Boundary-aware Prototype in Semi-supervised Medical Image Segmentation.

The true label plays an important role in semi-supervised medical image segmentation (SSMIS) because it can provide the most accurate supervision information when the label is limited. The popular SSMIS method trains labeled and unlabeled data separately, and the unlabeled data cannot be directly supervised by the true label. This limits the contribution of labels to model training. Is there an interactive mechanism that can break the separation between two types of data training to maximize the utilization of true labels? Inspired by this, we propose a novel consistency learning framework based on the non-parametric distance metric of boundary-aware prototypes to alleviate this problem. This method combines CNN-based linear classification and nearest neighbor-based non-parametric classification into one framework, encouraging the two segmentation paradigms to have similar predictions for the same input. More importantly, the prototype can be clustered from both labeled and unlabeled data features so that it can be seen as a bridge for interactive training between labeled and unlabeled data. When the prototype-based prediction is supervised by the true label, the supervisory signal can simultaneously affect the feature extraction process of both data. In addition, boundary-aware prototypes can explicitly model the differences in boundaries and centers of adjacent categories, so pixel-prototype contrastive learning is introduced to further improve the discriminability of features and make them more suitable for non-parametric distance measurement. Experiments show that although our method uses a modified lightweight UNet as the backbone, it outperforms the comparison method using a 3D VNet with more parameters.

Adapting Few-Shot Classification via In-Process Defense.

Most few-shot learning methods employ either adaptive approaches or parameter amortization techniques. However, their reliance on pre-trained models presents a significant vulnerability. When an attacker's trigger activates a hidden backdoor, it may result in the misclassification of images, profoundly affecting the model's performance. In our research, we explore adaptive defenses against backdoor attacks for few-shot learning. We introduce a specialized stochastic process tailored to task characteristics that safeguards the classification model against attack-induced incorrect feature extraction. This process functions during forward propagation and is thus termed an "in-process defense." Our method employs an adaptive strategy, effectively generating task-level representations, enabling rapid adaptation to pre-trained models, and proving effective in few-shot classification scenarios for countering backdoor attacks. We apply latent stochastic processes to approximate task distributions and derive task-level representations from the support set. This task-level representation guides feature extraction, leading to backdoor trigger mismatching and forming the foundation of our parameter defense strategy. Benchmark tests on Meta-Dataset reveal that our approach not only withstands backdoor attacks but also shows an improved adaptation in addressing few-shot classification tasks.

Image-Level Adaptive Adversarial Ranking for Person Re-Identification.

The potential vulnerability of deep neural networks and the complexity of pedestrian images, greatly limits the application of person re-identification techniques in the field of smart security. Current attack methods often focus on generating carefully crafted adversarial samples or only disrupting the metric distances between targets and similar pedestrians. However, both aspects are crucial for evaluating the security of methods adapted for person re-identification tasks. For this reason, we propose an image-level adaptive adversarial ranking method that comprehensively considers two aspects to adapt to changes in pedestrians in the real world and effectively evaluate the robustness of models in adversarial environments. To generate more refined adversarial samples, our image representation enhancement module leverages channel-wise information entropy, assigning varying weights to different channels to produce images with richer information content, along with a generative adversarial network to create adversarial samples. Subsequently, for adaptive perturbation of ranking, the adaptive weight confusion ranking loss is presented to calculate the weights of distances between positive or negative samples and query samples. It endeavors to push positive samples away from query samples and bring negative samples closer, thereby interfering with the ranking of system. Notably, this method requires no additional hyperparameter tuning or extra data training, making it an adaptive attack strategy. Experimental results on large-scale datasets such as Market1501, CUHK03, and DukeMTMC demonstrate the effectiveness of our method in attacking ReID systems.

Contrasting drought tolerance traits of woody plants is associated with mycorrhizal types at the global scale.

It is well-known that the mycorrhizal type of plants correlates with different modes of nutrient cycling and availability. However, the differences in drought tolerance between arbuscular mycorrhizal (AM) and ectomycorrhizal (EcM) plants remains poorly characterized. We synthesized a global dataset of four hydraulic traits associated with drought tolerance of 1457 woody species (1139 AM and 318 EcM species) at 308 field sites. We compared these traits between AM and EcM species, with evolutionary history (i.e. angiosperms vs gymnosperms), water availability (i.e. aridity index) and biomes considered as additional factors. Overall, we found that evolutionary history and biogeography influenced differences in hydraulic traits between mycorrhizal types. Specifically, we found that (1) AM angiosperms are less drought-tolerant than EcM angiosperms in wet regions or biomes, but AM gymnosperms are more drought-tolerant than EcM gymnosperms in dry regions or biomes, and (2) in both angiosperms and gymnosperms, variation in hydraulic traits as well as their sensitivity to water availability were higher in AM species than in EcM species. Our results suggest that global shifts in water availability (especially drought) may alter the biogeographic distribution and abundance of AM and EcM plants, with consequences for ecosystem element cycling and ultimately, the land carbon sink.

Local artifacts amplification for deepfakes augmentation.

With the rapid and continuous development of AIGC, It is becoming increasingly difficult to distinguish between real and forged facial images, which calls for efficient forgery detection systems. Although many detection methods have noticed the importance of local artifacts, there has been a lack of in-depth discussion regarding the selection of locations and their effective utilization. Besides, the traditional image augmentation methods that are widely used have limited improvements for forgery detection tasks and require more specialized augmentation methods specifically designed for forgery detection tasks. In this paper, this study proposes Local Artifacts Amplification for Deepfakes Augmentation, which amplifies the local artifacts on the forged faces. Furthermore, this study incorporates prior knowledge about similar facial features into the model. This means that within the facial regions defined in this work, forged features exhibit similar patterns. By aggregating the results from all facial regions, the study can enhance the overall performance of the model. The evaluation experiments conducted in this research, achieving an AUC of 93.40% and an Acc of 87.03% in the challenging WildDeepfake dataset, demonstrate a promising improvement in accuracy compared to traditional image augmentation methods and achieve superior performance on intra-dataset evaluation. The cross-dataset evaluation also showed that the method presented in this study has strong generalization abilities.

The Burden and Clinical Features of Neovascular Glaucoma in A Major Tertiary Care Center in China.

PRCIS: This research presents the burden and clinical characteristics of NVG in Zhongshan Ophthalmic Center, employing the most extensive sample size and the longest uninterrupted temporal duration so far, which may provide a theoretical reference for the effective prevention and diagnosis of NVG. PURPOSE: To summarize the burden and clinical characteristics of neovascular glaucoma (NVG) in a major tertiary care center in China. METHODS: The clinical data of NVG patients in Zhongshan Ophthalmic Center (ZOC) between 2012 and 2021 were collected retrospectively, including their age, sex, affected eye, best-corrected visual acuity (BCVA), intraocular pressure (IOP), clinical stage and aetiology. RESULTS: In this study, we included 1877 eyes of 1749 patients who developed NVG, with 2.01:1 ratio of male to female. Their mean age was 53.14±16.69 years and those aged 41-70 years (65.2%) were most affected. Monocular patients were more predominant in most of them (92.7%), while 7.3% were binocular and 1667 eyes (88.8%) were at the angle­closure stage. The BCVA and IOP were 2.42±0.70 logMAR and 38.6±12.2 mmHg, respectively. Over the decade, the number of NVG patients and the proportion of NVG patients among glaucoma patients showed an increasing trend, with annual percentage changes (APCs) of 9.1% (95% CI: 5.0-13.3%, P=0.001) and 4.8% (95% CI: 2.2-7.4%, P=0.003), respectively. The top three primary conditions were diabetic retinopathy (DR), retinal vein occlusion (RVO), and retinal detachment (RD). Moreover, the APCs for the constituent ratio of DR and RVO were 4.4% (95% CI: 0.5-8.4%, P=0.031) and ï¹£4.6% (95% CI: ï¹£8.4% to ï¹£0.7%, P=0.028), respectively. However, the first and second causes of NVG in minors (<18 years old) were Coat's disease and ocular tumours, followed by RD and RVO in third place. The top cause of NVG in patients aged 65 years and older was RVO. CONCLUSIONS: The burden of NVG is increasing, emphasizing the need to improve preventive strategies focusing on primary diseases such as DR, RVO, and RD, particularly the increasing proportion of DR cases and the previously underemphasized RD patients, while also highlighting the differences in primary diseases across different age groups.

GazeForensics: DeepFake detection via gaze-guided spatial inconsistency learning.

DeepFake detection is pivotal in personal privacy and public safety. With the iterative advancement of DeepFake techniques, high-quality forged videos and images are becoming increasingly deceptive. Prior research has seen numerous attempts by scholars to incorporate biometric features into the field of DeepFake detection. However, traditional biometric-based approaches tend to segregate biometric features from general ones and freeze the biometric feature extractor. These approaches resulted in the exclusion of valuable general features, potentially leading to a performance decline and, consequently, a failure to fully exploit the potential of biometric information in assisting DeepFake detection. Moreover, insufficient attention has been dedicated to scrutinizing gaze authenticity within the realm of DeepFake detection in recent years. In this paper, we introduce GazeForensics, an innovative DeepFake detection method that utilizes gaze representation obtained from a 3D gaze estimation model to regularize the corresponding representation within our DeepFake detection model, while concurrently integrating general features to further enhance the performance of our model. Experimental results demonstrate that our proposed GazeForensics method performs admirably in terms of performance and exhibits excellent interpretability.

Progression Patterns and Risk Factors of Axial Elongation in Young Adults With Nonpathologic High Myopia: Three-Year Large Longitudinal Cohort Follow-Up.

PURPOSE: To investigate the progression patterns and risk factors of axial elongation in young adults with nonpathologic high myopia. DESIGN: Prospective, clinical observational cohort study with 2- to 4-year follow-up. METHODS: A total of 1043 eyes of 563 participants (3515 medical records) aged 18 to 50 years with nonpathologic high myopia (axial length [AL] ≥ 26 mm; myopic maculopathy < diffuse chorioretinal atrophy; without posterior staphyloma) were included from 1546 participants (6318 medical records). Annual axial elongation was calculated via linear mixed-effect models. The associated risk factors of axial elongation were determined by ordinal logistic regression analysis, with generalized estimate equations for eliminating an interocular correlation bias. RESULTS: Based on 5359 times of AL measurements, the annual axial elongation of participants (mean [SD] age 31.39 [9.22] years) was 0.03 mm/year (95% confidence interval [CI], 0.03-0.04; P < .001) during a 30.23 (6.06) months' follow-up. Severe (>0.1 mm/year), moderate (0.05-0.09 mm/year), mild (0-0.049 mm/year), and nil (≤0 mm/year) elongation was observed in 122 (11.7%), 211 (20.2%), 417 (40.0%), and 293 (28.1%) eyes. The following risk factors were significantly associated with axial elongation: baseline AL ≥ 28 mm (odds ratio [OR], 4.23; 95% CI, 2.95-6.06; P < .001); age < 40 years (OR, 1.64; 95% CI, 1.18-2.28; P = .003); axial asymmetry (OR, 2.04; 95% CI, 1.26-3.29; P = .003), and women (OR, 1.52; 95% CI, 1.13-2.2.05; P = .006). Using antiglaucoma medications was a protective factor (OR, 0.46; 95% CI, 0.27-0.79; P = .005), which slowed 75% of axial elongation from 0.04 (0.06) to 0.01 (0.06) mm/y (P < .001). CONCLUSIONS: Axial elongation continued in young adults with nonpathologic myopia. Risk factors included longer baseline AL and axial asymmetry, younger age, and woman. Topical use of antiglaucoma medications may be useful to reduce ongoing axial elongation.

Inspector for Face Forgery Detection: Defending Against Adversarial Attacks From Coarse to Fine.

The emergence of face forgery has raised global concerns on social security, thereby facilitating the research on automatic forgery detection. Although current forgery detectors have demonstrated promising performance in determining authenticity, their susceptibility to adversarial perturbations remains insufficiently addressed. Given the nuanced discrepancies between real and fake instances are essential in forgery detection, previous defensive paradigms based on input processing and adversarial training tend to disrupt these discrepancies. For the detectors, the learning difficulty is thus increased, and the natural accuracy is dramatically decreased. To achieve adversarial defense without changing the instances as well as the detectors, a novel defensive paradigm called Inspector is designed specifically for face forgery detectors. Specifically, Inspector defends against adversarial attacks in a coarse-to-fine manner. In the coarse defense stage, adversarial instances with evident perturbations are directly identified and filtered out. Subsequently, in the fine defense stage, the threats from adversarial instances with imperceptible perturbations are further detected and eliminated. Experimental results across different types of face forgery datasets and detectors demonstrate that our method achieves state-of-the-art performances against various types of adversarial perturbations while better preserving natural accuracy. Code is available on https://github.com/xarryon/Inspector.

Spatial Pattern of Host Tree Size, Rather than of Host Tree Itself, Affects the Infection Likelihood of a Fungal Stem Disease.

The spatial pattern of diseased forest trees is a product of the spatial pattern of host trees and the disease itself. Previous studies have focused on describing the spatial pattern of diseased host trees, and it remains largely unknown whether an antecedent spatial pattern of host/nonhost trees affects the infection pattern of a disease and how large the effect sizes of the spatial pattern of host/nonhost trees and host size are. The results from trivariate random labeling showed that the antecedent pattern of the host ash tree, Fraxinus mandshurica, but not of nonhost tree species, impacted the infection pattern of a stem fungal disease caused by Inonotus hispidus. To investigate the effect size of the spatial pattern of ash trees, we employed the SADIE (Spatial Analysis by Distance IndicEs) aggregation index and clustering index as predictors in the GLMs. Globally, the spatial pattern (vi index) of ash trees did not affect the infection likelihood of the focal tree; however, the spatial pattern of DBH (diameter at breast height) of ash trees significantly affected the infection likelihood of the focal tree. We sampled a series of circular plots with different radii to investigate the spatial pattern effect of host size on the infection likelihood of the focal tree locally. The results showed that the location (patch/gap) of the DBH of the focal tree, rather than that of the focal tree itself, significantly affected its infection likelihood in most plots of the investigated sizes. A meta-analysis was employed to settle the discrepancy between plots of different sizes, which led to results consistent with those of global studies. The results from meta-regression showed that plot size had no significant effects.

Rectified Binary Network for Single-Image Super-Resolution.

Binary neural network (BNN) is an effective approach to reduce the memory usage and the computational complexity of full-precision convolutional neural networks (CNNs), which has been widely used in the field of deep learning. However, there are different properties between BNNs and real-valued models, making it difficult to draw on the experience of CNN composition to develop BNN. In this article, we study the application of binary network to the single-image super-resolution (SISR) task in which the network is trained for restoring original high-resolution (HR) images. Generally, the distribution of features in the network for SISR is more complex than those in recognition models for preserving the abundant image information, e.g., texture, color, and details. To enhance the representation ability of BNN, we explore a novel activation-rectified inference (ARI) module that achieves a more complete representation of features by combining observations from different quantitative perspectives. The activations are divided into several parts with different quantification intervals and are inferred independently. This allows the binary activations to retain more image detail and yield finer inference. In addition, we further propose an adaptive approximation estimator (AAE) for gradually learning the accurate gradient estimation interval in each layer to alleviate the optimization difficulty. Experiments conducted on several benchmarks show that our approach is able to learn a binary SISR model with superior performance over the state-of-the-art methods. The code will be released at https://github.com/jwxintt/Rectified-BSR.

Protecting Prostate Cancer Classification From Rectal Artifacts via Targeted Adversarial Training.

Magnetic resonance imaging (MRI)-based deep neural networks (DNN) have been widely developed to perform prostate cancer (PCa) classification. However, in real-world clinical situations, prostate MRIs can be easily impacted by rectal artifacts, which have been found to lead to incorrect PCa classification. Existing DNN-based methods typically do not consider the interference of rectal artifacts on PCa classification, and do not design specific strategy to address this problem. In this study, we proposed a novel Targeted adversarial training with Proprietary Adversarial Samples (TPAS) strategy to defend the PCa classification model against the influence of rectal artifacts. Specifically, based on clinical prior knowledge, we generated proprietary adversarial samples with rectal artifact-pattern adversarial noise, which can severely mislead PCa classification models optimized by the ordinary training strategy. We then jointly exploited the generated proprietary adversarial samples and original samples to train the models. To demonstrate the effectiveness of our strategy, we conducted analytical experiments on multiple PCa classification models. Compared with ordinary training strategy, TPAS can effectively improve the single- and multi-parametric PCa classification at patient, slice and lesion level, and bring substantial gains to recent advanced models. In conclusion, TPAS strategy can be identified as a valuable way to mitigate the influence of rectal artifacts on deep learning models for PCa classification.

GBSVM: An Efficient and Robust Support Vector Machine Framework via Granular-Ball Computing.

Granular-ball support vector machine (GBSVM) is a significant attempt to construct a classifier using the coarse-to-fine granularity of a granular ball as input, rather than a single data point. It is the first classifier whose input contains no points. However, the existing model has some errors, and its dual model has not been derived. As a result, the current algorithm cannot be implemented or applied. To address these problems, we fix the errors of the original model of the existing GBSVM and derive its dual model. Furthermore, a particle swarm optimization (PSO) algorithm is designed to solve the dual problem. The sequential minimal optimization (SMO) algorithm is also carefully designed to solve the dual problem. The latter is faster and more stable. The experimental results on the UCI benchmark datasets demonstrate that GBSVM is more robust and efficient. All codes have been released in the open source library available at: http://www.cquptshuyinxia.com/GBSVM.html or https://github.com/syxiaa/GBSVM.

Multi-modality Regional Alignment Network for Covid X-Ray Survival Prediction and Report Generation.

In response to the worldwide COVID-19 pandemic, advanced automated technologies have emerged as valuable tools to aid healthcare professionals in managing an increased workload by improving radiology report generation and prognostic analysis. This study proposes a Multi-modality Regional Alignment Network (MRANet), an explainable model for radiology report generation and survival prediction that focuses on high-risk regions. By learning spatial correlation in the detector, MRANet visually grounds region-specific descriptions, providing robust anatomical regions with a completion strategy. The visual features of each region are embedded using a novel survival attention mechanism, offering spatially and risk-aware features for sentence encoding while maintaining global coherence across tasks. A cross-domain LLMs-Alignment is employed to enhance the image-to-text transfer process, resulting in sentences rich with clinical detail and improved explainability for radiologists. Multi-center experiments validate the overall performance and each module's composition within the model, encouraging further advancements in radiology report generation research emphasizing clinical interpretation and trustworthiness in AI models applied to medical studies.

Semi-Supervised Learning With Heterogeneous Distribution Consistency for Visible Infrared Person Re-Identification.

Visible infrared person re-identification (VI-ReID) exposes considerable challenges because of the modality gaps between the person images captured by daytime visible cameras and nighttime infrared cameras. Several fully-supervised VI-ReID methods have improved the performance with extensive labeled heterogeneous images. However, the identity of the person is difficult to obtain in real-world situations, especially at night. Limited known identities and large modality discrepancies impede the effectiveness of the model to a great extent. In this paper, we propose a novel Semi-Supervised Learning framework with Heterogeneous Distribution Consistency (HDC-SSL) for VI-ReID. Specifically, through investigating the confidence distribution of heterogeneous images, we introduce a Gaussian Mixture Model-based Pseudo Labeling (GMM-PL) method, which adaptively adjusts different thresholds for each modality to label the identity. Moreover, to facilitate the representation learning of unutilized data whose prediction is lower than the threshold, Modality Consistency Regularization (MCR) is proposed to ensure the prediction consistency of the cross-modality pedestrian images and handle the modality variance. Extensive experiments with different label settings on two VI-ReID datasets demonstrate the effectiveness of our method. Particularly, HDC-SSL achieves competitive performance with state-of-the-art fully-supervised VI-ReID methods on RegDB dataset with only 1 visible label and 1 infrared label per class.

Effect of intraocular pressure reduction on progressive high myopia (PHM study): study protocol of a randomised controlled trial.

BACKGROUND: In adult patients with high myopia (HM), progressive axial elongation poses a significant risk for the development of subsequent ocular complications that may lead to visual impairment. Effective strategies to reduce or prevent further axial elongation in highly myopic adult patients have not been available so far. Recent studies suggested that medically lowering intraocular pressure (IOP) may reduce axial elongation. OBJECTIVE: This clinical randomised controlled trial (RCT) aims to evaluate the efficacy of medical IOP reduction in adult patients with progressive HM (PHM). TRIAL DESIGN: Single-centre, open-label, prospective RCT. METHODS: This RCT will recruit 152 participants with PHM at the Zhongshan Ophthalmic Center (ZOC). Randomised in a ratio of 1:1, participants will receive IOP-lowering eyedrops (intervention group) or will be followed without treatment (control group) for 12 months. Follow-up visits will be conducted at 1, 6 and 12 months after baseline. Only one eye per eligible participant will be included for analysis. The primary outcome is the change in axial length (AL) within the study period of 12 months. Secondary outcomes include the incidence and progression of visual field (VF) defects, changes in optic disc morphology and incidence and progression of myopic maculopathy. Difference in AL changes between the two groups will be analysed using linear regression analysis. For the secondary outcomes, a multifactor Poisson regression within a generalised linear model will be used to estimate the relative risk of progression in VF defects and myopic maculopathy, and the rate of thinning in retinal nerve fibre layer and ganglion cell-inner plexiform will be assessed through Kaplan-Meier curves and log-rank tests. ETHICS AND DISSEMINATION: Full ethics approval for this trial has been obtained from the Ethics Committee of ZOC, Sun Yat-sen University, China (ID: 2023KYPJ110). Results of this trial will be disseminated through peer-reviewed journals and conference presentations. TRIAL REGISTRATION NUMBER: NCT05850936.

Efficacy and Safety of Surgical Peripheral Iridectomy, Goniosynechialysis, and Goniotomy for Advanced Primary Angle Closure Glaucoma Without Cataract: 1-Year Results of a Multicenter Study.

PRCIS: The combination of surgical peripheral iridectomy, goniosynechialysis, and goniotomy is a safe and effective surgical approach for advanced primary angle closure glaucoma without cataract. PURPOSE: To evaluate the efficacy and safety of surgical peripheral iridectomy (SPI), goniosynechialysis (GSL), and goniotomy (GT) in advanced primary angle closure glaucoma (PACG) eyes without cataract. PATIENTS AND METHODS: A prospective multicenter observational study was performed for patients who underwent combined SPI, GSL, and GT for advanced PACG without cataract. Patients were assessed before and after the operation. Complete success was defined as achieving intraocular pressure (IOP) between 6 and 18 mm Hg with at least a 20% reduction compared with baseline, without the use of ocular hypotensive medications or reoperation. Qualified success adopted the same criteria but allowed medication use. Factors associated with surgical success were analyzed using logistic regression. RESULTS: A total of 61 eyes of 50 advanced PACGs were included. All participants completed 12 months of follow-up. Thirty-six eyes (59.0%) achieved complete success, and 56 eyes (91.8%) achieved qualified success. Preoperative and postsurgical at 12 months mean IOPs were 29.7±7.7 and 16.1±4.8 mm Hg, respectively. The average number of ocular hypotensive medications decreased from 1.9 to 0.9 over 12 months. The primary complications included IOP spike (n=9), hyphema (n=7), and shallow anterior chamber (n=3). Regression analysis indicated that older age (odds ratio [OR]=1.09; P =0.043) was positively associated with complete success, while a mixed angle closure mechanism (OR=0.17; P =0.036) reduced success rate. CONCLUSIONS: The combination of SPI, GSL, and GT is a safe and effective surgical approach for advanced PACG without cataract. It has great potential as a first-line treatment option for these patients.

Changes in monsoon precipitation in East Asia under a 2°C interglacial warming.

Past intervals of warming provide the unique opportunity to observe how the East Asia monsoon precipitation response happened in a warming world. However, the available evaluations are primarily limited to the last glacial-to-interglacial warming, which has fundamental differences from the current interglacial warming, particularly in changes in ice volume. Comparative paleoclimate studies of earlier warm interglacial periods can provide more realistic analogs. Here, we present high-resolution quantitative reconstructions of temperature and precipitation from north-central China over the past 800 thousand years. We found that the average precipitation increase, estimated by the interglacial data, was only around one-half of that estimated for the glacial-to-interglacial data, which is attributed to the amplification of climate change by ice volume variations. Analysis of the interglacial data suggests an increase in monsoon precipitation of ~100 mm for a warming level of 2°C on the Chinese Loess Plateau.

MGNR: A Multi-Granularity Neighbor Relationship and Its Application in KNN Classification and Clustering Methods.

In the real world, data distributions often exhibit multiple granularities. However, the majority of existing neighbor-based machine-learning methods rely on manually setting a single-granularity for neighbor relationships. These methods typically handle each data point using a single-granularity approach, which severely affects their accuracy and efficiency. This paper adopts a dual-pronged approach: it constructs a multi-granularity representation of the data using the granular-ball computing model, thereby boosting the algorithm's time efficiency. It leverages the multi-granularity representation of the data to create tailored, multi-granularity neighborhood relationships for different task scenarios, resulting in improved algorithmic accuracy. The experimental results convincingly demonstrate that the proposed multi-granularity neighbor relationship effectively enhances KNN classification and clustering methods. The source code has been publicly released and is now accessible on GitHub at https://github.com/xjnine/MGNR.

De-Biased Disentanglement Learning for Pulmonary Embolism Survival Prediction on Multimodal Data.

Health disparities among marginalized populations with lower socioeconomic status significantly impact the fairness and effectiveness of healthcare delivery. The increasing integration of artificial intelligence (AI) into healthcare presents an opportunity to address these inequalities, provided that AI models are free from bias. This paper aims to address the bias challenges by population disparities within healthcare systems, existing in the presentation of and development of algorithms, leading to inequitable medical implementation for conditions such as pulmonary embolism (PE) prognosis. In this study, we explore the diverse bias in healthcare systems, which highlights the demand for a holistic framework to reducing bias by complementary aggregation. By leveraging de-biasing deep survival prediction models, we propose a framework that disentangles identifiable information from images, text reports, and clinical variables to mitigate potential biases within multimodal datasets. Our study offers several advantages over traditional clinical-based survival prediction methods, including richer survival-related characteristics and bias-complementary predicted results. By improving the robustness of survival analysis through this framework, we aim to benefit patients, clinicians, and researchers by enhancing fairness and accuracy in healthcare AI systems.