14,130 research outputs found

    Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control

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    This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning and control. The paper also discusses the potential benefits of integrating AI and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    Ambiguous Medical Image Segmentation using Diffusion Models

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    Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks. For the task of medical image segmentation, existing research on AI-based alternatives focuses more on developing models that can imitate the best individual rather than harnessing the power of expert groups. In this paper, we introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent stochastic sampling process of diffusion using only minimal additional learning. We demonstrate on three different medical image modalities- CT, ultrasound, and MRI that our model is capable of producing several possible variants while capturing the frequencies of their occurrences. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks in terms of accuracy while preserving naturally occurring variation. We also propose a new metric to evaluate the diversity as well as the accuracy of segmentation predictions that aligns with the interest of clinical practice of collective insights

    One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era

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    OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demonstrated to be one small step for generative AI (GAI), but one giant leap for artificial general intelligence (AGI). Since its official release in November 2022, ChatGPT has quickly attracted numerous users with extensive media coverage. Such unprecedented attention has also motivated numerous researchers to investigate ChatGPT from various aspects. According to Google scholar, there are more than 500 articles with ChatGPT in their titles or mentioning it in their abstracts. Considering this, a review is urgently needed, and our work fills this gap. Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges. Moreover, we present an outlook on how ChatGPT might evolve to realize general-purpose AIGC (a.k.a. AI-generated content), which will be a significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated ([email protected]

    International alliance of Urolithiasis (IAU) guideline on percutaneous nephrolithotomy

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    The International Alliance of Urolithiasis (IAU) would like to release the latest guideline on percutaneous nephrolithotomy (PCNL) and to provide a clinical framework for surgeons performing PCNL. These recommendations were collected and appraised from a systematic review and assessment of the literature covering all aspects of PCNLs from the PubMed database between January 1, 1976, and July 31, 2021. Each generated recommendation was graded using a modified GRADE methodology. The quality of the evidence was graded using a classification system modified from the Oxford Center for Evidence-Based Medicine Levels of Evidence. Forty-seven recommendations were summarized and graded, which covered the following issues, indications and contraindications, stone complexity evaluation, preoperative imaging, antibiotic strategy, management of antithrombotic therapy, anesthesia, position, puncture, tracts, dilation, lithotripsy, intraoperative evaluation of residual stones, exit strategy, postoperative imaging and stone-free status evaluation, complications. The present guideline on PCNL was the first in the IAU series of urolithiasis management guidelines. The recommendations, tips and tricks across the PCNL procedures would provide adequate guidance for urologists performing PCNLs to ensure safety and efficiency in PCNLs

    In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning

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    Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlying complex physical events such as crack propagation and pores formation. However, due to the noisy environment and intricate signal content, acoustic-based monitoring in LDED has received little attention. This paper proposes a novel acoustic-based in-situ defect detection strategy in LDED. The key contribution of this study is to develop an in-situ acoustic signal denoising, feature extraction, and sound classification pipeline that incorporates convolutional neural networks (CNN) for online defect prediction. Microscope images are used to identify locations of the cracks and keyhole pores within a part. The defect locations are spatiotemporally registered with acoustic signal. Various acoustic features corresponding to defect-free regions, cracks, and keyhole pores are extracted and analysed in time-domain, frequency-domain, and time-frequency representations. The CNN model is trained to predict defect occurrences using the Mel-Frequency Cepstral Coefficients (MFCCs) of the lasermaterial interaction sound. The CNN model is compared to various classic machine learning models trained on the denoised acoustic dataset and raw acoustic dataset. The validation results shows that the CNN model trained on the denoised dataset outperforms others with the highest overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC score (98%). Furthermore, the trained CNN model can be deployed into an in-house developed software platform for online quality monitoring. The proposed strategy is the first study to use acoustic signals with deep learning for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin

    Towards Evaluating Explanations of Vision Transformers for Medical Imaging

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    As deep learning models increasingly find applications in critical domains such as medical imaging, the need for transparent and trustworthy decision-making becomes paramount. Many explainability methods provide insights into how these models make predictions by attributing importance to input features. As Vision Transformer (ViT) becomes a promising alternative to convolutional neural networks for image classification, its interpretability remains an open research question. This paper investigates the performance of various interpretation methods on a ViT applied to classify chest X-ray images. We introduce the notion of evaluating faithfulness, sensitivity, and complexity of ViT explanations. The obtained results indicate that Layerwise relevance propagation for transformers outperforms Local interpretable model-agnostic explanations and Attention visualization, providing a more accurate and reliable representation of what a ViT has actually learned. Our findings provide insights into the applicability of ViT explanations in medical imaging and highlight the importance of using appropriate evaluation criteria for comparing them.Comment: Accepted by XAI4CV Workshop at CVPR 202

    UniverSeg: Universal Medical Image Segmentation

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    While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task, researchers generally have to train or fine-tune models, which is time-consuming and poses a substantial barrier for clinical researchers, who often lack the resources and expertise to train neural networks. We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. Given a query image and example set of image-label pairs that define a new segmentation task, UniverSeg employs a new Cross-Block mechanism to produce accurate segmentation maps without the need for additional training. To achieve generalization to new tasks, we have gathered and standardized a collection of 53 open-access medical segmentation datasets with over 22,000 scans, which we refer to as MegaMedical. We used this collection to train UniverSeg on a diverse set of anatomies and imaging modalities. We demonstrate that UniverSeg substantially outperforms several related methods on unseen tasks, and thoroughly analyze and draw insights about important aspects of the proposed system. The UniverSeg source code and model weights are freely available at https://universeg.csail.mit.eduComment: Victor and Jose Javier contributed equally to this work. Project Website: https://universeg.csail.mit.ed

    Image classification over unknown and anomalous domains

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    A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting. Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each. While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so. In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks

    An investigation of the relationship between perioperative characteristics and perioperative anaesthesia on the postoperative systemic inflammatory response and clinical outcome in patients undergoing surgery for colorectal cancer

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    In UK, colorectal cancer (CRC) is the fourth most common cancer and the second most common cause of cancer death. Until now, surgical resection remains the cornerstone for the management of CRC in all stages, however, stress response elicit from surgery may cause different changes through multiple systems in human body including neural, endocrine, metabolic, inflammatory, and immunological changes. In addition, other perioperative factors such as volatile anaesthetic and opioids may induce the immunosuppression. There is a proportional correlation between the stress response and the magnitude of the inflammatory immune response, invasiveness, and duration of surgery. The pre-operative and post-operative status of patients are important when considering the prognosis. The systemic inflammatory response (SIR) has been recognised to correlate with tumour progression and the prognosis of CRC. An exaggerated postoperative SIR is associated with postoperative infective complications and poor survival. Several predictive markers of the SIR have been used, such as the neutrophil to lymphocyte ratio (NLR), serum C-reactive protein (CRP) level, and Glasgow prognostic score (GPS). Some evidence reported that general anaesthesia (GA) combined with regional anaesthesia (RA) are better than the single use of general anaesthesia in reducing the post-operative immuno-suppression in some degrees. Furthermore, the peri-operative inflammatory process may be affected by the choice of anaesthetic technique, with propofol reported to have anti-inflammatory effect by targeting neutrophil activity. Up to now, there is insufficient evidence to recommend any specific anaesthetic or analgesic technique for patients undergoing surgery for tumour resection based on inflammatory response, recurrence, and metastasis. The work presented in this thesis further examines the relationship between the perioperative characteristics, perioperative anaesthesia, and the postoperative systemic inflammatory response following surgery for colorectal cancer. Several preoperative medications along with anaesthesia might influence the postoperative systemic inflammatory response but the question is whether the post-operative systemic inflammatory response affected by the administration of different types of anaesthesia or not following surgery for colorectal cancer. Chapter 1 discusses the epidemiology, aetiology, carcinogenesis, risk factors of colorectal cancer, pro-carcinogenic factors, anti-carcinogenic agents, inflammation and cancer, the post-operative systemic inflammatory response, tumour staging, screening, and diagnosis of colorectal cancer. Chapter 2 discusses the treatment of colorectal cancer. Chapter 3 discusses different anaesthetic techniques and agents. Chapter 4 provides summary and aims of the thesis. Chapter 5 represents findings from a systematic review and meta-analysis about the effect of anaesthesia on the postoperative systemic inflammatory response in patients undergoing surgery. The results conclude that there was some evidence that anaesthetic regimens may reduce the magnitude of the post-operative SIR. However, the studies identified in this systematic review were heterogeneous and generally of low quality. Chapter 6 represents a retrospective cohort study about the relationship between anaesthetic technique, clinicopathological characteristics and the magnitude of the postoperative systemic inflammatory response in patients undergoing elective surgery for colon cancer. The results show that the type of anaesthesia varied over time and appears to influence the magnitude of the postoperative SIR on post-operative day 2 for those patients who underwent for open surgery but not laparoscopic surgery. Chapter 7 represents a prospective cohort study about the effect of anaesthesia on the magnitude of the postoperative systemic inflammatory response in patients undergoing elective surgery for colorectal cancer in the context of an enhanced recovery pathway. The results show that there was a modest but an independent association between regional anaesthesia (RA) and a lower magnitude of the postoperative SIR. Chapter 8 represents the relationship between pre-operative medications, the type of anaesthesia and post-operative sequelae in patients undergoing surgery for colorectal cancer. The results show that there was no association between the preoperative administration of aspirin, statins and ACE inhibitors and anaesthesia. Chapter 9 represents the relationship between nutritional status, anaesthetic approach, and peri-operative characteristics of patients undergoing surgery for colorectal cancer. The results show that there was no significant association between measures of nutritional status and anaesthetic approach. Chapter 10 represents the relationship between opioid administration, type of anaesthesia and clinicopathological characteristics in patients undergoing surgery for colorectal cancer. The results show that opioid administration was independently associated with both anaesthetic and operative factors. Chapter 11 represents the main findings of the thesis and some recommendation for a future work
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