due to warming slowed the carbon accumulation through the rest of the Holocene. While recent modeling studies indicate that the northern peatlands will continue to act as a carbon sink in this century, our studies suggest that future enhanced peat decomposition accompanied by peatland areal changes induced by permafrost degradation and other disturbances shall confound the sink and source analysis

High C1QTNF1 expression mediated by potential ncRNAs is associated with poor prognosis and tumor immunity in kidney renal clear cell carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) originates from proximal tubular cells and is the most common subtype of renal cell carcinoma. KIRC is characterized by changes in lipid metabolism, and obesity is a risk factor for it. C1q And TNF Related 1 (C1QTNF1), a novel adipokine and member of the C1q and TNF-related protein (CTRP) family, has been shown to affect the progression of various cancers. However, the role of C1QTNF1 in KIRC has not been studied.Methods: The Wilcoxon rank sum test was used to analyze the expression of C1QTNF1 in KIRC tissues and normal tissues. The relationship between clinicopathological features and C1QTNF1 levels was also examined by logistic regression and the Wilcoxon rank sum test. In addition, the effect of C1QTNF1 on the prognosis of KIRC patients was analyzed by Kaplan-Meier (KM). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the potential signaling pathways and biological functions of differential genes. A nomogram was constructed to predict the prognosis of KIRC patients. Spearman correlation analysis was performed to determine the association between C1QTNF1 expression and immune cell infiltration and immune checkpoint genes. The upstream miRNAs and lncRNAs of C1QTNF1 were predicted by the ENCORI online tool. Finally, we examined the proliferation, invasion, and migration abilities of KIRC cells after C1QTNF1 knockdown.Results: The expression of C1QTNF1 in KIRC tissues was significantly higher than in normal renal tissues. Patients with higher C1QTNF1 expression had a poor prognosis, a finding supported by Kaplan-Meier survival analysis. C1QTNF1 expression was significantly correlated with TNM and pathologic stages, age, and gender (p < 0.05). The C1QTNF1 expression level was significantly correlated with immune cell infiltration and immune checkpoint genes in KIRC. Additionally, high C1QTNF1 expression was associated with poor prognosis in stage I and II, T1 and T2, T3 and T4, N0, and M0 patients (HR > 1, p < 0.05). The calibration diagram shows that the C1QTNF1 model has effective predictive performance for the survival of KIRC patients. Knockdown of C1QTNF1 inhibited KIRC cell proliferation, cell migration, and cell invasion. In addition, CYTOR and AC040970.1/hsa-miR-27b-3p axis were identified as the most likely upstream ncRNA-related pathways of C1QTNF1 in KIRC.Conclusion: In conclusion, our study suggests that high expression of C1QTNF1 is associated with KIRC progression and immune infiltration. The increased expression of C1QTNF1 suggests a poor prognosis in KIRC patients

Kagome surface states and weak electronic correlation in vanadium-kagome metals

RV6Sn6 (R = Y and lanthanides) with two-dimensional vanadium-kagome surface states is an ideal platform to investigate kagome physics and manipulate the kagome features to realize novel phenomena. Utilizing the micron-scale spatially resolved angle-resolved photoemission spectroscopy and first-principles calculations, we report a systematical study of the electronic structures of RV6Sn6 (R = Gd, Tb, and Lu) on the two cleaved surfaces, i.e., the V- and RSn1-terminated (001) surfaces. The calculated bands without any renormalization match well with the main ARPES dispersive features, indicating the weak electronic correlation in this system. We observe 'W'-like kagome surface states around the Brillouin zone corners showing R-element-dependent intensities, which is probably due to various coupling strengths between V and RSn1 layers. Our finding suggests an avenue for tuning electronic states by interlayer coupling based on two-dimensional kagome lattices