by jsendak | May 2, 2024 | Computer Science
arXiv:2405.00344v1 Announce Type: new
Abstract: A chest X-ray radiology report describes abnormal findings not only from X-ray obtained at current examination, but also findings on disease progression or change in device placement with reference to the X-ray from previous examination. Majority of the efforts on automatic generation of radiology report pertain to reporting the former, but not the latter, type of findings. To the best of the authors’ knowledge, there is only one work dedicated to generating summary of the latter findings, i.e., follow-up summary. In this study, we therefore propose a transformer-based framework to tackle this task. Motivated by our observations on the significance of medical lexicon on the fidelity of summary generation, we introduce two mechanisms to bestow expert insight to our model, namely expert soft guidance and masked entity modeling loss. The former mechanism employs a pretrained expert disease classifier to guide the presence level of specific abnormalities, while the latter directs the model’s attention toward medical lexicon. Extensive experiments were conducted to demonstrate that the performance of our model is competitive with or exceeds the state-of-the-art.
Analysis: Automatic Generation of Radiology Reports
The task of automatically generating radiology reports is an important area of research in the field of multimedia information systems. Most existing efforts in this domain focus on reporting abnormal findings from current X-ray examinations, but there is a lack of research on generating summaries that consider changes or progression in disease or device placement based on previous X-ray images. This article introduces a transformer-based framework to address this gap.
The multi-disciplinary nature of this research is evident as it combines concepts from several fields, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By leveraging transformer-based models, which have achieved remarkable success in natural language processing tasks, the authors aim to generate accurate and informative summaries of X-ray findings from different timepoints.
Expert Soft Guidance
One of the key contributions of this study is the use of expert soft guidance to improve the fidelity of summary generation. The authors employ a pretrained expert disease classifier to guide the model in determining the presence level of specific abnormalities. This mechanism leverages the knowledge and expertise of medical professionals to guide the automatic generation process, increasing the accuracy and relevance of the generated summaries.
Masked Entity Modeling Loss
To direct the model’s attention towards medical lexicon, the authors introduce a masked entity modeling loss. This mechanism helps the model understand and focus on the medical terminology and concepts that are crucial for generating meaningful and informative summaries. By incorporating this loss, the model becomes more adept at capturing the relevant medical information and producing accurate reports.
Implications and Future Directions
The proposed transformer-based framework shows promising results in generating follow-up summaries of abnormal findings in radiology reports. The incorporation of expert soft guidance and masked entity modeling loss enhances the model’s performance, making it competitive with or surpassing the state-of-the-art approaches.
Looking ahead, further research could explore the integration of additional modalities, such as medical images or patient data, to provide a more comprehensive understanding of the disease progression or device placement. Furthermore, the application of this framework to other domains, such as pathology or cardiology, could expand the potential impact of automatic report generation in the medical field.
In conclusion, this study highlights the importance and potential of automatic generation of radiology reports, particularly in capturing changes and progression over time. The proposed framework incorporates insights from multiple disciplines and achieves competitive performance, paving the way for more advanced applications in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | May 2, 2024 | Computer Science
Analysis and Expert Insights:
In this article, the author addresses the issue of decreasing download speed from WebHDFS API when using the cURL library in PHP. They conduct a series of experimental analyses to determine whether the cause of this decrease is the cURL library itself or the WebHDFS API.
It is interesting to note that the cURL library is widely used and considered a reliable tool for connecting to external resources and consuming REST web services. Therefore, it may come as a surprise to many programmers that there could be performance issues with the library.
The author’s experimental analysis focuses on testing the cURL library and the WebHDFS API separately and independently. This approach is crucial in order to accurately identify the cause of the decrease in download speed.
The results of the experiments, as mentioned by the author, clearly indicate that the cause of the decrease in download speed is the php’s cURL library itself, rather than the WebHDFS API. This finding has important implications for PHP developers who rely on the cURL library for communication with external resources.
Further investigation will be required to determine the specific reasons behind the performance issues in the cURL library. This could involve examining the code and architecture of the library, as well as exploring any potential compatibility issues with different versions of PHP or other factors that may contribute to the slowdown.
Overall, this article highlights the importance of thorough testing and analysis when encountering performance issues in software libraries. It serves as a reminder for developers to not take libraries like cURL for granted and to actively consider any potential performance implications in their applications.
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by jsendak | May 1, 2024 | Computer Science
arXiv:2404.19282v1 Announce Type: new
Abstract: Loss functions and sample mining strategies are essential components in deep metric learning algorithms. However, the existing loss function or mining strategy often necessitate the incorporation of additional hyperparameters, notably the threshold, which defines whether the sample pair is informative. The threshold provides a stable numerical standard for determining whether to retain the pairs. It is a vital parameter to reduce the redundant sample pairs participating in training. Nonetheless, finding the optimal threshold can be a time-consuming endeavor, often requiring extensive grid searches. Because the threshold cannot be dynamically adjusted in the training stage, we should conduct plenty of repeated experiments to determine the threshold. Therefore, we introduce a novel approach for adjusting the thresholds associated with both the loss function and the sample mining strategy. We design a static Asymmetric Sample Mining Strategy (ASMS) and its dynamic version Adaptive Tolerance ASMS (AT-ASMS), tailored for sample mining methods. ASMS utilizes differentiated thresholds to address the problems (too few positive pairs and too many redundant negative pairs) caused by only applying a single threshold to filter samples. AT-ASMS can adaptively regulate the ratio of positive and negative pairs during training according to the ratio of the currently mined positive and negative pairs. This meta-learning-based threshold generation algorithm utilizes a single-step gradient descent to obtain new thresholds. We combine these two threshold adjustment algorithms to form the Dual Dynamic Threshold Adjustment Strategy (DDTAS). Experimental results show that our algorithm achieves competitive performance on CUB200, Cars196, and SOP datasets.
Loss functions and sample mining strategies in deep metric learning
Deep metric learning algorithms are widely used in various applications such as image and video retrieval, face recognition, and person re-identification. These algorithms aim to learn a mapping function that can embed data points into a high-dimensional space, where similar points are closer to each other and dissimilar points are far apart.
Loss functions play a crucial role in training deep metric learning models. They measure the similarity or dissimilarity between pairs of samples and guide the learning process. However, existing loss functions often require an additional hyperparameter called a threshold. This threshold is used to determine whether a sample pair is informative or not.
The threshold acts as a numerical standard to filter out redundant sample pairs during training. If the threshold is set too high, it may exclude informative pairs and lead to underfitting. On the other hand, if the threshold is set too low, it may include irrelevant pairs and introduce noise, resulting in overfitting.
Therefore, finding the optimal threshold is a critical task in deep metric learning. Traditionally, this involves conducting extensive grid searches and repeated experiments, which can be time-consuming and computationally expensive.
Introducing the Asymmetric Sample Mining Strategy (ASMS) and Adaptive Tolerance ASMS (AT-ASMS)
To overcome the challenges of threshold selection, the authors propose a novel approach called the Asymmetric Sample Mining Strategy (ASMS) and its dynamic version, the Adaptive Tolerance ASMS (AT-ASMS).
ASMS addresses the problems caused by applying a single threshold to filter samples. It uses differentiated thresholds to handle the issues of too few positive pairs and too many redundant negative pairs. By employing multiple thresholds, ASMS can filter samples more effectively and improve the quality of the training data.
AT-ASMS takes the idea further by adaptively regulating the ratio of positive and negative pairs during training. It dynamically adjusts the thresholds based on the ratio of currently mined positive and negative pairs. This adaptive approach ensures that the model focuses on challenging examples and avoids being overwhelmed by easy or redundant samples.
The Dual Dynamic Threshold Adjustment Strategy (DDTAS)
To combine the benefits of ASMS and AT-ASMS, the authors propose the Dual Dynamic Threshold Adjustment Strategy (DDTAS). This strategy integrates the two threshold adjustment algorithms to achieve improved performance.
In DDTAS, the authors introduce a meta-learning-based threshold generation algorithm. It utilizes a single-step gradient descent to obtain new thresholds, allowing for more efficient and effective threshold adjustment.
Experimental results demonstrate that the proposed DDTAS algorithm achieves competitive performance on benchmark datasets such as CUB200, Cars196, and SOP. This highlights the effectiveness of the threshold adjustment strategies in deep metric learning tasks.
Connection to multimedia information systems, Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The concepts discussed in this article are highly relevant to the wider field of multimedia information systems and related technologies such as animations, artificial reality, augmented reality, and virtual realities.
In multimedia information systems, deep metric learning algorithms can be used for content-based retrieval and recommendation. By learning a high-dimensional representation of multimedia data, these algorithms enable efficient search and retrieval of relevant multimedia content.
Animations, artificial reality, augmented reality, and virtual realities often rely on the understanding of similarity and dissimilarity between objects or scenes. Deep metric learning plays a crucial role in these domains by providing techniques to measure and compare similarities between multimedia elements.
Moreover, the approach presented in this article, with its focus on optimizing loss functions and sample mining strategies, is directly applicable to the development of intelligent systems for generating and manipulating multimedia content. By improving the training process and the quality of the training data, these techniques can enhance the realism and effectiveness of various multimedia applications.
Overall, the concepts discussed in this article demonstrate the multi-disciplinary nature of deep metric learning and its relevance to the broader field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | May 1, 2024 | Computer Science
Analysis: Dynamic Model Switching in Machine Learning
Machine learning researchers and practitioners are constantly faced with the challenge of selecting the most effective model for a given dataset. Traditional approaches often fixate on a single model, which may lead to suboptimal performance when the dataset size and complexity change.
However, a recent breakthrough in the field introduces dynamic model switching as a solution to this challenge. This paradigm shift allows for the seamless transition between different models based on the evolving size of the dataset. In this case, the research focuses on the use of CatBoost and XGBoost, two popular machine learning algorithms.
What makes this approach unique is its adaptability. CatBoost, known for its exceptional efficacy in handling smaller datasets, provides nuanced insights and accurate predictions. On the other hand, XGBoost offers scalability and robustness, making it the preferred choice for larger and more intricate datasets. By dynamically switching between these models, researchers can harness the inherent strengths of each algorithm at the right time.
To ensure a meticulous balance between model sophistication and data requirements, this research introduces an accuracy threshold set by the user. This threshold acts as a benchmark, prompting the system to transition to a new model only if it guarantees improved performance. This user-defined approach allows practitioners to have control over the trade-off between model complexity and predictive accuracy.
The benefits of this dynamic model-switching mechanism extend beyond simply adapting to dataset size. In real-world scenarios where data is constantly evolving, this approach offers a flexible and efficient solution. It allows machine learning models to stay up-to-date with changing data dynamics, optimizing predictive accuracy at every step.
This research stands at the forefront of innovation in the field of machine learning. By redefining how models adapt and excel in the face of varying dataset dynamics, it opens new possibilities for improved performance and efficiency. As the field continues to evolve, we can expect further advancements in dynamic model selection and the integration of other algorithms into this framework.
Future Directions
While the research presented here focuses on the dynamic switching between CatBoost and XGBoost, there are opportunities for further exploration. One area of interest is the integration of additional machine learning algorithms into this framework. By expanding the repertoire of models that can be dynamically switched, researchers can better adapt to the unique characteristics of different datasets.
Another direction for future research is the exploration of more advanced techniques for determining the optimal time to switch models. Currently, the user-defined accuracy threshold serves as a simple mechanism for triggering the switch. However, there may be opportunities to incorporate more sophisticated algorithms, such as reinforcement learning, to make more informed decisions based on changing dataset dynamics.
Overall, dynamic model switching has the potential to revolutionize the field of machine learning by providing a flexible and efficient solution to the challenge of selecting the most effective model. As more researchers embrace this paradigm shift, we can expect to see further advancements, opening up new opportunities for improving predictive accuracy and model adaptability.
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by jsendak | Apr 30, 2024 | Computer Science
arXiv:2404.18162v1 Announce Type: new
Abstract: Despite significant strides in visual quality assessment, the neural mechanisms underlying visual quality perception remain insufficiently explored. This study employed fMRI to examine brain activity during image quality assessment and identify differences in human processing of images with varying quality. Fourteen healthy participants underwent tasks assessing both image quality and content classification while undergoing functional MRI scans. The collected behavioral data was statistically analyzed, and univariate and functional connectivity analyses were conducted on the imaging data. The findings revealed that quality assessment is a more complex task than content classification, involving enhanced activation in high-level cognitive brain regions for fine-grained visual analysis. Moreover, the research showed the brain’s adaptability to different visual inputs, adopting different strategies depending on the input’s quality. In response to high-quality images, the brain primarily uses specialized visual areas for precise analysis, whereas with low-quality images, it recruits additional resources including higher-order visual cortices and related cognitive and attentional networks to decode and recognize complex, ambiguous signals effectively. This study pioneers the intersection of neuroscience and image quality research, providing empirical evidence through fMRI linking image quality to neural processing. It contributes novel insights into the human visual system’s response to diverse image qualities, thereby paving the way for advancements in objective image quality assessment algorithms.
Visual quality assessment is an essential aspect of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. Understanding how humans perceive and evaluate the quality of visual content is crucial for developing algorithms and technologies that can automatically assess and enhance visual quality. This study, which employed functional MRI (fMRI), provides valuable insights into the neural mechanisms underlying visual quality perception.
The Complexity of Image Quality Assessment
The study found that assessing image quality is a more complex task for the human brain compared to content classification. While both tasks involve visual analysis, fine-grained analysis of image quality requires enhanced activation in high-level cognitive brain regions. This suggests that the brain engages in more in-depth processing to evaluate the quality of visual content.
By investigating brain activity during image quality assessment, the researchers have shed light on the multi-disciplinary nature of visual quality perception. The study involved a combination of neuroscience, psychology, and computer science, highlighting the need for an interdisciplinary approach in understanding human perception and cognitive processing.
The Brain’s Adaptability to Different Visual Inputs
The research demonstrates the brain’s adaptability to different qualities of visual information. When presented with high-quality images, the brain primarily utilizes specialized visual areas for precise analysis. This finding aligns with what we understand about the processing hierarchy in the visual system, where lower-level visual areas extract low-level features, such as edges and contours, while higher-level visual areas analyze more complex visual patterns.
In contrast, when presented with low-quality images, the brain recruits additional resources, including higher-order visual cortices and related cognitive and attentional networks. This suggests that the brain tries to compensate for the lack of detailed visual information by engaging broader cognitive and attentional processes. These processes might involve pattern recognition, inference, and top-down influences to decode and recognize ambiguous signals effectively.
The Intersection of Neuroscience and Image Quality Research
This study represents a pioneering effort to bridge the gap between neuroscience and image quality research. By using fMRI to link image quality to neural processing, the researchers have provided empirical evidence for the neural mechanisms underlying visual quality perception. This intersection of neuroscience and image quality research opens up new possibilities for objective image quality assessment algorithms.
Objective image quality assessment algorithms play a crucial role in various fields, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By developing algorithms that can automatically assess and enhance visual quality, we can improve the user experience across these domains.
In conclusion, this study contributes novel insights into the complexity of image quality assessment and the brain’s adaptable processing of visual inputs. It highlights the multi-disciplinary nature of understanding visual quality perception and paves the way for advancements in objective image quality assessment algorithms. The intersection of neuroscience and image quality research has the potential to revolutionize our understanding of visual perception and enhance the technologies that rely on it.
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by jsendak | Apr 30, 2024 | Computer Science
Expert Commentary: Deepfake Videos and the Battle Against Misinformation
Introduction
Deepfake videos have emerged as a significant threat to public trust and the spread of accurate information. With advances in artificial intelligence and video editing technologies, it has become easier to create highly realistic videos that manipulate and deceive viewers. The ramifications of this technology are vast, as it undermines the public’s ability to distinguish between what is real and what is fake. In this experiment, the researchers set out to explore whether labeling videos as containing actual or deepfake statements from US President Biden could influence participants’ ability to differentiate between true and false information.
The Power of Labeling
The findings from this study suggest that labeling videos can play a crucial role in combating misinformation. Participants accurately recalled 93.8% of deepfake videos and 84.2% of actual videos when they were properly labeled. This is an important finding, as it indicates that providing viewers with explicit information about the nature of a video can significantly impact their ability to discern between real and fake content. The implications of this research are particularly relevant in our current media landscape, where deepfake videos can easily make their way into newsfeeds and social media platforms.
The Role of Ideology and Trust
The study also revealed an interesting pattern when it comes to political ideology and trust in the message source. Individuals who identified as Republican and held lower favorability ratings of President Biden performed better in distinguishing between actual and deepfake videos. This finding aligns with the elaboration likelihood model (ELM), a psychological theory that predicts how people process and evaluate persuasive messages. According to the ELM, individuals who distrust the source of a message are more likely to engage in critical thinking and evaluation of the information presented. This heightened skepticism may explain why Republicans with lower favorability ratings of Biden were more discerning in their judgment of the videos.
Looking Ahead
As deepfake technology continues to evolve, it is imperative for researchers, policymakers, and tech companies to develop robust strategies to combat its negative impact. This study provides important insights into the effectiveness of labeling videos as a means to enhance public awareness and differentiate between real and fake content. However, there are still challenges ahead. Deepfake videos can become more sophisticated, making it harder to detect manipulation even with labels. Furthermore, the study only focused on a specific context (statements from President Biden) and may not fully capture the complexities of deepfake videos in other scenarios.
In the future, it will be essential to explore additional approaches to tackling deepfakes, such as developing advanced detection algorithms and implementing media literacy programs to educate the public about the dangers of misinformation. Collaboration between technology companies, researchers, and policy experts will be vital in staying one step ahead of those who seek to exploit deepfake technology for malicious purposes. Ultimately, a multi-faceted approach that combines technological solutions, educational initiatives, and regulatory measures will be crucial in ensuring the public’s ability to distinguish truth from fiction.
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