swarm intelligence in nature

P.Q. Extended Data Fig. A more recent work of al-Rifaie et al., "Swarmic Sketches and Attention Mechanism",[60] introduces a novel approach deploying the mechanism of 'attention' by adapting SDS to selectively attend to detailed areas of a digital canvas. and J.N.K. Article This was evaluated for a single model, a simple swarm model trained on 200 random patients from each training cohort, repeated three times with different random seeds. Med. 4 CONSORT chart for TCGA. 4ac), the merged cohort (Fig. What Nature Can Teach Us About Resiliency | Blog - PBS The study showed a 23% increase in diagnostic accuracy when using Artificial Swarm Intelligence (ASI) technology compared to majority voting. Swarm Intelligence (S.I.) PubMed Central and N.P.W. Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study. In detail, the systems had the following specifications: systemA, 128GB RAM and two NVIDIA Quadro RTX 6000 graphics processing units (GPUs); systemB, 64GB RAM and one NVIDIA RTX A6000 GPU; and systemC, 64GB RAM and two NVIDIA Quadro RTX 6000 GPUs. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. (BCCA) 140146 (IEEE, 2020). Ethical approval for research use of all cohorts was obtained from each contributing center. Swarm Intelligence | Volumes and issues - Springer Together, these data show that swarm-trained models consistently outperform local models and perform on par with centralized models in pathology image analysis. A method for normalizing histology slides for quantitative analysis. In addition, we assessed the model predictions on a micrometer scale by extracting the image patches with the highest scores for models trained on 300 patients and all patients from the local training cohorts (Fig. In histopathology, institutions across the world are digitizing their workflows, generating an abundance of data6. Third, classifiers were trained by SL, with the SL training process initiated on three separate bare-metal servers containing one training cohort each. 3, 118 (2020). SL, however, offers a solution to the governance problem, providing a true collaborative and democratic approach in which partners communicate and work on the same level, jointly and equally training models and sharing the benefits25,26,33. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. Our study provides a benchmark and a clear guideline for such future efforts, ultimately paving the way to establish SL in routine workflows. Mod. Rev. Nat. Benjamens, S., Dhunnoo, P. & Mesk, B. Similar results were obtained for the second MSI validation dataset YCR BCIP (Supplementary Table 3). Google Scholar. ): IEEE International Conference on Robotics and Automation, Workshop on Art and Robotics: Freud's Unheimlich and the Uncanny Valley. Findeisen, P. et al. In nature, it describes how honeybees migrate, how ants form perfect trails, and how birds flock. Despite the different geographic origins, the distribution of tumor stages in TCGA, DACHS and YCR BCIP is similar (Table 1), whereas in QUASAR, stageIII tumors are overrepresented, as adjuvant therapy is mainly indicated in stageIII tumors. In FL, peers do not share any input data with each other, and only share the learned model weights. Our SL setup can also be executed on a cluster with tasks potentially queued. Health 3, E763E772 (2021). 115, 20072016 (2020). J. In particular, it has not been applied to histopathology images, a common data modality with a high information density3. a, Classification performance (AUROC) for prediction of BRAF mutational status at the patient level in the QUASAR cohort. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima. Total cohort sizes (number of patients, for MSI/dMMR status) in the training sets are identical to those in b. Total cohort size (number of patients, for MSI/dMMR status) in the test set is 1,774 for QUASAR. In weighted SL, only one model checkpoint is generated (w-chkpt). Marks, K. & West, N. Molecular assessment of colorectal cancer through Lynch syndrome screening. Unlike the stigmergic communication used in ACO, in SDS agents communicate hypotheses via a one-to-one communication strategy analogous to the tandem running procedure observed in Leptothorax acervorum. The key component is the SL process, which contains the image processing components (Extended Data Fig. & Blackwell, T., al-Rifaie, Mohammad Majid, John Mark Bishop, and Tim Blackwell. Feature vectors and patient-wise target labels (BRAF or MSI status) served as input to a fully connected classification network. 2a and Supplementary Table 1). Ultimately, SL could improve the quality, robustness and resilience of AI in healthcare. & Zhu, J.) Urol. In each one, swarm intelligence blends global and local insight to improve how businesses make decisions. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploiting an artistic tension between the local behaviour of the 'birds flocking'as they seek to follow the input sketchand the global behaviour of the "ants foraging"as they seek to encourage the flock to explore novel regions of the canvas. Echle, A. et al. 54 (Eds. Loeffler, C. M. L. et al. For example, deep convolutional neural networks have been used to predict molecular alterations of cancer directly from routine pathology slides8,9,10,11,12,13. A possible technical limitation of our study is that we did not explicitly investigate differential privacy, but this could be incorporated in future work. Second, all training cohorts were merged, and new classifiers were trained on the merged cohort (combining all three training cohorts in a single computer system). Google Scholar. We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States, and validated the prediction performance in two independent datasets from the United Kingdom. To examine the plausibility of model predictions29, we used three methods: whole slide prediction heatmaps; a qualitative analysis of highly scoring image tiles (patches); and a quantitative, blinded, reader study of highly scoring image tiles. Gastroenterol. For 200 patients or fewer, the merged model outperformed local models and swarm models (Fig. Further information on research design is available in the Nature Research Reporting Summary linked to this article. In one such study, swarms of human radiologists connected together were tasked with diagnosing chest x-rays and demonstrated a 33% reduction in diagnostic errors as compared to the traditional human methods, and a 22% improvement over traditional machine-learning. and JavaScript. In QUASAR, b-chkpt1 and b-chkpt2 achieved prediction AUROCs of 0.80010.0073 and 0.81510.0071, respectively, and thereby significantly outperformed single-cohort models trained on Epi700 with an AUROC of 0.78840.0043 (P=0.0154 and P=8.79105 for b-chkpt1 and b-chkpt2, respectively; Supplementary Table 2). The Epi700 creation was enabled by funding from Cancer Research UK (C37703/A15333 and C50104/A17592) and a Northern Ireland HSC R&D Doctoral Research Fellowship (EAT/4905/13). [46], Airlines have also used ant-based routing in assigning aircraft arrivals to airport gates. Lu, M. Y. et al. To investigate this, we perform a retrospective multicentric study. If worker ants found food, they further backward to nest and their . J. These methods expand the utility of H&E-stained tissue slides from routine tumor diagnosis and subtyping to a source for direct prediction of molecular alterations3. The use of swarm intelligence in telecommunication networks has also been researched, in the form of ant-based routing. Merging the three training cohorts on a central server (merged model) improved the prediction AUROC to 0.75670.0139 (P=0.0727 vs Epi700, P=0.0198 vs DACHS, P=0.0043 vs TCGA; Fig. Currently, the total amount of healthcare data is increasing at an exponential pace. ; and (3) Is the phenotype subjectively consistent with a histological phenotype associated with BRAF mutations and/or MSI/dMMR? National Cancer Institute. Swarm Intelligence - an overview | ScienceDirect Topics Open access funding provided by Deutsches Krebsforschungszentrum (DKFZ). Extended Data Fig. Swarm intelligence - Scholarpedia Mach. 11, 3877 (2020). As the ground truth diagnostic methods for MSI/dMMR, immunohistochemistry was used in YCR BCIP and QUASAR, and PCR was used in TCGA, DACHS (ref. 2 and Fig. Howard, F. M. et al. "The pilot learns from his experience what's the best for him, and it turns out that that's the best solution for the airline," Douglas A. Lawson explains. Initial patient number in this dataset, exclusions and missing values, and final patient number. Unlike in FL, there is no central instance that always merges the parameters. J. Surg. These criteria were assessed in highly scoring image tiles generated by the median model (median performance out of five replicates) for each model type (three local, one merged and three swarm), using the model that was trained on all patients, as well as the model that was trained on 300 patients per cohort. Oncol. West, N. P. et al. Swarm technology is particularly attractive because it is cheap, robust, and simple. In fact, SI is the most high-growing branch on the current bio-inspired computation community [ 2 ]. Adjuvant chemotherapy versus observation in patients with colorectal cancer: a randomised study. Such behavior can also suggest deep learning algorithms, in particular when mapping of such swarms to neural circuits is considered. How Swarm Intelligence Blends Global and Local Insight https://en.wikipedia.org/w/index.php?title=Swarm_intelligence&oldid=1151978600, This page was last edited on 27 April 2023, at 12:04. Groups of different organisms of the same species in nature have been found to be successful in carrying out specific tasks, by means of a collective behavior, namely collective intelligence or swarm intelligence (SI) (Fisher 2009; Miller 2007; Surowiecki 2004 ). Chain FL: decentralized federated machine learning via blockchain. Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany, Oliver Lester Saldanha,Didem Cifci,Narmin Ghaffari Laleh,Tobias Seibel,Marko van Treeck,Christian Trautwein,Hannah Sophie Muti&Jakob Nikolas Kather, Pathology & Data Analytics, Leeds Institute of Medical Research at St Jamess, University of Leeds, Leeds, UK, Philip Quirke,Nicholas P. West,Heike I. Grabsch,Gordon G. A. Hutchins&Jakob Nikolas Kather, Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston Centre for Cancer Research, Queens University Belfast, Belfast, UK, Jacqueline A. James&Manuel Salto-Tellez, Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, UK, The Patrick G Johnston Centre for Cancer Research, Queens University Belfast, Belfast, UK, Jacqueline A. James,Maurice B. Loughrey&Manuel Salto-Tellez, Department of Cellular Pathology, Belfast Health and Social Care Trust, Belfast, UK, Centre for Public Health, Queens University Belfast, Belfast, UK, Department of Pathology and GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands, Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany, Elizabeth Alwers,Hermann Brenner,Tanwei Yuan&Michael Hoffmeister, Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK, Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany, Digital Biomarkers for Oncology Group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany, Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany, Institute for Computational Biomedicine, JRC for Computational Biomedicine, RWTH Aachen University, University Hospital Aachen, Aachen, Germany, Department of Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty of Heinrich Heine University Dsseldorf, University Hospital Dsseldorf, Dsseldorf, Germany, Institute of Pathology, University Medical Center Mainz, Mainz, Germany, Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany, You can also search for this author in performed the experiments. Medical AI models should not only have high performance, but should also be interpretable28,29. For BRAF prediction models trained on all patients, we observed no significant difference in the abundance of relevant patterns or structures (Extended Data Fig. CAS Gut 55, 11451150 (2006). During this process, blurry patches and patches with no tissue are removed from the dataset using Canny edge detection52. 65, 80728078 (2005). Med. af, Model trained on Epi700 (a), model trained on DACHS (b), model trained on TCGA (c), model trained on all three datasets (d), swarm chkpt1 (e), swarm chkpt2 (f). Extended Data Fig. The UK government has deployed a "collaborative swarm" of autonomous drones to detect and track military targets using artificial intelligence (AI), as part of a . Eur. Primary Handling Editor: Javier Carmona, in collaboration with the Nature Medicine team. [8] SPP models predict that swarming animals share certain properties at the group level, regardless of the type of animals in the swarm. and C.T. All processes (also called nodes in the original HPE implementation) run in a Docker container. 70113864). These ground truth diagnostic methods are the clinical state of the art in determining MSI/dMMR status50. To obtain In practice, such data sharingespecially across different countriesfaces legal and logistical obstacles. 256, 5060 (2022). [7] as a special case of the boids model introduced in 1986 by Reynolds. Jakob Nikolas Kather. Guest Editors: Karl Doerner, Daniel Merkle and Thomas Sttzle. S.I. Performance results of all experiments related to Fig. Br. Gastroenterology 159, 129138.E9 (2020). In the world of AI, swarm systems draw input from . contracts here. For all cohorts, BRAF mutational status and MSI/dMMR48 data were acquired. QUASAR Collaborative Group. provided clinical and histopathological data. The idea is utilized in chipping away at man-made consciousness. Eng. Swarm prediction has been used in the context of forecasting problems. Model weights are averaged at each sync event, before the training continues at each peer with the averaged parameters. Qualitative plausibility criteria were as follows: (1) Is tumor present on the highly scoring tiles? Learning from small datasets is a challenge in medical AI because prediction performance generally increases with increasing size of the training dataset19,20. In all image tiles for highly scoring tiles in the wild-type BRAF and microsatellite stability (MSS)/mismatch repair proficiency (pMMR) classes, the occurrence of relevant patterns or structures was uniformly low, and no statistically significant differences were present. Each pilot acts like an ant searching for the best airport gate. In this study, the five highest-scoring tiles for the five highest-scoring patients for mutated and wild-type BRAF and MSI/dMMR and MSS/pMMR (1,400 image tiles total) were assessed for the presence of TILs, the presence of any mucin, poor differentiation, Crohns-like lymphoid reaction and the presence of signet ring cells, based on criteria proposed in ref. BMC Cancer 19, 681 (2019). Exact Pvalues are available in Supplementary Table 1 (for a), Supplementary Table 2 (for b) and Supplementary Table 3 (for c). This was pioneered separately by Dorigo et al. Federated learning for computational pathology on gigapixel whole slide images. declares consulting services for Owkin, France, and Panakeia, UK. The swarms move throughout the digital canvas in an attempt to satisfy their dynamic rolesattention to areas with more detailsassociated with them via their fitness function. b, Classification performance (AUROC) for prediction of MSI/dMMR status at the patient level in the QUASAR cohort. As pathology services are currently undergoing a digital transformation, embedding AI methods into routine diagnostic workflows could ultimately enable the prescreening of patients, thereby reducing the number of costly genetic tests and increasing the speed at which results are available to clinicians27. In this setup, the blockchain maintains the global state information about the model. Examples of swarm intelligence exist in nature, in formations of migratory birds and in swarms of insects, from which swarm intelligence derives its name. Swarm intelligence for next-generation networks: Recent - ScienceDirect The participating peers coordinate the synchronization among each other such that the other peers will wait if one peer is not yet ready for synchronization. Swarm intelligence: Bee-inspired AI can help improve decisions - Big Think In ac, the boxes show the median values and quartiles, the whiskers show the rest of the distribution (except for points identified as outliers), and all original data points are shown. Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization.
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