Recent advancements in deep learning have shown significant potential in predicting breast cancer risk from mammograms. While leveraging longitudinal changes in mammograms is crucial for accurate risk prediction, exis...
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ISBN:
(数字)9798331520526
ISBN:
(纸本)9798331520533
Recent advancements in deep learning have shown significant potential in predicting breast cancer risk from mammograms. While leveraging longitudinal changes in mammograms is crucial for accurate risk prediction, existing models often face limitations in capturing these temporal relationships effectively, and they tend to be computationally intensive. To address these challenges, we introduce LongMambAttn, a novel architecture designed to handle variable-length, multitemporal mammograms, thereby enhancing breast cancer risk prediction accuracy. LongMambAttn models the temporal changes of mammograms taken over periods ranging from 1 to 8 years. In an internal case-control dataset of 590 patients, our model demonstrates superior performance in predicting future cancer incidence, surpassing methods that rely only on the most recent prior mammogram as well as other models that incorporate mammograms taken at varying time intervals. Our results show that incorporating longitudinal mammograms via LongMambAttn leads to improved predictive accuracy for breast cancer risk.
Future shocks from climate change impacts will likely overstretch current individual coping capacities. Integrated policy strategies could foster sustainable and resilient reactions of households and businesses by reb...
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Cryogenic electron microscopy (cryo-EM) has the potential to capture snapshots of proteins in motion and generate hypotheses linking conformational states to biological function. This potential has been increasingly r...
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Modern design of nuclear facilities represents unique challenges: enabling the design of complex advanced concepts, supporting geographically dispersed teams, and supporting first-of-a-kind system development. Errors ...
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In TFT-LCD (thin film transistor-liquid crystal display) manufacturing industry, achieving accurate defect detection is a critical and a complex task, which involves using optical inspection technology to capture imag...
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External disturbance and denial-of-service (DoS) attacks pose significant challenges to the quantized control of multi-agent systems (MAS). Most of the existing quantized control strategies primarily focus on the scal...
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Evaluating individual treatment effects (ITE) is challenging due to the lack of access to counterfactual outcomes, particularly when working with biased data. Recent efforts have focused on leveraging the generative c...
ISBN:
(纸本)9798331534202
Evaluating individual treatment effects (ITE) is challenging due to the lack of access to counterfactual outcomes, particularly when working with biased data. Recent efforts have focused on leveraging the generative capabilities of models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) for ITE estimation. However, few approaches effectively address the need for uncertainty quantification in these estimates. In this work, we introduce GANCQR, a GAN-based conformal prediction method that generates prediction intervals for ITE with reliable coverage. Numerical experiments on synthetic and semi-synthetic datasets demonstrate GANCQR's superiority in handling selection bias compared to state-of-the-art methods.
Background: Medicines use related challenges such as inadequate adherence, high levels of antimicrobial resistance and preventable adverse drug reactions have underscored the need to incorporate pharmaceutical service...
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Background: Medicines use related challenges such as inadequate adherence, high levels of antimicrobial resistance and preventable adverse drug reactions have underscored the need to incorporate pharmaceutical services to help achieve desired treatment outcomes, and protect patients from inappropriate use of medicines. This situation is further constrained by insufficient numbers of pharmaceutical personnel and inappropriate skill mix. Studies have addressed individual capacity building approaches of logistics, supply chain or disease specific interventions but few have documented those involving such pharmacy assistants/professionals, or health workers/professionals charged with improving access and provision of pharmaceutical services. We examined how different training modalities have been employed and adapted to meet country-specific context and needs by a global pharmaceutical systems strengthening program in collaboration with a country's Ministry of Health and local stakeholders. Methods: Structured, content analysis of training approaches from twelve selected countries and a survey among conveniently selected trainees in Bangladesh and Ethiopia. Results: Case-based learning, practice and feedback, and repetitive interventions such as post-training action plan, supportive supervision and mentoring approaches are effective, evidence-based training techniques. In Ethiopia and Bangladesh, over 94% of respondents indicated that they have improved or developed skills or competencies as a result of the program's training activities. Supportive supervision structures and mentorship have been institutionalized with appropriate management structures. National authorities have been sensitized to secure funding from domestic resources or from the global fund grants for post-training follow-up initiatives. The Pharmaceutical Leadership Development program is an effective, case-based training modality that motivates staff to develop quality-improvement interventions and s
Searching reads from unknown origins in a reference database and finding evolutionarily similar genomes is central to many applications. Quantifying the similarity by estimating the distance between each read and matc...
Thrust stands are essential tools for evaluating the performance of propulsion systems, including Hall effect thrusters, ion thrusters, and pulsed plasma thrusters (PPTs). Our thrust stand supports a prototype weighin...
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