Adversarial Masked Image Inpainting for Robust Detection of Mpox and Non-Mpox

Due to the lack of efficient mpox diagnostic technology, mpox cases continue to increase. Recently, the great potential of deep learning models in detecting mpox and non-mpox has been proven. However, existing models learn image representations via image classification, which results in they may be easily susceptible to interference from real-world noise, require diverse non-mpox images, and fail to detect abnormal input. These drawbacks make classification models inapplicable in real-world settings. To address these challenges, we propose "Mask, Inpainting, and Measure" (MIM). In MIM's pipeline, a generative adversarial network only learns mpox image representations by inpainting the masked mpox images. Then, MIM determines whether the input belongs to mpox by measuring the similarity between the inpainted image and the original image. The underlying intuition is that since MIM solely models mpox images, it struggles to accurately inpaint non-mpox images in real-world settings. Without utilizing any non-mpox images, MIM cleverly detects mpox and non-mpox and can handle abnormal inputs. We used the recognized mpox dataset (MSLD) and images of eighteen non-mpox skin diseases to verify the effectiveness and robustness of MIM. Experimental results show that the average AUROC of MIM achieves 0.8237. In addition, we demonstrated the drawbacks of classification models and buttressed the potential of MIM through clinical validation. Finally, we developed an online smartphone app to provide free testing to the public in affected areas. This work first employs generative models to improve mpox detection and provides new insights into binary decision-making tasks in medical images

US-SFNet: A Spatial-Frequency Domain-based Multi-branch Network for Cervical Lymph Node Lesions Diagnoses in Ultrasound Images

Ultrasound imaging serves as a pivotal tool for diagnosing cervical lymph node lesions. However, the diagnoses of these images largely hinge on the expertise of medical practitioners, rendering the process susceptible to misdiagnoses. Although rapidly developing deep learning has substantially improved the diagnoses of diverse ultrasound images, there remains a conspicuous research gap concerning cervical lymph nodes. The objective of our work is to accurately diagnose cervical lymph node lesions by leveraging a deep learning model. To this end, we first collected 3392 images containing normal lymph nodes, benign lymph node lesions, malignant primary lymph node lesions, and malignant metastatic lymph node lesions. Given that ultrasound images are generated by the reflection and scattering of sound waves across varied bodily tissues, we proposed the Conv-FFT Block. It integrates convolutional operations with the fast Fourier transform to more astutely model the images. Building upon this foundation, we designed a novel architecture, named US-SFNet. This architecture not only discerns variances in ultrasound images from the spatial domain but also adeptly captures microstructural alterations across various lesions in the frequency domain. To ascertain the potential of US-SFNet, we benchmarked it against 12 popular architectures through five-fold cross-validation. The results show that US-SFNet is SOTA and can achieve 92.89% accuracy, 90.46% precision, 89.95% sensitivity and 97.49% specificity, respectively

U-SEANNet: A Simple, Efficient and Applied U-Shaped Network for Diagnosis of Nasal Diseases on Nasal Endoscopic Images

Numerous studies have affirmed that deep learning models can facilitate early diagnosis of lesions in endoscopic images. However, the lack of available datasets stymies advancements in research on nasal endoscopy, and existing models fail to strike a good trade-off between model diagnosis performance, model complexity and parameters size, rendering them unsuitable for real-world application. To bridge these gaps, we created the first large-scale nasal endoscopy dataset, named 7-NasalEID, comprising 11,352 images that contain six common nasal diseases and normal samples. Subsequently, we proposed U-SEANNet, an innovative U-shaped architecture, underpinned by depth-wise separable convolution. Moreover, to enhance its capacity for detecting nuanced discrepancies in input images, U-SEANNet employs the Global-Local Channel Feature Fusion module, enabling it to utilize salient channel features from both global and local contexts. To demonstrate U-SEANNet's potential, we benchmarked U-SEANNet against seventeen modern architectures through five-fold cross-validation. The experimental results show that U-SEANNet achieves a commendable accuracy of 93.58%. Notably, U-SEANNet's parameters size and GFLOPs are only 0.78M and 0.21, respectively. Our findings suggest U-SEANNet is the state-of-the-art model for nasal diseases diagnosis in endoscopic images.Comment: This manuscript has been submitted to ICASSP 202

Can ischemic stroke patients with mTICI of 2b achieve similar outcomes compared to those with complete recanalization following endovascular therapy?

Background and purposeEndovascular therapy (EVT) has been used as a standard treatment method for patients with large vessel ischemic stroke within 24 h of the onset. The extent of recanalization after EVT can be assessed using the modified thrombolysis in cerebral infarction (mTICI) scale as an accepted angiographic grading system. In this study, we aimed to investigate whether patients with a mTICI grade of 2b achieve similar outcomes compared to those with complete recanalization (mTICI of 3) following EVT for acute ischemic stroke.MethodsA retrospective analysis was conducted on 196 consecutive patients who underwent EVT in a comprehensive stroke center. In the final study, 176 patients were included based on the inclusion criteria. The primary outcome was the 3-month modified Rankin Scale (mRS) of 0–2 considered as a favorable outcome, while excellent outcomes were defined as mRS scores of 0–1.ResultsOur data showed that 59.46% of patients in the mTICI 2b group achieved a favorable outcome, comparable to 58.99% observed in the mTICI 3 group (p = 0.959). Additionally, 54.05% (n = 37) of patients with mTICI 2b achieved an excellent outcome, compared to 51.80% (n = 139) in the mTICI 3 group (p = 0.807). The case fatality rates were also comparable between the groups, with 8.11% in the mTICI 2b group and 10.79% in the mTICI 3 group (p = 0.632). Overall, there were no statistically significant differences between the two groups in terms of 3-month favorable outcomes, excellent outcomes, or mortality.ConclusionSimilar 3-month outcomes can be achieved for ischemic stroke patients undergoing EVT with a mTICI grade of 2b compared to those with a mTICI grade of 3. These data can help clinicians in setting realistic expectations and making informed decisions during EVT procedures

Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere

Due to the lack of more efficient diagnostic tools for monkeypox, its spread remains unchecked, presenting a formidable challenge to global health. While the high efficacy of deep learning models for monkeypox diagnosis has been demonstrated in related studies, the overlook of inference speed, the parameter size and diagnosis performance for early-stage monkeypox renders the models inapplicable in real-world settings. To address these challenges, we proposed an ultrafast and ultralight network named Fast-MpoxNet. Fast-MpoxNet possesses only 0.27M parameters and can process input images at 68 frames per second (FPS) on the CPU. To counteract the diagnostic performance limitation brought about by the small model capacity, it integrates the attention-based feature fusion module and the multiple auxiliary losses enhancement strategy for better detecting subtle image changes and optimizing weights. Using transfer learning and five-fold cross-validation, Fast-MpoxNet achieves 94.26% Accuracy on the Mpox dataset. Notably, its recall for early-stage monkeypox achieves 93.65%. By adopting data augmentation, our model's Accuracy rises to 98.40% and attains a Practicality Score (A new metric for measuring model practicality in real-time diagnosis application) of 0.80. We also developed an application system named Mpox-AISM V2 for both personal computers and mobile phones. Mpox-AISM V2 features ultrafast responses, offline functionality, and easy deployment, enabling accurate and real-time diagnosis for both the public and individuals in various real-world settings, especially in populous settings during the outbreak. Our work could potentially mitigate future monkeypox outbreak and illuminate a fresh paradigm for developing real-time diagnostic tools in the healthcare field

MpoxMamba: A Grouped Mamba-based Lightweight Hybrid Network for Mpox Detection

Due to the lack of effective mpox detection tools, the mpox virus continues to spread worldwide and has once again been declared a public health emergency of international concern by the World Health Organization. Lightweight deep learning model-based detection systems are crucial to alleviate mpox outbreaks since they are suitable for widespread deployment, especially in resource-limited scenarios. However, the key to its successful application depends on ensuring that the model can effectively model local features and long-range dependencies in mpox lesions while maintaining lightweight. Inspired by the success of Mamba in modeling long-range dependencies and its linear complexity, we proposed a lightweight hybrid architecture called MpoxMamba for efficient mpox detection. MpoxMamba utilizes depth-wise separable convolutions to extract local feature representations in mpox skin lesions and greatly enhances the model\u27s ability to model the global contextual information by grouped Mamba modules. Notably, MpoxMamba\u27s parameter size and FLOPs are 0.77M and 0.53G, respectively. Experimental results on two widely recognized benchmark datasets demonstrate that MpoxMamba outperforms state-of-the-art lightweight models and existing mpox detection methods. Importantly, we developed a web-based online application to provide free mpox detection (http://5227i971s5.goho.co:30290). The source codes of MpoxMamba are available at https://github.com/YubiaoYue/MpoxMamba

Ultrafast and Ultralight Network-Based Intelligent System for Real-time Diagnosis of Ear diseases in Any Devices

Traditional ear disease diagnosis heavily depends on experienced specialists and specialized equipment, frequently resulting in misdiagnoses, treatment delays, and financial burdens for some patients. Utilizing deep learning models for efficient ear disease diagnosis has proven effective and affordable. However, existing research overlooked model inference speed and parameter size required for deployment. To tackle these challenges, we constructed a large-scale dataset comprising eight ear disease categories and normal ear canal samples from two hospitals. Inspired by ShuffleNetV2, we developed Best-EarNet, an ultrafast and ultralight network enabling real-time ear disease diagnosis. Best-EarNet incorporates the novel Local-Global Spatial Feature Fusion Module which can capture global and local spatial information simultaneously and guide the network to focus on crucial regions within feature maps at various levels, mitigating low accuracy issues. Moreover, our network uses multiple auxiliary classification heads for efficient parameter optimization. With 0.77M parameters, Best-EarNet achieves an average frames per second of 80 on CPU. Employing transfer learning and five-fold cross-validation with 22,581 images from Hospital-1, the model achieves an impressive 95.23% accuracy. External testing on 1,652 images from Hospital-2 validates its performance, yielding 92.14% accuracy. Compared to state-of-the-art networks, Best-EarNet establishes a new state-of-the-art (SOTA) in practical applications. Most importantly, we developed an intelligent diagnosis system called Ear Keeper, which can be deployed on common electronic devices. By manipulating a compact electronic otoscope, users can perform comprehensive scanning and diagnosis of the ear canal using real-time video. This study provides a novel paradigm for ear endoscopy and other medical endoscopic image recognition applications.Comment: This manuscript has been submitted to Neural Network

Predictive outcome of late window ischemic stroke patients following endovascular therapy: a multi-center study

Background and purposeStroke is a leading cause of morbidity and mortality worldwide. Endovascular therapy (EVT) has been established as a gold standard option to treat acute ischemic stroke (AIS) patients with large vessel occlusion (LVO) presenting within 6 h of symptom onset. However, there is a paucity of information regarding patient outcome and mortality in patients presenting in late time window within 6 to 24 h. In this study, we aimed to assess for predictors of outcomes in late window stroke patients following EVT.MethodsWe analyzed data from 202 patients treated with EVT from four comprehensive stroke centers. All patients were above 18 years of age and had symptoms onset of 6–24 h. mRS of 0–2 after three months was defined as favorable outcome.ResultsPatients with favorable outcome had lower median age (p = 0.003), lower pre-EVT National Institute of Health Stroke Scale (NIHSS) score (p = 0.000), lower diabetes mellitus (p = 0.041), stroke history (p = 0.041), parenchymal hematoma (PH) (p = 0.000) and fewer attempts to achieve successful recanalization (p = 0.001). Multivariate regression analysis found age (p = 0.007), diabetes mellitus (p = 0.022), successful recanalization (mTICI≥2b) (p = 0.006), NIHSS at onset (p = 0.000), and PH1 + PH2 Heidelberg bleeding classification (p = 0.009) as predictors of functional outcome.ConclusionAge, diabetes mellitus history, baseline NIHSS score, successful recanalization, and PH are predictors of 90-day functional outcome of late-window ischemic stroke patients undergoing EVT

Reducing door-to-wire time for ST-elevation myocardial infarction patients undergoing primary percutaneous coronary intervention by multidisciplinary collaboration: An observational study

The aim of this study is to reduce door-to-wire time for ST-elevation myocardial infarction patients undergoing primary percutaneous coronary intervention through multidisciplinary collaboration. Patients over the age of 18who visited the Foshan Sanshui District People's Hospital between 2018 and 2019 and were diagnosed with STEMI were included in this study. Analyses were performed with patients segregated into a pre-intervention interim period (2018) and a post-intervention period (2019) based on the date of admission. Intervention measures for reducing door to wire time were fully implemented towards the end of the interim period. There were no significant differences in the baseline characteristics of the 2 groups. Median door to puncture time was reduced from 57.5 minutes in the interim period to 46.0 minutes (P < .001) in the post-intervention period. Similarly, median door to wire time was shortened from 88.0 minutes to 63.5 minutes (P < .001). During the interim period, 24% of patients had a door to wire time of <60 minutes, compared to 40.67% of patients in the post-intervention period (P = .002). Multidisciplinary collaboration is an important strategy to reduce door to wire time for patients with STEMI, and may be implemented in suitable centers to improve patient care

Out-of-distribution Detection in Medical Image Analysis: A survey

Computer-aided diagnostics has benefited from the development of deep learning-based computer vision techniques in these years. Traditional supervised deep learning methods assume that the test sample is drawn from the identical distribution as the training data. However, it is possible to encounter out-of-distribution samples in real-world clinical scenarios, which may cause silent failure in deep learning-based medical image analysis tasks. Recently, research has explored various out-of-distribution (OOD) detection situations and techniques to enable a trustworthy medical AI system. In this survey, we systematically review the recent advances in OOD detection in medical image analysis. We first explore several factors that may cause a distributional shift when using a deep-learning-based model in clinic scenarios, with three different types of distributional shift well defined on top of these factors. Then a framework is suggested to categorize and feature existing solutions, while the previous studies are reviewed based on the methodology taxonomy. Our discussion also includes evaluation protocols and metrics, as well as the challenge and a research direction lack of exploration.23 pages, 3 figure