PneumoLLM: Harnessing the power of large language model for pneumoconiosis diagnosis.

The conventional pretraining-and-finetuning paradigm, while effective for common diseases with ample data, faces challenges in diagnosing data-scarce occupational diseases like pneumoconiosis. Recently, large language models (LLMs) have exhibits unprecedented ability when conducting multiple tasks in dialogue, bringing opportunities to diagnosis. A common strategy might involve using adapter layers for vision-language alignment and diagnosis in a dialogic manner. Yet, this approach often requires optimization of extensive learnable parameters in the text branch and the dialogue head, potentially diminishing the LLMs' efficacy, especially with limited training data. In our work, we innovate by eliminating the text branch and substituting the dialogue head with a classification head. This approach presents a more effective method for harnessing LLMs in diagnosis with fewer learnable parameters. Furthermore, to balance the retention of detailed image information with progression towards accurate diagnosis, we introduce the contextual multi-token engine. This engine is specialized in adaptively generating diagnostic tokens. Additionally, we propose the information emitter module, which unidirectionally emits information from image tokens to diagnosis tokens. Comprehensive experiments validate the superiority of our methods.

U2PNet: An Unsupervised Underwater Image-Restoration Network Using Polarization.

This article presents U2PNet, a novel unsupervised underwater image restoration network using polarization for improving signal-to-noise ratio and image quality in underwater imaging environments. Traditional methods for underwater image restoration using polarization require specific cues or pairs of underwater polarization datasets, which limit their practical applications. Our proposed method requires only one mosaicked polarized image of the scene and does not require datasets for pretraining or specific cues. We design two subnetworks (T-net and B ∞ -net) to accurately estimate the transmission map and background light, and unique nonreference loss functions to ensure effective restoration. Our experiments are based on an indoor polarization simulated dataset and a real polarization image dataset constructed from our underwater robotic platform equipped with polarization cameras. Experiment results demonstrate that our proposed method achieves state-of-the-art performance on both simulated and real underwater polarization images. The code and datasets will be available at https://github.com/polwork/U-2Pnet.

Healthcare professionals' perspectives on the challenges with managing polycystic ovary syndrome: A systematic review and meta-synthesis.

OBJECTIVE: To provide an overview of healthcare professionals' experience of PCOS management and identify the relevant facilitators and barriers. METHODS: A systematic search was conducted in MEDLINE, EMBASE, CINAHL, Web of Science, and Cochrane CENTRAL database from the earliest available date to April 2023. Qualitative and mixed methods studies that described healthcare professionals' experiences of PCOS management were included. RESULTS: A total of 74 findings were extracted from the 8 included studies, which were categorized into facilitators and barriers. The barriers were meta-aggregated into four themes: the weakness of clinical evidence; women's low adherence to PCOS management; various obstacles that healthcare professionals face, and the influence of social environment and culture. The facilitators were meta-aggregated into three themes: chronic disease healthcare plan, communication techniques and healthcare professionals' ability and awareness. CONCLUSION: The findings of this study have the potential to improve the care provided to women with PCOS. However, it is important for national health professionals and policy markers to consider the cultural context of their own country when implementing these findings. PRACTICAL IMPLICATIONS: This study illustrated several challenges in managing the heterogeneous condition of PCOS and provide insights for the development of medical policies and future research directions.

Temporal Action Localization in the Deep Learning Era: A Survey.

The temporal action localization research aims to discover action instances from untrimmed videos, representing a fundamental step in the field of intelligent video understanding. With the advent of deep learning, backbone networks have been instrumental in providing representative spatiotemporal features, while the end-to-end learning paradigm has enabled the development of high-quality models through data-driven training. Both supervised and weakly supervised learning approaches have contributed to the rapid progress of temporal action localization, resulting in a multitude of methods and a large body of literature, making a comprehensive survey a pressing necessity. This paper presents a thorough analysis of existing action localization works, offering a well-organized taxonomy that highlights the strengths and weaknesses of each strategy. In the realm of supervised learning, in addition to the anchor mechanism, we introduce a novel classification mechanism to categorize and summarize existing works. Similarly, for weakly supervised learning, we extend the traditional pre-classification and post-classification mechanisms by providing a fresh perspective on enhancement strategies. Furthermore, we shed light on the bottleneck of confidence estimation, a critical yet overlooked aspect of current works. By conducting detailed analyses, this survey serves as a valuable resource for researchers, providing beneficial guidance to newcomers and inspiring seasoned researchers alike.

Factors associated with discontinuation in fertility treatment: a systematic scoping review.

PURPOSE: The discontinuation of fertility treatment could decrease the chances of achieving parenthood for infertile patients and often leads to economic loss and medical resource waste. However, the evidence on the factors associated with discontinuation is unclear and inconsistent in the context of fertility treatment. This scoping review aimed to summarize the evidence on factors associated with discontinuation in fertility treatment, identify the current knowledge gap, and generate recommendations for future research. METHODS: We searched PubMed, Embase, The Cochrane Library, Web of Science, CINAHL, American Psychological Association, and http://clinicaltrials.gov from inception to June 2023 without language or time restrictions. We also searched the grey literature in Open Grey and Google Scholar and hand-searched the reference lists of relevant studies to identify potentially eligible studies. Publications that studied factors associated with discontinuation in fertility treatment were included. The identified factors were mapped to the World Health Organization's treatment adherence model. RESULTS: Thirty-seven articles involving 41,973 infertile patients from 13 countries were included in this scoping review. All studies identified the factors from the perspective of patients, except for one that described the factors from the healthcare providers' perspective. A total of 42 factors were identified, with most of them belonging to the patient-related dimension, followed by socio-economic-related, treatment-related, condition-related, and healthcare system-related dimensions. Female education level, social support, and insurance coverage decreased the likelihood of treatment discontinuation, whereas multiparous women, male infertility, depression, higher infertility duration, and treatment duration increased the likelihood of treatment discontinuation. Age, education level, and ethnicity are the commonly nonmodifiable factors for treatment discontinuation, while insurance coverage, depression, and anxiety symptoms are among some of the more commonly reported modifiable factors. CONCLUSION: This is the first scoping review examining and synthesizing evidence on the factors influencing of discontinuation in fertility treatment. This review could inform researchers, clinicians, and policymakers to address modifiable barriers and facilitators to develop personalized and multicomponent interventions that could improve the discontinuation in fertility treatment.

Joint Denoising-Demosaicking Network for Long-Wave Infrared Division-of-Focal-Plane Polarization Images With Mixed Noise Level Estimation.

Denoising and demosaicking long-wave infrared (LWIR) division-of-focal-plane (DoFP) polarization images are crucial for various vision applications. However, existing methods rely on the sequential application of individual denoising and demosaicking processes, which may result in the accumulation of errors produced by each process. To address this issue, we propose a joint denoising and demosaicking method for LWIR DoFP images based on a three-stage progressive deep convolutional neural network. To ensure the generalization ability of this network, it is essential to have adequate training data that closely resembles real data. Therefore, we model the complex noise sources that affect LWIR DoFP images as mixed Poisson-Additive-Stripe noise and construct a least-squares problem based on the polarization measurement redundancy error to estimate the parameters of this model on real images. Subsequently, the estimated noise parameters are used to generate training data that enables the network to learn accurate polarization image statistics and improve its generalization ability. The experimental results demonstrate the effectiveness of the proposed method in enhancing the image restoration performance on real LWIR DoFP polarization data.

Early mobilization implementation for critical ill patients: A cross-sectional multi-center survey about knowledge, attitudes, and perceptions of critical care nurses.

OBJECTIVE: To explore critical care clinicians' knowledge, attitudes and perceptions toward early mobilization of critically ill patients in ICUs. DESIGN: A cross-sectional national survey was conducted. From January to August 2020, ICU nurses in 11 hospitals were surveyed by using a questionnaire on the knowledge, attitudes and perceptions of ICU early mobilization. RESULTS: Totally 512 nurses completed the questionnaire. The respondents' mean score for knowledge of early mobilization was 6.89 ± 2.91. The level of knowledge was good in 2.5% (13/512), fair in 52.3% (268/512). The attitudes toward early mobilization were positive in 31.4% (161/512). In terms of perceived implementation of ICU early mobilization, 42.9% (220/512) of nurses did not believe that this should be a top priority in intensive care. The attitudes of nurses from different ICUs were significantly different (F = 3.58, P < 0.05). The knowledge (7.34 ± 2.78 vs. 6.49 ± 2.97, t = 3.37, P < 0.001) and attitudes (3.82 ± 0.58 vs. 3.52 ± 0.56, t = 5.63, P < 0.001) of nurses who had early mobilization related training were higher than those of nurses who had no training. CONCLUSIONS: The importance of early ICU early mobilization is increasingly recognized by critical care providers. However, there is still a gap in the knowledge, attitudes and perceptions of ICU early mobilization among nurses. In future studies, it is necessary to further systematically identify the reasons leading to the gaps in these aspects and implement targeted interventions around these gaps. Meanwhile, more nurses should be encouraged to participate in decision-making to ensure the efficient and quality implementation of ICU early mobilization practices.