FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise

Federated learning (FL) has emerged as a promising paradigm for training segmentation models on decentralized medical data, owing to its privacy-preserving property. However, existing research overlooks the prevalent annotation noise encountered in real-world medical datasets, which limits the performance ceilings of FL. In this paper, we, for the first time, identify and tackle this problem. For problem formulation, we propose a contour evolution for modeling non-independent and identically distributed (Non-IID) noise across pixels within each client and then extend it to the case of multi-source data to form a heterogeneous noise model (i.e., Non-IID annotation noise across clients). For robust learning from annotations with such two-level Non-IID noise, we emphasize the importance of data quality in model aggregation, allowing high-quality clients to have a greater impact on FL. To achieve this, we propose Federated learning with Annotation quAlity-aware AggregatIon, named FedA3I, by introducing a quality factor based on client-wise noise estimation. Specifically, noise estimation at each client is accomplished through the Gaussian mixture model and then incorporated into model aggregation in a layer-wise manner to up-weight high-quality clients. Extensive experiments on two real-world medical image segmentation datasets demonstrate the superior performance of FedA$^3$I against the state-of-the-art approaches in dealing with cross-client annotation noise. The code is available at https://github.com/wnn2000/FedAAAI.Comment: Accepted at AAAI'2

C2FTrans: Coarse-to-Fine Transformers for Medical Image Segmentation

Convolutional neural networks (CNN), the most prevailing architecture for deep-learning based medical image analysis, are still functionally limited by their intrinsic inductive biases and inadequate receptive fields. Transformer, born to address this issue, has drawn explosive attention in natural language processing and computer vision due to its remarkable ability in capturing long-range dependency. However, most recent transformer-based methods for medical image segmentation directly apply vanilla transformers as an auxiliary module in CNN-based methods, resulting in severe detail loss due to the rigid patch partitioning scheme in transformers. To address this problem, we propose C2FTrans, a novel multi-scale architecture that formulates medical image segmentation as a coarse-to-fine procedure. C2FTrans mainly consists of a cross-scale global transformer (CGT) which addresses local contextual similarity in CNN and a boundary-aware local transformer (BLT) which overcomes boundary uncertainty brought by rigid patch partitioning in transformers. Specifically, CGT builds global dependency across three different small-scale feature maps to obtain rich global semantic features with an acceptable computational cost, while BLT captures mid-range dependency by adaptively generating windows around boundaries under the guidance of entropy to reduce computational complexity and minimize detail loss based on large-scale feature maps. Extensive experimental results on three public datasets demonstrate the superior performance of C2FTrans against state-of-the-art CNN-based and transformer-based methods with fewer parameters and lower FLOPs. We believe the design of C2FTrans would further inspire future work on developing efficient and lightweight transformers for medical image segmentation. The source code of this paper is publicly available at https://github.com/xianlin7/C2FTrans

SAMUS: Adapting Segment Anything Model for Clinically-Friendly and Generalizable Ultrasound Image Segmentation

Segment anything model (SAM), an eminent universal image segmentation model, has recently gathered considerable attention within the domain of medical image segmentation. Despite the remarkable performance of SAM on natural images, it grapples with significant performance degradation and limited generalization when confronted with medical images, particularly with those involving objects of low contrast, faint boundaries, intricate shapes, and diminutive sizes. In this paper, we propose SAMUS, a universal model tailored for ultrasound image segmentation. In contrast to previous SAM-based universal models, SAMUS pursues not only better generalization but also lower deployment cost, rendering it more suitable for clinical applications. Specifically, based on SAM, a parallel CNN branch is introduced to inject local features into the ViT encoder through cross-branch attention for better medical image segmentation. Then, a position adapter and a feature adapter are developed to adapt SAM from natural to medical domains and from requiring large-size inputs (1024x1024) to small-size inputs (256x256) for more clinical-friendly deployment. A comprehensive ultrasound dataset, comprising about 30k images and 69k masks and covering six object categories, is collected for verification. Extensive comparison experiments demonstrate SAMUS's superiority against the state-of-the-art task-specific models and universal foundation models under both task-specific evaluation and generalization evaluation. Moreover, SAMUS is deployable on entry-level GPUs, as it has been liberated from the constraints of long sequence encoding. The code, data, and models will be released at https://github.com/xianlin7/SAMUS

Effects of Root-Zone Temperature and N, P, and K Supplies on Nutrient Uptake of Cucumber (Cucumis sativus L.) Seedlings in Hydroponics

The nutrient uptake and allocation of cucumber (Cucumis sativus L.) seedlings at different root-zone temperatures (RZT) and different concentrations of nitrogen (N), phosphorus (P), and potassium (K) nutrients were examined. Plants were grown in a nutrient solution for 30 d at two root-zone temperatures (a diurnally fluctuating ambient 10°C-RZT and a constant 20° C-RZT) with the aerial parts of the plants maintained at ambient temperature (10°C -30°C). Based on a Hoagland nutrient solution, seven N, P, and K nutrient concentrations were supplied to the plants at each RZT. Results showed that total plant and shoot dry weights under each nutrient treatment were significantly lower at low root-zone temperature (10°C-RZT) than at elevated root-zone temperature (20°C-RZT). But higher root dry weights were obtained at 10°C-RZT than those at 20°C-RZT. Total plant dry weights at both 10°C-RZT and 20°C-RZT were increased with increased solution N concentration, but showed different responses under P and K treatments. All estimated nutrient concentrations (N, P, and K) and uptake by the plant were obviously influenced by RZT. Low root temperature (10°C-RZT) caused a remarkable reduction in total N, P, and K uptake of shoots in all nutrient treatments, and more nutrients were accumulated in roots at 10 degrees C-RZT than those at 20°C-RZT. N, P, and K uptakes and distribution ratios in shoots were both improved at elevated root-zone temperature (20° C-RZT). N supplies were favorable to P and K uptake at both 10°C-RZT and 20°C-RZT, with no significantly positive correlation between N and P, or N and K uptake. In conclusion, higher RZT was more beneficial to increase of plant biomass and mineral nutrient absorption than was increase of nutrient concentration. Among the three element nutrients, increasing N nutrient concentration in solution promoted better tolerance to low RZT in cucumber seedlings than increasing P and K. In addition, appropriately decreased P concentration favors plant growth

Does the Short Term Fluctuation of Mineral Element Concentrations in the Closed Hydroponic Experimental Facilities Affect the Mineral Concentrations in Cucumber Plants Exposed to Elevated CO\u3csub\u3e2\u3c/sub\u3e?

Aims Studies dealing with plants’ mineral nutrient status under elevated atmospheric CO2concentration (eCO2) are usually conducted in closed hydroponic systems, in which nutrient solutions are entirely renewed every several days. Here, we investigated the contribution of the fluctuation of concentrations of N ([N]), P ([P]), and K ([K]) in nutrient solutions in this short period on their concentrations in cucumber plants exposed to different [CO2] and N levels. Methods Cucumber (Cucumis sativus L.) plants were hydroponically grown under two [CO2] and three N levels. [N], [P], and [K] in nutrient solutions and cucumber plants were analyzed. Results The transpiration rate (Tr) was significantly inhibited by eCO2, whereas Tr per plant was increased due to the larger leaf area. Elevated [CO2] significantly decreased [N] in low N nutrient solutions, which imposed an additional decrease in [N] in plants. [P] in nutrient solutions fluctuated slightly, so the change of [P] in plants might be attributed to the dilution effect and the demand change under eCO2. [K] in moderate and high N nutrient solutions were significantly decreased, which exacerbated the [K] decrease in plants under eCO2. Conclusions The short-term fluctuation of [N] and [K] in nutrient solutions is caused by the asynchronous uptakes of N, K, and water under eCO2, which has an appreciable influence on [N] and [K] in plants besides the dilution effect. This defect of the closed hydroponic system may let us exaggerate the negative impact of eCO2 itself on [N] and [K] in plants

Federated Learning with Imbalanced and Agglomerated Data Distribution for Medical Image Classification

Federated learning (FL), training deep models from decentralized data without privacy leakage, has drawn great attention recently. Two common issues in FL, namely data heterogeneity from the local perspective and class imbalance from the global perspective have limited FL's performance. These two coupling problems are under-explored, and existing few studies may not be sufficiently realistic to model data distributions in practical sceneries (e.g. medical sceneries). One common observation is that the overall class distribution across clients is imbalanced (e.g. common vs. rare diseases) and data tend to be agglomerated to those more advanced clients (i.e., the data agglomeration effect), which cannot be modeled by existing settings. Inspired by real medical imaging datasets, we identify and formulate a new and more realistic data distribution denoted as L2 distribution where global class distribution is highly imbalanced and data distributions across clients are imbalanced but forming a certain degree of data agglomeration. To pursue effective FL under this distribution, we propose a novel privacy-preserving framework named FedIIC that calibrates deep models to alleviate bias caused by imbalanced training. To calibrate the feature extractor part, intra-client contrastive learning with a modified similarity measure and inter-client contrastive learning guided by shared global prototypes are introduced to produce a uniform embedding distribution of all classes across clients. To calibrate the classification heads, a softmax cross entropy loss with difficulty-aware logit adjustment is constructed to ensure balanced decision boundaries of all classes. Experimental results on publicly-available datasets demonstrate the superior performance of FedIIC in dealing with both the proposed realistic modeling and the existing modeling of the two coupling problems

Interactive Effects of the CO\u3csub\u3e2\u3c/sub\u3e Enrichment and Nitrogen Supply on the Biomass Accumulation, Gas Exchange Properties, and Mineral Elements Concentrations in Cucumber Plants at Different Growth Stages

The concentration changes of mineral elements in plants at different CO2 concentrations ([CO2]) and nitrogen (N) supplies and the mechanisms which control such changes are not clear. Hydroponic trials on cucumber plants with three [CO2] (400, 625, and 1200 µmol mol−1) and five N supply levels (2, 4, 7, 14, and 21 mmol L−1) were conducted. When plants were in high N supply, the increase in total biomass by elevated [CO2] was 51.7% and 70.1% at the seedling and initial fruiting stages, respectively. An increase in net photosynthetic rate (Pn) by more than 60%, a decrease in stomatal conductance (Gs) by 21.2–27.7%, and a decrease in transpiration rate (Tr) by 22.9–31.9% under elevated [CO2] were also observed. High N supplies could further improve the Pn and offset the decrease of Gs and Tr by elevated [CO2]. According to the mineral concentrations and the correlation results, we concluded the main factors affecting these changes. The dilution effect was the main factor driving the reduction of all mineral elements, whereas Tr also had a great impact on the decrease of [N], [K], [Ca], and [Mg] except [P]. In addition, the demand changes of N, Ca, and Mg influenced the corresponding element concentrations in cucumber plants

Sleep duration in Chinese adolescents: biological, environmental, and behavioral predictors

AbstractObjectiveTo examine sleep duration-related risk factors from multidimensional domains among Chinese adolescents.MethodsA random sample of 4801 adolescents aged 11–20 years participated in a cross-sectional survey. A self-reported questionnaire was used to collect information about the adolescents' sleep behaviors and possible related factors from eight domains.ResultsIn all, 51.0% and 9.8% of adolescents did not achieve optimal sleep duration (defined as <8.0 h per day) on weekdays and on weekends, respectively. According to multivariate logistic regression models, after adjusting for all possible confounders, 17 factors were associated with sleep duration <8 h. Specifically, 13 factors from five domains were linked to physical and psychosocial condition, environment, and behaviors. These factors were overweight/obesity, chronic pain, bedtime anxiety/excitement/depression, bed/room sharing, school starting time earlier than 07:00, cram school learning, more time spent on homework on weekdays, television viewing ≥2 h/day, physical activity <1 h/day, irregular bedtime, and shorter sleep duration of father.ConclusionBiological and psychosocial conditions, sleep environments, school schedules, daily activity and behaviors, and parents' sleep habits significantly may affect adolescents' sleep duration, indicating that the existing chronic sleep loss in adolescents could be, at least partly, intervened by improving adolescents' physical and psychosocial conditions, controlling visual screen exposure, regulating school schedules, improving sleep hygiene and daytime behaviors, and changing parents' sleep habits

Bisacurone gel ameliorated burn wounds in experimental rats via its anti-inflammatory, antioxidant, and angiogenic properties

ABSTRACT Purpose: To investigate putative mechanism of wound healing for chitosan-based bisacurone gel against secondary burn wounds in rats. Methods: A second-degree burn wound with an open flame using mixed fuel (2 mL, 20 seconds) was induced in Sprague Dawley rats (male, 180-220 g, n = 15, each) followed by topical treatments with either vehicle control (white petroleum gel, 1%), silver sulfadiazine (1%) or bisacurone gel (2.5, 5, or 10%) for 20 days. Wound contraction rate and paw withdrawal threshold were monitored on various days. Oxidative stress (superoxide dismutase, glutathione, malondialdehyde, and nitric oxide), pro-inflammatory cytokines (tumour necrosis factor-alpha, interleukins by enzyme-linked immunosorbent assay), growth factors (transforming growth factor-β, vascular endothelial growth factor C using real time polymerase chain reaction and Western blot assay) levels, and histology of wound skin were assessed at the end. Results: Bisacurone gel showed 98.72% drug release with a 420.90–442.70 cps viscosity. Bisacurone gel (5 and 10%) significantly (p < 0.05) improved wound contraction rate and paw withdrawal threshold. Bisacurone gel attenuated oxidative stress, pro-inflammatory cytokines, and water content. It also enhanced angiogenesis (hydroxyproline and growth factor) and granulation in wound tissue than vehicle control. Conclusions: These findings suggested that bisacurone gel can be a potential candidate to treat burn wounds via its anti-inflammatory, antioxidant, and angiogenic properties