Research on aluminum defect classification algorithm based on deep learning with attention mechanism

Zhang, Wen; Sun, Shanhui; Yang, Huanjing

doi:10.54097/fcis.v2i1.3173

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…This approach to semantic analysis could be adapted to improve the extraction and interpretation of medical temporal phrases by enhancing the model's ability to understand and categorize the semantic contexts of these phrases. Zhang et al [13] focused on optimizing deep learning algorithms for medical image processing, highlighting the importance of performance evaluation in models designed for the medical field. Their emphasis on optimization and evaluation is crucial as it aligns with our need to continuously refine our architecture to handle the nuances of medical language in clinical texts effectively.…”

Section: Related Work and Methodologymentioning

confidence: 99%

Innovations in Time Related Expression Recognition Using LSTM Networks

Zhan,

Ma,

Gao

et al. 2024

IJIRCST

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The proposed architecture leverages the strengths of both Convolutional Neural Network (CNN) and Bidirectional Long Short-Term (BLSTM) to create a robust model for temporal expression recognition in clinical texts. The CNN component effectively captures morphological and orthographic features at the character level, which enriches the semantic understanding of complex medical terminologies that are often abbreviated or have unique suffixes and prefixes. The BLSTM component excels in capturing long-range dependencies in text, which is crucial for understanding the context in which temporal expressions occur. By integrating these models with a CRF layer, the system not only predicts discrete labels but also ensures that the sequence of predicted labels is coherent and contextually appropriate, addressing the limitations of models that predict labels independently. The integration of pre-trained biomedical word vectors provides significant contextual grounding tailored to the medical domain, enhancing the model's ability to discern and interpret the nuances of medical language. This is crucial in clinical contexts where accurate interpretation of temporal phrases can be critical for patient management and treatment timelines. Further, experiments conducted on the dataset validate the effectiveness of the proposed model, demonstrating a notable improvement over traditional methods that rely heavily on hand-crafted features and rule-based approaches. Future work could explore the adaptability of this model to other subdomains of the medical field and its efficacy in processing multilingual texts, potentially increasing its applicability in global healthcare settings, with further refinement of the neural architecture and optimization of training strategies potentially yielding even better performance and faster processing times essential for real-time clinical decision support systems.

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Section: Related Work and Methodologymentioning

confidence: 99%

Innovations in Time Related Expression Recognition Using LSTM Networks

Zhan,

Ma,

Gao

et al. 2024

IJIRCST

View full text Add to dashboard Cite

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“…The increase of multimodal data, which integrates disparate data formats such as text, image, or audio, requires developing sophisticated computational techniques to process and integrate these heterogeneous data types [1][2][3]. This integration, known as multimodal data fusion, leverages mainly deep learning techniques [4,5] and is critical for building systems that can interpret complex data in a manner akin to human cognition, thereby enhancing decision-making processes in clinical applications [6][7][8][9][10][11][12][13]: with fundus photos [14], Chest X-rays [15], or even public health applications using remote sensing techniques [16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Deep Learning for Low-Resource Settings: A Vector Embedding Alignment Approach for Healthcare Applications

Restrepo,

Wu,

Cajas

et al. 2024

Preprint

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ObjectiveLarge-scale multi-modal deep learning models and datasets have revolutionized various domains such as healthcare, underscoring the critical role of computational power. However, in resource-constrained regions like Low and Middle-Income Countries (LMICs), GPU and data access is limited, leaving many dependent solely on CPUs. To address this, we advocate leveraging vector embeddings for flexible and efficient computational methodologies, aiming to democratize multimodal deep learning across diverse contexts.Background and SignificanceOur paper investigates the computational efficiency and effectiveness of leveraging vector embeddings, extracted from single-modal foundation models and multi-modal Vision-Language Models (VLM), for multimodal deep learning in low-resource environments, particularly in health-care applications. Additionally, we propose an easy but effective inference-time method to enhance performance by further aligning image-text embeddings.Materials and MethodsBy comparing these approaches with traditional multimodal deep learning methods, we assess their impact on computational efficiency and model performance using accuracy, F1-score, inference time, training time, and memory usage across 3 medical modalities such as BRSET (ophthalmology), HAM10000 (dermatology), and SatelliteBench (public health).ResultsOur findings indicate that embeddings reduce computational demands without compromising the model’s performance, and show that our embedding alignment method improves the performance of the models in medical tasks.DiscussionThis research contributes to sustainable AI practices by optimizing computational resources in resource-constrained environments. It highlights the potential of embedding-based approaches for efficient multimodal learning.ConclusionVector embeddings democratize multimodal deep learning in LMICs, especially in healthcare. Our study showcases their effectiveness, enhancing AI adaptability in varied use cases.

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