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Biomedical image analysis competitions: The state of current participation practice

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

Illinois Express Quantum Network (IEQNET): Metropolitan-scale experimental quantum networking over deployed optical fiber

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arXiv 2021年

作者： Chung, Joaquin Kanter, Gregory Lauk, Nikolai Valivarthi, Raju Wu, Wenji Ceballos, Russell R. Peña, Cristián Sinclair, Neil Thomas, Jordan Xie, Si Kettimuthu, Rajkumar Kumar, Prem Spentzouris, Panagiotis Spiropulu, Maria Data Science and Learning Division Argonne National Laboratory 9700 S Cass Ave. LemontIL60439 United States University of Chicago Consortium for Advanced Science and Engineering 924 E 57th St ChicagoIL60637 United States NuCrypt LLC 1840 Oak Ave EvanstonIL60201 United States Center for Photonic Communication and Computing Department of Electrical and Computer Engineering McCormick School of Engineering and Applied Science Northwestern University 2145 Sheridan Road EvanstonIL60208-3118 United States Division of Physics Mathematics and Astronomy California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States Fermi National Accelerator Laboratory Kirk Rd and Pine St BataviaIL60510 United States Department of Chemistry Physics and Engineering Studies Chicago State University 9501 S. King Dr. ChicagoIL60628 United States Pittsburgh Quantum Institute B4 Thaw Hall 4061 O'Hara Street PittsburghPA15260 United States John A. Paulson School of Engineering and Applied Sciences Harvard University 29 Oxford St. CambridgeMA02138 United States 2205 Tech Drive Suite 1-160 EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208-3112 United States

The Illinois Express Quantum Network (IEQNET) is a program to realize metro-scale quantum networking over deployed optical fiber using currently available technology. IEQNET consists of multiple sites that are geographically dispersed in the Chicago metropolitan area. Each site has one or more quantum nodes (Q-nodes) representing the communication parties in a quantum network. Q-nodes generate or measure quantum signals such as entangled photons and communicate the results via standard, classical, means. The entangled photons in IEQNET nodes are generated at multiple wavelengths, and are selectively distributed to the desired users via optical switches. Here we describe the network architecture of IEQNET, including the Internet-inspired layered hierarchy that leverages software-defined-networking (SDN) technology to perform traditional wavelength routing and assignment between the Q-nodes. Specifically, SDN decouples the control and data planes, with the control plane being entirely classical. Issues associated with synchronization, calibration, network monitoring, and scheduling will be discussed. An important goal of IEQNET is demonstrating the extent to which the control plane can coexist with the data plane using the same fiber lines. This goal is furthered by the use of tunable narrow-band optical filtering at the receivers and, at least in some cases, a wide wavelength separation between the quantum and classical channels. We envision IEQNET to aid in developing robust and practical quantum networks by demonstrating metro-scale quantum communication tasks such as entanglement distribution and quantum-state teleportation. © 2021, CC BY.

关键词： Optical fibers

Roadmap on Electronic Structure Codes in the Exascale Era

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arXiv 2022年

作者： Gavini, Vikram Baroni, Stefano Blum, Volker Bowler, David R. Buccheri, Alexander Chelikowsky, James R. Das, Sambit Dawson, William Delugas, Pietro Dogan, Mehmet Draxl, Claudia Galli, Giulia Genovese, Luigi Giannozzi, Paolo Giantomassi, Matteo Gonze, Xavier Govoni, Marco Gulans, Andris Gygi, François Herbert, John M. Kokott, Sebastian Kühne, Thomas D. Liou, Kai-Hsin Miyazaki, Tsuyoshi Motamarri, Phani Nakata, Ayako Pask, John E. Plessl, Christian Ratcliff, Laura E. Richard, Ryan M. Rossi, Mariana Schade, Robert Scheffler, Matthias Schütt, Ole Suryanarayana, Phanish Torrent, Marc Truflandier, Lionel Windus, Theresa L. Xu, Qimen Yu, Victor W.-Z. Perez, Danny Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States SISSA ISAS Trieste Trieste34136 Italy IOM-CNR Trieste Trieste34136 Italy Duke University DurhamNC27708 United States London Centre for Nanotechnology LondonWC1H 0AH United Kingdom WPI-MANA National Institute for Materials Science Ibaraki305-0044 Japan Humboldt-Universität zu Berlin Berlin10117 Germany McKetta Department of Chemical Engineering University of Texas at Austin AustinTX78712 United States Center for Computational Materials Oden Institute University of Texas at Austin AustinTX78712 United States Department of Physics University of Texas at Austin AustinTX78712 United States RIKEN Center for Computational Science Hyogo Kobe650-0047 Japan Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Materials Science Division Argonne National Laboratory LemontIL60439 United States Université Grenoble Alpes MEM L_Sim GrenobleF-38000 France University of Udine Udine33100 Italy IMCN Université catholique de Louvain Louvain-la-neuve Belgium University of Latvia RīgaLV-1586 Latvia University of California DavisCA95616 United States Department of Chemistry & Biochemistry The Ohio State University ColumbusOH43210 United States Berlin14195 Germany Dynamics of Condensed Matter Center for Sustainable Systems Design Chair of Theoretical Chemistry Paderborn University D-33098 Germany Paderborn Center for Parallel Computing Paderborn University D-33098 Germany Department of Computational and Data Sciences Indian Institute of Science Karnataka Bengaluru560012 India Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Computer Science Paderborn University D-33098 Germany Centre for Computational Chemistry School of Chemistry University of Bristol BristolBS8 1TS United Kingdom Ames National Laboratory AmesIA50011 United States Iowa State University AmesIA50

Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Electronic structure

Detecting social influence in event cascades by comparing discriminative rankers a preprint

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arXiv 2018年

作者： Soni, Sandeep Ramirez, Shaun Ling Eisenstein, Jacob School of Interactive Computing Georgia Institute of Technology AtlantaGA30308 United States Insight Data Science SeattleWA98104 United States

The global dynamics of event cascades are often governed by the local dynamics of peer influence. However, detecting social influence from observational data is challenging due to confounds like homophily and practical issues like missing data. We propose a simple discriminative method to detect influence from observational data. The core of the approach is to train a ranking algorithm to predict the source of the next event in a cascade, and compare its out-of-sample accuracy against a competitive baseline which lacks access to features corresponding to social influence. We analyze synthetically generated data to show that this method correctly identifies influence in the presence of confounds, and is robust to both missing data and misspecification - unlike well-known alternatives. We apply the method to two real-world datasets: (1) the co-sponsorship of legislation in the U.S. House of Representatives on a social network of shared campaign donors;(2) rumors about the Higgs boson discovery on a follower network of 105 Twitter accounts. Our model identifies the role of social influence in these scenarios and uses it to make more accurate predictions about the future trajectory of cascades. Copyright © 2018, The Authors. All rights reserved.

关键词： Bosons

The Young Supernova Experiment data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae

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arXiv 2022年

作者： Aleo, P.D. Malanchev, K. Sharief, S. Jones, D.O. Narayan, G. Foley, R.J. Villar, V.A. Angus, C.R. Baldassare, V.F. Bustamante-Rosell, M.J. Chatterjee, D. Cold, C. Coulter, D.A. Davis, K.W. Dhawan, S. Drout, M.R. Engel, A. French, K.D. Gagliano, A. Gall, C. Hjorth, J. Huber, M.E. Jacobson-Galán, W.V. Kilpatrick, C.D. Langeroodi, D. Macias, P. Mandel, K.S. Margutti, R. Matasić, F. McGill, P. Pierel, J.D.R. Ramirez-Ruiz, E. Ransome, C.L. Rojas-Bravo, C. Siebert, M.R. Smith, K.W. de Soto, K.M. Stroh, M.C. Tinyanont, S. Taggart, K. Ward, S.M. Wojtak, R. Auchettl, K. Blanchard, P.K. de Boer, T.J.L. Boyd, B.M. Carroll, C.M. Chambers, K.C. DeMarchi, L. Dimitriadis, G. Dodd, S.A. Earl, N. Farias, D. Gao, H. Gomez, S. Grayling, M. Grillo, C. Hayes, E.E. Hung, T. Izzo, L. Khetan, N. Kolborg, A.N. Law-Smith, J.A.P. LeBaron, N. Lin, C.-C. Luo, Y. Magnier, E.A. Matthews, D. Mockler, B. O’Grady, A.J.G. Pan, Y.-C. Politsch, C.A. Raimundo, S.I. Rest, A. Ridden-Harper, R. Sarangi, A. Schrøder, S.L. Smartt, S.J. Terreran, G. Thorp, S. Vazquez, J. Wainscoat, R.J. Wang, Q. Wasserman, A.R. Yadavalli, S.K. Yarza, R. Zenati, Y. Department of Astronomy University of Illinois at Urbana-Champaign 1002 W. Green St. IL61801 United States Fellow United States Center for AstroPhysical Surveys National Center for Supercomputing Applications UrbanaIL61801 United States Sternberg Astronomical Institute Lomonosov Moscow State University 13 Universitetsky pr. Moscow119234 Russia Department of Astronomy and Astrophysics University of California Santa CruzCA95064 United States Gemini Observatory NSF’s NOIRLab 670 N. A’ohoku Place HiloHI96720 United States Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States DARK Niels Bohr Institute University of Copenhagen Jagtvej 128 Copenhagen2200 Denmark Department of Physics & Astronomy Washington State University PullmanWA99164 United States LIGO Laboratory Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology 185 Albany St CambridgeMA02139 United States Institute of Astronomy Kavli Institute for Cosmology Madingley Road CambridgeCB3 0HA United Kingdom Department of Astronomy and Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada NSF Graduate Fellow Institute for Astronomy University of Hawaii 2680 Woodlawn Drive HonoluluHI96822 United States Department of Astronomy University of California BerkeleyCA94720 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Space Telescope Science Institute BaltimoreMD21218 United States Astrophysics Research Centre School of Mathematics and Physics Queen’s University Belfast BelfastBT7 1NN United Kingdom School of Physics The University of Melbourne VIC3010 Australia Australia LSSTC Cata

We present the Young Supernova Experiment data Release 1 (YSE DR1), comprised of processed multi-color Pan-STARRS1 (PS1) griz and Zwicky Transient Facility (ZTF) gr photometry of 1975 transients with host-galaxy associations, redshifts, spectroscopic/photometric classifications, and additional data products from 2019 November 24 to 2021 December 20. YSE DR1 spans discoveries and observations from young and fast-rising supernovae (SNe) to transients that persist for over a year, with a redshift distribution reaching z ≈ 0.5. We present relative SN rates from YSE’s magnitude- and volume-limited surveys, which are consistent with previously published values within estimated uncertainties for untargeted surveys. We combine YSE and ZTF data, and create multi-survey SN simulations to train the ParSNIP and SuperRAENN photometric classification algorithms;when validating our ParSNIP classifier on 472 spectroscopically classified YSE DR1 SNe, we achieve 82% accuracy across three SN classes (SNe Ia, II, Ib/Ic) and 90% accuracy across two SN classes (SNe Ia, core-collapse SNe). Our classifier performs particularly well on SNe Ia, with high (>90%) individual completeness and purity, which will help build an anchor photometric SNe Ia sample for cosmology. We then use our photometric classifier to characterize our photometric sample of 1483 SNe, labeling 1048 (∼71%) SNe Ia, 339 (∼23%) SNe II, and 96 (∼6%) SNe Ib/Ic. YSE DR1 provides a training ground for building discovery, anomaly detection, and classification algorithms, performing cosmological analyses, understanding the nature of red and rare transients, exploring tidal disruption events and nuclear variability, and preparing for the forthcoming Vera C. Rubin Observatory Legacy Survey of Space and Time. © 2022, CC BY.

关键词： Supernovae

Double-precision FPUs in high-performance computing: An embarrassment of riches?

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arXiv 2018年

作者： Domke, Jens Matsumura, Kazuaki Wahib, Mohamed Zhang, Haoyu Yashima, Keita Tsuchikawa, Toshiki Tsuji, Yohei Podobas, Artur Matsuoka, Satoshi Global Scientific Information and Computing Center Tokyo Institute of Technology Department of Mathematical and Computing Science Tokyo Institute of Technology AIST-TokyoTech Real World Big-Data Computation Open Innovation Laboratory Tokyo Japan RIKEN Japan

Among the (uncontended) common wisdom in High-Performance computing (HPC) is the applications' need for large amount of double-precision support in hardware. Hardware manufacturers, the TOP500 list, and (rarely revisited) legacy software have without doubt followed and contributed to this view. In this paper, we challenge that wisdom, and we do so by exhaustively comparing a large number of HPC proxy applications on two processors: Intel's Knights Landing (KNL) and Knights Mill (KNM). Although similar, the KNL and KNM architecturally deviate at one important point: the silicon area devoted to double-precision arithmetics. This fortunate discrepancy allows us to empirically quantify the performance impact in reducing the amount of hardware double-precision arithmetic. Our analysis shows that this common wisdom might not always be right. We find that the investigated HPC proxy applications do allow for a (significant) reduction in double-precision with little-to-no performance implications. With the advent of a failing of Moore's law, our results partially reinforce the view taken by modern industry (e.g., upcoming Fujitsu ARM64FX) to integrate hybrid-precision hardware units. Copyright © 2018, The Authors. All rights reserved.

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Multi-Thread Concurrent Compression Algorithm for Genomic Big data

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Multi-Thread Concurrent Compression Algorithm for Genomic Bi...

IEEE International Conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Yimu Ji Shuai Chen Haichang Yao Houzhi Fang Kui Li Shangdong Liu Zhengyuan Xie Kairui Wang School of Computer Science Nanjing University of Posts and Telecommunications Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing Center of HPC China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications School of Computer and Software Nanjing Institute of Industry Technology Nanjing China University of Toronto Toronto Canada BSc Mathematics with Finance University of Liverpool Liverpool United Kingdom

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

At present, there are many excellent genome compression algorithms with high genome compression ratio. However, there is a lack of highly efficient compression algorithms for simultaneous compression of a large number of genomes. This manuscript presents an algorithm, which is called FastLNGC, for simultaneous compression of a large amount of genome data based on multi-thread concurrency. This algorithm is based on the LNGC (Large Number of Genomes Compressor) algorithm, and adopts multi-thread technology to achieve concurrent processing of genome data compression. A large number of experiments show that FastLNGC has better performance on compression of a large number of genes. The source code of FastLNGC is available at https://***/APandaThief/FastLNGC.

关键词： Bioinformatics Genomics Compression algorithms Instruction sets Telecommunications Computer science Concurrent computing

Balanced distribution adaptation for transfer learning

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arXiv 2018年

作者： Wang, Jindong Chen, Yiqiang Hao, Shuji Feng, Wenjie Shenk, Zhiqi Beijing Key Laboratory of Mobile Computing and Pervasive Device CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Institute of High Performance Computing A*STAR School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

Transfer learning has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. However, these two distances are often treated equally in existing algorithms, which will result in poor performance in real applications. Moreover, existing methods usually assume that the dataset is balanced, which also limits their performances on imbalanced tasks that are quite common in real problems. To tackle the distribution adaptation problem, in this paper, we propose a novel transfer learning approach, named as Balanced Distribution Adaptation (BDA), which can adaptively leverage the importance of the marginal and conditional distribution discrepancies, and several existing methods can be treated as special cases of BDA. Based on BDA, we also propose a novel Weighted Balanced Distribution Adaptation (W-BDA) algorithm to tackle the class imbalance issue in transfer learning. W-BDA not only considers the distribution adaptation between domains but also adaptively changes the weight of each class. To evaluate the proposed methods, we conduct extensive experiments on several transfer learning tasks, which demonstrate the effectiveness of our proposed algorithms over several state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Learning algorithms

Cell membrane vaccine delivery system: A review of the recent advances against breast cancer

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International Journal of Pharmaceutics 2025年 681卷

作者： Ayah Rebhi Hilles Syed Mahmood Riyanto Teguh Widodo Ahmad Khusairi Azemi Abu Hassan Nordin Abdin Shakirin Mohamad Norpi Muhammad Fauzi Abd Jalil Zarif Mohamed Sofian Mazlan Mohamed Kamarul Ariffin Hambali Nani Izreen Mohd Sani Roslina Rashid Muhammad Luqman Nordin INHART International Islamic University Malaysia Jalan Gombak 53100 Kuala Lumpur Malaysia Department of Pharmaceutical Technology Faculty of Pharmacy Universiti Malaya 50603 Kuala Lumpur Malaysia Universiti Malaya Research Centre for Pharmaceuticals and Advanced Therapeutics (UBAT) Department of Pharmacology Faculty of Medicine Universiti Malaya 50603 Kuala Lumpur Malaysia Centre of Advanced Materials (CAM) Faculty of Engineering Universiti Malaya 50603 Kuala Lumpur Malaysia Faculty of Pharmaceutical Sciences Chulalongkorn University Pathum Wan Bangkok 10330 Thailand Institute of Climate Change Adaptation and Marine Biotechnology Universiti Malaysia Terengganu 21030 Kuala Terengganu Terengganu Malaysia Faculty of Applied Sciences Universiti Teknologi MARA (UiTM) Campus Arau 02600 Perlis Malaysia Faculty of Pharmacy and Health Sciences Universiti Kuala Lumpur Royal College of Medicine Perak Perak 30450 Malaysia Faculty of Data Science and Computing Universiti Malaysia Kelantan City Campus Pengkalan Chepa Kota Bharu 16100 Kelantan Malaysia Faculty of Earth Science Universiti Malaysia Kelantan Jeli 17600 Kelantan Malaysia Animal and Wildlife Research Group Faculty of Earth Science Jeli Campus Universiti Malaysia Kelantan Jeli 17600 Kelantan Malaysia Faculty of Veterinary Medicine Universiti Malaysia Kelantan Pengkalan Chepa 16100 Kelantan Malaysia

Natural cells exhibit unique properties, such as homotypic targeting and prolonged circulation in the bloodstream, making them attractive candidates for breast cancer therapy vaccine delivery systems (VDSs). To improve delivery efficiency, these cells can be integrated with nanocarriers, paving the way for next-generation VDSs in the form of biomimetic nanomedicines. This review highlights recent progress in cell membrane-based vaccine delivery systems (CVDSs) and other types of vaccine delivery systems as a potential for breast cancer vaccine development. Cell membrane sources, including red blood cells (RBCs), cancer cells, and platelets are explored. Additionally, the methods for extracting these membranes and the mechanisms driving the functionality of biomimetic CVDSs are detailed. This review also highlights recent advancements and cutting-edge strategies of cell membrane vaccine delivery systems, with a particular focus on the use of cell-derived biomimetic nanotechnology for breast cancer. It also highlights breast cancer vaccines under development encompass protein-based (e.g., peptide vaccines), nucleic acid-based (DNA and RNA vaccines), immune cell-based (including dendritic cell and autologous tumor cell vaccines), and bacterial/viral vector-based vaccines. Each platform aims to elicit targeted anti-tumor immune responses.

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