Federated Few-shot Learning for Cough Classification with Edge Devices

Automatically classifying cough sounds is one of the most critical tasks for the diagnosis and treatment of respiratory diseases. However, collecting a huge amount of labeled cough dataset is challenging mainly due to high laborious expenses, data scarcity, and privacy concerns. In this work, our aim is to develop a framework that can effectively perform cough classification even in situations when enormous cough data is not available, while also addressing privacy concerns. Specifically, we formulate a new problem to tackle these challenges and adopt few-shot learning and federated learning to design a novel framework, termed F2LCough, for solving the newly formulated problem. We illustrate the superiority of our method compared with other approaches on COVID-19 Thermal Face & Cough dataset, in which F2LCough achieves an average F1-Score of 86%. Our results show the feasibility of few-shot learning combined with federated learning to build a classification model of cough sounds. This new methodology is able to classify cough sounds in data-scarce situations and maintain privacy properties. The outcomes of this work can be a fundamental framework for building support systems for the detection and diagnosis of cough-related diseases.Comment: 21 pages, 5 figure

A Multiple Choices Reading Comprehension Corpus for Vietnamese Language Education

Machine reading comprehension has been an interesting and challenging task in recent years, with the purpose of extracting useful information from texts. To attain the computer ability to understand the reading text and answer relevant information, we introduce ViMMRC 2.0 - an extension of the previous ViMMRC for the task of multiple-choice reading comprehension in Vietnamese Textbooks which contain the reading articles for students from Grade 1 to Grade 12. This dataset has 699 reading passages which are prose and poems, and 5,273 questions. The questions in the new dataset are not fixed with four options as in the previous version. Moreover, the difficulty of questions is increased, which challenges the models to find the correct choice. The computer must understand the whole context of the reading passage, the question, and the content of each choice to extract the right answers. Hence, we propose the multi-stage approach that combines the multi-step attention network (MAN) with the natural language inference (NLI) task to enhance the performance of the reading comprehension model. Then, we compare the proposed methodology with the baseline BERTology models on the new dataset and the ViMMRC 1.0. Our multi-stage models achieved 58.81% by Accuracy on the test set, which is 5.34% better than the highest BERTology models. From the results of the error analysis, we found the challenge of the reading comprehension models is understanding the implicit context in texts and linking them together in order to find the correct answers. Finally, we hope our new dataset will motivate further research in enhancing the language understanding ability of computers in the Vietnamese language

On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation

Constructing a robust model that can effectively generalize to test samples under distribution shifts remains a significant challenge in the field of medical imaging. The foundational models for vision and language, pre-trained on extensive sets of natural image and text data, have emerged as a promising approach. It showcases impressive learning abilities across different tasks with the need for only a limited amount of annotated samples. While numerous techniques have focused on developing better fine-tuning strategies to adapt these models for specific domains, we instead examine their robustness to domain shifts in the medical image segmentation task. To this end, we compare the generalization performance to unseen domains of various pre-trained models after being fine-tuned on the same in-distribution dataset and show that foundation-based models enjoy better robustness than other architectures. From here, we further developed a new Bayesian uncertainty estimation for frozen models and used them as an indicator to characterize the model's performance on out-of-distribution (OOD) data, proving particularly beneficial for real-world applications. Our experiments not only reveal the limitations of current indicators like accuracy on the line or agreement on the line commonly used in natural image applications but also emphasize the promise of the introduced Bayesian uncertainty. Specifically, lower uncertainty predictions usually tend to higher out-of-distribution (OOD) performance.Comment: Advances in Neural Information Processing Systems (NeurIPS) 2023, Workshop on robustness of zero/few-shot learning in foundation model

When Intervention Becomes Imperative: A Case Report of Spontaneous Vulvar Edema During Pregnancy

Spontaneous idiopathic vulvar edema during the second trimester is a rare condition. The approach to managing this condition involves relieving symptoms, identifying underlying causes, and implementing appropriate treatment. Managing such cases during pregnancy is challenging because of concerns for potential adverse fetal outcomes. Conservative management expects the condition to be relieved spontaneously postpartum, whereas invasive treatment offers a more rapid resolution. Treatment choices are controversial because each method has its pros and cons and influences the delivery process to a certain extent. Surgical drainage becomes a viable option when patients are not responsive to medications. We report a case of spontaneous massive vulvar edema in a 22-year-old primigravida in her 23rd week of pregnancy. After ruling out other notable causes of vulvar edema, we decided to intervene using an invasive procedure because she complained of progressive symptoms and discomfort. Subsequently, the edema subsided postprocedure, and the patient experienced successful labor with no complications. This report aims to alert clinicians that drainage attempts should be considered in pregnant patients with worsening symptoms

Synthesis of p-type Mg2Si1-xSnx with x = 0-1 and optimization of the synthesis parameters

Mg2Si is a promising thermoelectric material in the mid-temperature region 500 – 800 K. Development of Mg2Si based thermoelectric generators requires both good n- and p-type materials. While the thermoelectric properties n-type Mg2(Si,Sn) materials are good, those of the corresponding p-type are not as much. Therefore, optimizing p-type solid solution of magnesium silicide and magnesium stannide is highly desired. We employ high energy ball milling for efficient synthesis of p-type Mg2(Si,Sn) and investigate the effect of milling time, sintering temperature, and holding time on the thermoelectric properties of p-type Mg2Si1-xSnx with x = 0-1. We can show the synthesis of p-type Mg2(Si,Sn) for the whole compositions using Li as a dopant. We have also studied the effect of the synthesis parameters (milling time, sintering temperature, and holding time) on the phase purity, functional homogeneity and thermoelectric properties. The phase purity increases with longer milling time. The functional homogeneity decreases with higher sintering temperature and longer holding time. The optimum synthesis condition for x = 0.6 leads to zTmax0.6 at 700 K, which is one of the highest value reported for p-type Mg2(Si,Sn)

Clinical Characteristics and Histopathology of Idiopathic Epiretinal Membrane in Vietnam

BACKGROUND: Idiopathic epiretinal membrane (iERM) is an avascular proliferation of different types of cells between the posterior vitreous cortex and the internal limiting membrane. That causes visual impairment including blurry, distortion, scotoma. Many studies of iERM were done to describe the clinical characteristics and investigate the histopathology of this disease. Nonetheless, there has not been a study of iERM histopathology in Vietnam. AIMS: To describe clinical characteristics and histopathological results of idiopathic retinal membrane and the association between them. METHODS: A cross sectional decriptive study of 35 iERMs (33 patients) in Vietnam National Institute of Ophthalmology (VNIO). RESULTS: High morbidity incidence was in group age >50 years (32/35), female gender (26/35), limited movement works (27/35), and high educational levels (28/35). Distortion was the highest (77.14%), scotoma and floater was less frequent (28.5%, 45.7%). Macular edema in all cases and PVD and exudate were high frequent (65.7%, 62.8%). Symptom duration was 8.2 ± 4.7 months, (1-21 months). Mean of central macular thickness was 468.51 ± 97.24 µm (656-274 µm). Six types of cell were detected, including glial cell (35/35), fibroblast (23/35), myofibroblast (23/35), macrophage (13/35), lymphocyte (5/35) and neutrophil (2/35). The number of cell types in one sample ranged from 1-5 types (2.85 ± 1.28 cell types). Number of cell types were correlated to symptom duration (r = 0.47, p = 0.004, Pearson's test) and central macular thickness (r = 0.72, p < 0.001, Pearson's test). CONCLUSION: There were 6 types of cells in iERM. Glial cell was the most frequent cell, inflammatory cells (macrophage, lymphocyte, neutrophil) was also detected. The number of cell types was stastitically correlated to symptom duration and CMT