ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation

The domain discrepancy existed between medical images acquired in different situations renders a major hurdle in deploying pre-trained medical image segmentation models for clinical use. Since it is less possible to distribute training data with the pre-trained model due to the huge data size and privacy concern, source-free unsupervised domain adaptation (SFDA) has recently been increasingly studied based on either pseudo labels or prior knowledge. However, the image features and probability maps used by pseudo label-based SFDA and the consistent prior assumption and the prior prediction network used by prior-guided SFDA may become less reliable when the domain discrepancy is large. In this paper, we propose a \textbf{Pro}mpt learning based \textbf{SFDA} (\textbf{ProSFDA}) method for medical image segmentation, which aims to improve the quality of domain adaption by minimizing explicitly the domain discrepancy. Specifically, in the prompt learning stage, we estimate source-domain images via adding a domain-aware prompt to target-domain images, then optimize the prompt via minimizing the statistic alignment loss, and thereby prompt the source model to generate reliable predictions on (altered) target-domain images. In the feature alignment stage, we also align the features of target-domain images and their styles-augmented counterparts to optimize the source model, and hence push the model to extract compact features. We evaluate our ProSFDA on two multi-domain medical image segmentation benchmarks. Our results indicate that the proposed ProSFDA outperforms substantially other SFDA methods and is even comparable to UDA methods. Code will be available at \url{https://github.com/ShishuaiHu/ProSFDA}

URL: Combating Label Noise for Lung Nodule Malignancy Grading

Due to the complexity of annotation and inter-annotator variability, most lung nodule malignancy grading datasets contain label noise, which inevitably degrades the performance and generalizability of models. Although researchers adopt the label-noise-robust methods to handle label noise for lung nodule malignancy grading, they do not consider the inherent ordinal relation among classes of this task. To model the ordinal relation among classes to facilitate tackling label noise in this task, we propose a Unimodal-Regularized Label-noise-tolerant (URL) framework. Our URL contains two stages, the Supervised Contrastive Learning (SCL) stage and the Memory pseudo-labels generation and Unimodal regularization (MU) stage. In the SCL stage, we select reliable samples and adopt supervised contrastive learning to learn better representations. In the MU stage, we split samples with multiple annotations into multiple samples with a single annotation and shuffle them into different batches. To handle label noise, pseudo-labels are generated using the similarity between each sample and the central feature of each class, and temporal ensembling is used to obtain memory pseudo-labels that supervise the model training. To model the ordinal relation, we introduce unimodal regularization to keep the ordinal relation among classes in the predictions. Moreover, each lung nodule is characterized by three orthographic views. Experiments conducted on the LIDC-IDRI dataset indicate the superiority of our URL over other competing methods. Code is available at https://github.com/axz520/UR.Comment: 11 pages, accepted by DALI@MICCAI202

Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation

Deep learning-based medical image segmentation models suffer from performance degradation when deployed to a new healthcare center. To address this issue, unsupervised domain adaptation and multi-source domain generalization methods have been proposed, which, however, are less favorable for clinical practice due to the cost of acquiring target-domain data and the privacy concerns associated with redistributing the data from multiple source domains. In this paper, we propose a \textbf{C}hannel-level \textbf{C}ontrastive \textbf{S}ingle \textbf{D}omain \textbf{G}eneralization (\textbf{C$^2$SDG}) model for medical image segmentation. In C$^2$SDG, the shallower features of each image and its style-augmented counterpart are extracted and used for contrastive training, resulting in the disentangled style representations and structure representations. The segmentation is performed based solely on the structure representations. Our method is novel in the contrastive perspective that enables channel-wise feature disentanglement using a single source domain. We evaluated C$^2$SDG against six SDG methods on a multi-domain joint optic cup and optic disc segmentation benchmark. Our results suggest the effectiveness of each module in C$^2$SDG and also indicate that C$^2$SDG outperforms the baseline and all competing methods with a large margin. The code will be available at \url{https://github.com/ShishuaiHu/CCSDG}.Comment: 12 pages, 5 figure

Transformer-based Annotation Bias-aware Medical Image Segmentation

Manual medical image segmentation is subjective and suffers from annotator-related bias, which can be mimicked or amplified by deep learning methods. Recently, researchers have suggested that such bias is the combination of the annotator preference and stochastic error, which are modeled by convolution blocks located after decoder and pixel-wise independent Gaussian distribution, respectively. It is unlikely that convolution blocks can effectively model the varying degrees of preference at the full resolution level. Additionally, the independent pixel-wise Gaussian distribution disregards pixel correlations, leading to a discontinuous boundary. This paper proposes a Transformer-based Annotation Bias-aware (TAB) medical image segmentation model, which tackles the annotator-related bias via modeling annotator preference and stochastic errors. TAB employs the Transformer with learnable queries to extract the different preference-focused features. This enables TAB to produce segmentation with various preferences simultaneously using a single segmentation head. Moreover, TAB takes the multivariant normal distribution assumption that models pixel correlations, and learns the annotation distribution to disentangle the stochastic error. We evaluated our TAB on an OD/OC segmentation benchmark annotated by six annotators. Our results suggest that TAB outperforms existing medical image segmentation models which take into account the annotator-related bias.Comment: 11 pages, 2 figure

The Impact of Mentoring Relationships on Innovation Performance of Post-90s Employees: A Dual-Path Model of Cognition and Affect

(1) Background: In recent years, post-90s employees have emerged as the driving force behind enterprise innovation, presenting unique challenges for innovation management. Their distinct characteristics and attitudes towards work require a thoughtful and adaptable approach from businesses to harness their potential effectively; (2) Methods: through empirical analysis of 518 valid samples in the Chinese context, with SPSS 26.0 and PROCESS V4.1 being used for the analysis, and to test the moderated mediation model; (3) Results: a. Mentoring relationships positively predict innovation performance; b. This relationship is mediated by role stress (cognition) and job vigor (affect); c. Innovative self-efficacy negatively moderates the impact of role stress on innovation performance and positively moderates the impact of job vigor on innovation performance; d. Moreover, innovative self-efficacy significantly moderates the mediating effect of role stress and job vigor, and the moderated mediating model is established; (4) Conclusions: Our findings reveal the “black box” of mentoring relationships in the process of influencing the innovation performance of post-90s employees, an area that has received limited research attention. This study further reveals the boundary effect of innovative self-efficacy

Highly efficient and selective hydrogenation of quinolines at room temperature over Ru@NC-500 catalyst

Selective hydrogenation of quinolines into 1,2,3,4-tetrahydroquinolines under mild conditions holds tremendous promise for the green synthesis of a multitude of fine chemicals. Herein, we describe nitrogen-doped carbon supported ruthenium nanoparticles were robust for the mide and selective hydrogenation of quinolines to the corresponding 1,2,3,4-tetrahydroquinolines with both excellent activity and selectivity at 30 similar to 50 degrees C and 10 bar H-2

Longitudinal variation characteristics of stable isotope ratios of suspended particulate organic matter in the headwaters of the Qingjiang River, China

To determine the sources and characteristics of suspended particulate organic matter (SPOM), the spatial distribution of carbon and nitrogen and their isotopic values (δ13C and δ15N) were measured from upstreamto downstream (i.e. site 1 to site 4) in the head waters of the Qingjiang River in central China. The mean annual SPOM δ15N and δ13C values varied between sites but exhibited a unimodal pattern. The mean annual δ15N increased from site 1 (2.5‰) to 3 (5.3‰), followed by a major decrease to 2.2‰ at site 4. Furthermore, the mean annual δ13C varied unimodally, being the most positiveat sites 1 (−21.6‰) and 4 (−22.8‰) followed by sites 2 (−24.5‰) and 3 (−26.4‰). In particular, the mean SPOM δ15N and δ13C in the tailwaters from a domestic wastewater treatment plant, which was located approximately 0.3 km upstream of site 4, were 2.2‰ and −25.6‰, respectively. The SPOM C/N values from stream water at site 4 (8.5 ± 1.5) and tailwater (6.2 ± 0.9) were similar. Collectively, the results suggested that wastewater treatment plant tailwater influenced the stable isotope values of SPOM in the stream and affected the variation trendfrom upstream to downstream

Enhanced thermal conductivity of polydimethylsiloxane composites with carbon fiber

Highly thermal conductive thermal interface materials are playing an irreplaceable role in modern highly integrated microelectronic devices. A facile method was used to fabricate a highly thermal conductive composite combined with highly thermal conductivity and excellent flexibility. The carbon fiber/polydimethylsiloxane (CF/PDMS) composites were prepared by solution blending with PDMS as matrix and different contents CF as thermal conductive fillers. Thermal conductivity of the PDMS composite is up to 2.73 W/mK with 20 wt% CF filler loading. Otherwise, we have characterized the heat transport performance of composites under various conditions. The results indicate that it has very stable and efficient thermal conductivity, which can meet the heat dissipation requirements under various conditions. Therefore, the CF/PDMS composite is a promising thermal management material that can be used to the heat dissipation of electronic devices in the near future