Lancet Digit. Artificial intelligence can cut costs while maintaining accuracy in colorectal cancer genotyping. Most recently, SL has been tested to detect coronavirus disease 2019 (COVID-19), tuberculosis, leukemia and lung pathologies from transcriptome analysis or X-ray images26. National Cancer Institute. 2b,c). 2c and Extended Data Fig. (a) Presence of relevant patterns or structures in highly scoring tiles in the BRAF mutated class for BRAF prediction models trained on 300 patients per cohort, as scored by the blinded observer. 49) and Epi700 (ref. Rev. In nature, it describes how honeybees migrate, how ants form perfect trails, and how birds flock. Morphological characterization of colorectal cancers in The Cancer Genome Atlas reveals distinct morphologymolecular associations: clinical and biological implications. Nat. Cancer Res. Swarm Intelligence | Home - Springer This approach is a re-implementation of a previously published workflow52. BMJ Open 9, e030618 (2019). 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 nature, collective behavior and self-or ganization allow complicated global patterns. Google Scholar. 7a). 2) of patients with stageII and stageIII colon cancer, whose data were provided by the Northern Ireland Biobank40,42 (application NIB20-0346); (2)the DACHS cohort (n=2,448; Extended Data Fig. Instead, smart contracts on an Ethereum blockchain (https://ethereum.org) enable the network to select any of the peers to perform parameter merging at every sync stop. [36] ASI has also been used to enable groups of doctors to generate diagnoses with significantly higher accuracy than traditional methods. How Nature is Inspiring AI Algorithms - rootstrap.com The swarm network process handles peer crosstalk over the network. https://doi.org/10.1038/s41591-022-01768-5, DOI: https://doi.org/10.1038/s41591-022-01768-5. Swarm Intelligence: From Natural to Artificial Systems PubMed Central O.L.S., M.v.T. Only one model was developed and used, and no other models were evaluated. 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. Nat. The impact of site-specific digital histology signatures on deep learning model accuracy and bias. [27][28], Ant colony optimization (ACO), introduced by Dorigo in his doctoral dissertation, is a class of optimization algorithms modeled on the actions of an ant colony. Swarm Learning for decentralized and confidential clinical machine learning. Swarm Intelligence (SI, [ 1 ]) has arisen as one of the most studied areas within the wider artificial intelligence field. The concept is employed in work on artificial intelligence. Lancet Digit. Before training, the number of tiles in each class was equalized by random undersampling, as described before9,12. Michael Theodore and Nikolaus Correll use swarm intelligent art installation to explore what it takes to have engineered systems to appear lifelike. together and then reaching the optimized solution for a given problem. Again, for the qualitative reader study, the model with the median performance out of five replicates was used. Each server runs an AI process (a program that trains a model on the data) and a network process (a program that handles communication with peers via blockchain). The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. [50][51] Human swarming allows the collective intelligence of interconnected groups of people online to be harnessed. 1e). Mach. Article Swarm Intelligence - an overview | ScienceDirect Topics O.L.S. https://en.wikipedia.org/w/index.php?title=Swarm_intelligence&oldid=1151978600, This page was last edited on 27 April 2023, at 12:04. Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J. Swarm intelligence is a form of artificial intelligence (AI) inspired by the insect kingdom. [9] Swarming systems give rise to emergent behaviours which occur at many different scales, some of which are turning out to be both universal and robust. As the system behaves stochastically and is therefore lacking repeatability, there are large hurdles to commercial deployment. We hypothesize that SL could be a substitute for the centralized collection of data from large patient cohorts in histopathology, improving prediction performance20 and generalizability22 without centralizing control over the final model. Kundu, S. AI in medicine must be explainable. Pathologic predictors of microsatellite instability in colorectal cancer. SDS is both an efficient and robust global search and optimisation algorithm, which has been extensively mathematically described. Exact Pvalues are available in Supplementary Table 1 (for a), Supplementary Table 2 (for b) and Supplementary Table 3 (for c). In this setup, the blockchain maintains the global state information about the model. June 2009, issue 2. P-values indicate a two-sided comparison between the three local models and the three Swarm-trained models for each feature. [7] as a special case of the boids model introduced in 1986 by Reynolds. ; and (3) Is the phenotype subjectively consistent with a histological phenotype associated with BRAF mutations and/or MSI/dMMR? P.Q., M.B.L., M.S.-T., T.J.B., H.I.G., G.G.A.H., E.A., J.A.J., R.G., J.C.-C., H.B., M.H. designed the study. A possible technical limitation of our study is that we did not explicitly investigate differential privacy, but this could be incorporated in future work. c, AUROC for prediction of MSI status in the YCR BCIP dataset. J. Surg. [13] This list includes algorithms published up to circa the year 2000. 3c and Supplementary Table 6). The dataset used in this study contains information on the mixture proportion and CS values of 3240 concrete mixtures. At Southwest Airlines a software program uses swarm theory, or swarm intelligencethe idea that a colony of ants works better than one alone. PubMed Schrammen, P. L. et al. Artificial 'ants'simulation agentslocate optimal solutions by moving through a parameter space representing all possible solutions. Swarm Learning for decentralized and confidential clinical - Nature For prediction of molecular features from image data, we adapted our weakly supervised end-to-end prediction pipeline, which outperformed similar approaches for mutation prediction in a recent benchmark study52. Cancer 124, 686696 (2021). Abbreviations: WSI=whole slide images, MSI=microsatellite instability, SL=swarm learning, SN=swarm network, SPIRE=SPIFFE Runtime Environment. Correspondence to Coudray, N. et al. Development of AI-based pathology biomarkers in gastrointestinal and liver cancer. PubMed Examples of swarm intelligence exist in nature, in formations of migratory birds and in swarms of insects, from which swarm intelligence derives its name. 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. The articulation was presented by Gerado Beni and Jing Wang in 1989, with regards to cell mechanical frameworks. Mobile media and new technologies have the potential to change the threshold for collective action due to swarm intelligence (Rheingold: 2002, P175). On the Global Convergence of Particle Swarm Optimization Methods The idea is utilized in chipping away at man-made consciousness. Abstract Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. Learning from small datasets is a challenge in medical AI because prediction performance generally increases with increasing size of the training dataset19,20. 27 (ref. Total cohort sizes (number of patients, for BRAF mutational status) in the training set are 642 for Epi700, 2,075 for DACHS and 500 for TCGA. As a preprocessing step, high-resolution WSIs were tessellated into patches of 512pixels512pixels3colors and were color-normalized53. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. d, Model examination through slide heatmaps of tile-level predictions for representative cases in the QUASAR cohort. . We are grateful to the HPE customer support team for providing assistance in using the HPE Swarm Learning package. The Collective Power Of Swarm Intelligence In AI And Robotics - Forbes Feature vectors and patient-wise target labels (BRAF or MSI status) served as input to a fully connected classification network. Hence, we selected three geographically distributed training cohorts, representative of various real-world clinical settings: (1)the Northern Ireland Epi700 cohort (n=661; Extended Data Fig. Am. Access to QUASAR and YCR BCIP was obtained via Pathology & Data Analytics, Leeds Institute of Medical Research at St Jamess, University of Leeds, Leeds, UK (https://medicinehealth.leeds.ac.uk/dir-record/research-groups/557/pathology-and-data-analytics), and access to DACHS was obtained via the DACHS study group at http://dachs.dkfz.org/dachs/kontakt.html. The journal publishes original research articles and occasional reviews on theoretical, experimental, and practical aspects of swarm intelligence. Preprint at https://www.biorxiv.org/content/10.1101/2021.08.09.455633v1 (2021). Our SL implementation requires HPEs SL community edition, which is publicly available under an Apache 2.0 license along with detailed instructions and troubleshooting help at https://github.com/HewlettPackard/swarm-learning. Nat. For BRAF prediction models, TILs (P=0.019), poor differentiation (P=0.017) and signet ring cells (P=0.019) were significantly more frequently present in tiles selected by swarm-trained models than in those selected by locally trained models (Extended Data Fig. NASA is investigating the use of swarm technology for planetary mapping. 4c). ". This study is a retrospective analysis of digital images of anonymized archival tissue samples from five cohorts of patients with CRC. Military Drone Swarm Intelligence Explained - Sentient Digital, Inc. Artem Shmatko, Narmin Ghaffari Laleh, Jakob Nikolas Kather, Wouter Bulten, Kimmo Kartasalo, the PANDA challenge consortium, Andr Homeyer, Christian Geiler, Norman Zerbe, Nicole M. Thomasian, Ihab R. Kamel & Harrison X. Bai, Wouter Bulten, Maschenka Balkenhol, Geert Litjens, Jean Ogier du Terrail, Armand Leopold, Pierre-Etienne Heudel, Shivam Kalra, H. R. Tizhoosh, Liron Pantanowitz, Alexander Binder, Michael Bockmayr, Frederick Klauschen, Benjamin Liechty, Zhuoran Xu, David J. Pisapia, Nature Medicine The training time decreased with more frequent synchronizations (Extended Data Fig. Together, these data show that swarm-trained models consistently outperform local models and perform on par with centralized models in pathology image analysis. Introduction Advances in scalable computing and artificial intelligence have developed swarm intelligence approaches. Histopathol. [37][53][54][38], The University of California San Francisco (UCSF) School of Medicine released a preprint in 2021 about the diagnosis of MRI images by small groups of collaborating doctors. 254, 7079 (2021). Use the Previous and Next buttons to navigate the slides or the slide controller buttons at the end to navigate through each slide. For all cohorts, BRAF mutational status and MSI/dMMR48 data were acquired. Swarm prediction has been used in the context of forecasting problems. Internet Explorer). No other potential conflicts of interest are reported by any of the authors. The principle of SL is to jointly train a machine learning model in different physically separated computer systems. The "creativity" of this hybrid swarm system has been analysed under the philosophical light of the "rhizome" in the context of Deleuze's "Orchid and Wasp" metaphor.[59]. Value of mismatch repair, KRAS, and BRAF mutations in predicting recurrence and benefits from chemotherapy in colorectal cancer. Image Anal. [5] 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. This could potentially facilitate collaboration among several parties, hence generating more powerful and more reliable AI systems. . (b) Presence of relevant patterns or structures in highly scoring tiles in the MSI/dMMR for MSI status prediction models trained on 300 patients per cohort, as scored by the blinded observer. Swarm learning for decentralized artificial intelligence in cancer histopathology, https://doi.org/10.1038/s41591-022-01768-5. Our data show that SL-trained AI models outperform most locally trained models, and perform on par with models that are trained on the merged datasets. Rev. AI is expected to have a profound effect on the practice of medicine in the next 10years1,2,3,4. Swarm learning for decentralized artificial intelligence in - Nature O.L.S., N.G.L. 29). A. Y. Communication-efficient learning of deep networks from decentralized data. Google Scholar. A very different-ant inspired swarm intelligence algorithm, stochastic diffusion search (SDS), has been successfully used to provide a general model for this problem, related to circle packing and set covering. 4). 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. Southwest Airlines researcher Douglas A. Lawson used an ant-based computer simulation employing only six interaction rules to evaluate boarding times using various boarding methods. In each one, swarm intelligence blends global and local insight to improve how businesses make decisions. We developed an SL-capable AI pipeline for molecular classification of solid tumors based on histopathology images (Fig. and Hewlett-Packard in the mid-1990s, with a number of variants existing. [39][bettersourceneeded], Swarm Intelligence-based techniques can be used in a number of applications. Intell. Total cohort size (number of patients, for BRAF mutational status) in the test set is 1,477 for QUASAR. This is a significant improvement on the performances of all other models, including the local models of Epi700 (P=0.0015), DACHS (P=8.65105) and TCGA (P=0.0004), but also the merged model (P=0.0374), b-chkpt1 (P=0.0154) and b-chkpt2 (P=0.0081; Supplementary Table 1). In particular, for training BRAF prediction models on the largest cohort (DACHS), there was a pronounced performance drop from an AUROC of 0.73390.0108 when training on all patients to an AUROC of 0.66260.0162 when restricting the number of patients in the training set to 200. analyzed the data. Each pilot acts like an ant searching for the best airport gate. However, AI requires large and diverse datasets, and its performance scales with the amount of training data19,20. These nature-inspired metaheuristic algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. 3), including samples from patients with CRC at any disease stage recruited at more than 20 hospitals in Germany for a large population-based case-control study, which is coordinated by the German Cancer Research Center (DKFZ)43,44,45; and (3)the TCGA CRC cohort (n=632; Extended Data Fig. 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. 6), from a population-based study collected in Yorkshire in the United Kingdom. Bilal, M. et al. Echle, A. et al. Histopathology 78, 401413 (2021). ISSN 1546-170X (online) Swarm learning for decentralized artificial intelligence in cancer histopathology. 1a). A Systematic Literature Review on Robust Swarm Intelligence - Hindawi Lynch syndrome screening in colorectal cancer: results of a prospective 2-year regional programme validating the NICE diagnostics guidance pathway throughout a 5.2-million population. 29, 12611270 (2011). 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. Commun. [47], Artists are using swarm technology as a means of creating complex interactive systems or simulating crowds. Swarm Learning combines a special kind of information exchange across different nodes of a network with methods from the toolbox of "machine learning," a branch of artificial intelligence (AI . December 2009, issue 4. Google Scholar. quantified the results of the reader study. Regional multidisciplinary team intervention programme to improve colorectal cancer outcomes: study protocol for the Yorkshire Cancer Research Bowel Cancer Improvement Programme (YCR BCIP). Benchmarking artificial intelligence methods for end-to-end computational pathology. CAS Google Scholar. Pan-cancer image-based detection of clinically actionable genetic alterations. In particular, it has not been applied to histopathology images, a common data modality with a high information density3. is supported by the German Federal Ministry of Health (DEEP LIVER, ZMVI1-2520DAT111) and the Max Eder Program of the German Cancer Aid (grant no. Artificial intelligencebased detection of FGFR3 mutational status directly from routine histology in bladder cancer: a possible preselection for molecular testing? Basic model checkpoint1 (b-chkpt1) was obtained when the partner with the smallest training cohort (TCGA) reached the end of the final epoch (Fig. In the future, our approach could be applied to other image classification tasks in computational pathology. & Shah, S. P. Harnessing multimodal data integration to advance precision oncology. Description. What Is Swarm Intelligence? | Baeldung on Computer Science Ultimately, SL could improve the quality, robustness and resilience of AI in healthcare. They are used in many optimization problems due to their simplicity, flexibility, and scalability. Conf. This also reduces hardware requirements, potentially making SL an option for researchers in low-income and middle-income countries. [8] SPP models predict that swarming animals share certain properties at the group level, regardless of the type of animals in the swarm. 2, 305311 (2020). In the future, SL can be used to train distributed AI models for any histopathology image analysis task, eliminating the need for data transfer. Having associated the rendering process with the concepts of attention, the performance of the participating swarms creates a unique, non-identical sketch each time the 'artist' swarms embark on interpreting the input line drawings. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. In Epi700, BRAF mutation screening was performed as part of the ColoCarta panel using a validated mass spectrometry-based targeted screening panel of 32 somatic mutations in six genes (Agena Bioscience)40. Swarm intelligence for next-generation networks: Recent - ScienceDirect 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 . Swarm intelligence algorithms for multiple unmanned aerial - Springer Mod. 1 Workflow details and effect of synchronization interval. Strikingly, SL was also able to rescue the performance: down to 100 patients per cohort, weighted SL (w-chkpt) maintained a high performance with AUROCs of 0.70000.0260 for 100 patients, 0.71390.0149 for 200 patients and 0.74380.0093 for 300 patients. [20], First published in 1989 Stochastic diffusion search (SDS)[21][22] was the first Swarm Intelligence metaheuristic. P.Q. We show that AI models trained using SL can predict BRAF mutational status and microsatellite instability directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer. Provided by the Springer Nature SharedIt content-sharing initiative, Nature Medicine (Nat Med) ACO is a probabilistic technique useful in problems that deal with finding better paths through graphs. We train on all datasets for the same fixed number of epochs (five epochs, motivated by previous studies). These ground truth diagnostic methods are the clinical state of the art in determining MSI/dMMR status50. Nature-inspired computation and swarm intelligence have become popular and effective tools for solving problems in optimization, computational intelligence, soft computing and data science. Histopathology 79, 690699 (2021). Sometimes referred to as Human Swarming or Swarm AI, the technology connects groups of human participants into real-time systems that deliberate and converge on solutions as dynamic swarms when simultaneously presented with a question[32][33][34] ASI has been used for a wide range of applications, from enabling business teams to generate highly accurate financial forecasts[35] to enabling sports fans to outperform Vegas betting markets. In weighted SL, only one model checkpoint is generated (w-chkpt). [30][31] Particles then move through the solution space, and are evaluated according to some fitness criterion after each timestep. The program can even alert a pilot of plane back-ups before they happen. Swarm Intelligence - an overview | ScienceDirect Topics Special Issue: Particle Swarm Optimization. The system holding datasetA will reach the final epoch faster than those holding datasetsB and C. At this point, the basic model checkpoint b-chkpt1 is created. Swarm Intelligence | Volumes and issues - Springer A minimal selection of locations (or sites) are required subject to providing adequate area coverage for users. Med. Article As a subset of artificial intelligence (AI), swarm intelligence (SI) has been widely used in the literature. S.I. Swarm Learning for decentralized and confidential clinical machine learning | Nature Article Open Access Published: 26 May 2021 Swarm Learning for decentralized and confidential clinical. The weighting factor is strictly proportional to the number of tiles. Echle, A. et al. et al. (b) Schematic of the training procedure in Swarm Learning. "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. Deleuze G, Guattari F, Massumi B. Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F. K. & Mahmood, F. Synthetic data in machine learning for medicine and healthcare. We collected digital WSIs of H&E-stained slides of archival tissue sections of human CRC from five patient cohorts, three of which were used as training cohorts and two of which were used as test cohorts (Table 1). fascinating capabilities of swarm intelligence, large multi-agent systems are employed. freedom and constraints) within the swarm intelligence's two infamous phases of exploration and exploitation. [57], In a series of works, al-Rifaie et al. In practice, such data sharingespecially across different countriesfaces legal and logistical obstacles. Gut 55, 11451150 (2006). 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. Slider with three articles shown per slide. In SL, AI models are trained locally, and models are combined centrally without requiring central coordination. 65, 80728078 (2005). Here, we improved this by using three physically separate devices and implementing our code largely with open-source software. Model weights are sent from each partner to the other peers at multiple synchronization (sync) events, which happen at the end of each sync interval. For 200 patients or fewer, the merged model outperformed local models and swarm models (Fig. Natural ants lay down pheromones directing each other to resources while exploring their environment. (c) Same experiment as panel (A), but for the models which were trained on all patients in all cohorts. Estimation of absolute risk of colorectal cancer based on healthy lifestyle, genetic risk, and colonoscopy status in a population-based study. [64], Stochastic diffusion search (Bishop 1989), Particle swarm optimization (Kennedy, Eberhart & Shi 1995), Hu, J.; Turgut, A.; Krajnik, T.; Lennox, B.; Arvin, F., ", Hu, J.; Bhowmick, P.; Jang, I.; Arvin, F.; Lanzon, A., ". In Proc. Each dataset was stored in a physically separate computing server. CAS and T.S. Singh, A. Swarm Intelligence The term Swarm intelligence, first introduced by G. Beni and J. Wang in 1989, describes a self-organization system formed by a group of individuals that interact with each other and their environment. Swarm robotics - a case study: bat robotics - ScienceDirect Rev. Open J. al-Rifaie, M.M., Bishop, J.M. The main advantages of SI and other nature-inspired algorithms over . Designing deep learning studies in cancer diagnostics. 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.