From 70f2f7bf11fab89653d0805b4c04b5656f8dce3c Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Mon, 26 Feb 2024 11:22:30 -0800 Subject: [PATCH] chore(openchallenges): 2024-02-26 DB update (#2532) Co-authored-by: vpchung <9377970+vpchung@users.noreply.github.com> --- .../service/service/ChallengeAnalyticsService.java | 2 +- .../src/main/resources/db/categories.csv | 4 ++++ .../src/main/resources/db/challenges.csv | 10 +++++----- 3 files changed, 10 insertions(+), 6 deletions(-) diff --git a/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/service/ChallengeAnalyticsService.java b/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/service/ChallengeAnalyticsService.java index 61c3d386e0..0c922141a7 100644 --- a/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/service/ChallengeAnalyticsService.java +++ b/apps/openchallenges/challenge-service/src/main/java/org/sagebionetworks/openchallenges/challenge/service/service/ChallengeAnalyticsService.java @@ -19,7 +19,7 @@ public ChallengesPerYearDto getChallengesPerYear() { // The following line will be auto-updated by a script and should NOT be modified manually. List challengeCounts = /* AUTO-UPDATE MARKER */ - Arrays.asList(6, 9, 13, 17, 23, 29, 34, 41, 49, 59, 86, 97, 116, 135, 184, 243, 305, 319); + Arrays.asList(6, 9, 13, 17, 23, 29, 34, 41, 49, 59, 86, 97, 116, 135, 184, 243, 305, 320); Integer undatedChallengeCount = 171; // int currentYear = Year.now().getValue(); diff --git a/apps/openchallenges/challenge-service/src/main/resources/db/categories.csv b/apps/openchallenges/challenge-service/src/main/resources/db/categories.csv index c3d979a043..5e85df8b3c 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/categories.csv +++ b/apps/openchallenges/challenge-service/src/main/resources/db/categories.csv @@ -14,3 +14,7 @@ "13","487","hackathon" "14","491","hackathon" "15","497","hackathon" +"16","158","benchmark" +"17","457","benchmark" +"18","458","benchmark" +"19","462","benchmark" diff --git a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv index 46eb31fd8b..8861b2b132 100644 --- a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv +++ b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv @@ -181,7 +181,7 @@ "180","lish-moa","Mechanisms of Action (MoA) Prediction","Improve the algorithm that classifies drugs based on their biological activity","Can you improve the algorithm that classifies drugs based on their biological activity?","","https://www.kaggle.com/competitions/lish-moa","completed","8","","2020-09-03","2020-11-30","2023-08-08 19:09:31","2023-11-14 19:33:49" "181","recursion-cellular-image-classification","Recursion Cellular Image Classification","CellSignal-Disentangling biological signal in cellular images","This competition will have you disentangling experimental noise from real biological signals. Your entry will classify images of cells under one of 1,108 different genetic perturbations. You can help eliminate the noise introduced by technical execution and environmental variation between experiments. If successful, you could dramatically improve the industry’s ability to model cellular images according to their relevant biology. In turn, applying AI could greatly decrease the cost of treatments, and ensure these treatments get to patients faster.","","https://www.kaggle.com/competitions/recursion-cellular-image-classification","completed","8","","2019-06-27","2019-09-26","2023-08-08 19:38:42","2023-11-14 19:34:11" "182","tlvmc-parkinsons-freezing-gait-prediction","Parkinson's Freezing of Gait Prediction","Event detection from wearable sensor data","The goal of this competition is to detect freezing of gait (FOG), a debilitating symptom that afflicts many people with Parkinson’s disease. You will develop a machine learning model trained on data collected from a wearable 3D lower back sensor. Your work will help researchers better understand when and why FOG episodes occur. This will improve the ability of medical professionals to optimally evaluate, monitor, and ultimately, prevent FOG events.","","https://www.kaggle.com/competitions/tlvmc-parkinsons-freezing-gait-prediction","completed","8","","2023-03-09","2023-06-08","2023-08-08 19:47:54","2023-10-10 19:53:08" -"183","chaimeleon","CHAIMELEON Open Challenges","AI-powered solutions driving innovation in cancer diagnosis and treatment","The CHAIMELEON Open Challenges is a competition designed to train and refine AI models to answer clinical questions about five types of cancer-prostate, lung, breast, colon, and rectal. Participants are challenged to collaborate and develop innovative AI-powered solutions that can significantly impact cancer diagnosis, management, and treatment. They will be evaluated considering a balance between the performance of their AI algorithms to predict different clinical endpoints such as disease staging, treatment response or progression free survival and their trustworthiness. The challenges are open to the whole scientific and tech community interested in AI. They are a unique opportunity to showcase how AI can be used to advance medical research and improve patient outcomes within the CHAIMELEON project.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/744/Logo_Grand_Challenge_-_2.png","https://chaimeleon.grand-challenge.org/","completed","5","","2023-11-02","2023-11-30","2023-08-09 17:13:09","2023-11-14 19:34:50" +"183","chaimeleon","CHAIMELEON Open Challenges","AI-powered solutions driving innovation in cancer diagnosis and treatment","The CHAIMELEON Open Challenges is a competition designed to train and refine AI models to answer clinical questions about five types of cancer-prostate, lung, breast, colon, and rectal. Participants are challenged to collaborate and develop innovative AI-powered solutions that can significantly impact cancer diagnosis, management, and treatment. They will be evaluated considering a balance between the performance of their AI algorithms to predict different clinical endpoints such as disease staging, treatment response or progression free survival and their trustworthiness. The challenges are open to the whole scientific and tech community interested in AI. They are a unique opportunity to showcase how AI can be used to advance medical research and improve patient outcomes within the CHAIMELEON project.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/744/Logo_Grand_Challenge_-_2.png","https://chaimeleon.grand-challenge.org/","active","5","","2023-11-02","2024-02-29","2023-08-09 17:13:09","2024-02-26 19:18:26" "184","topcow23","Topology-Aware Anatomical Segmentation of the Circle of Willis","Segment the Circle of Willis (CoW) vessel components for both CTA and MRA","The aim of the challenge is to extract the CoW angio-architecture from 3D angiographic imaging by segmentation of the vessel components. There are two sub-tasks-binary segmentation of CoW vessels, and multi-class CoW anatomical segmentation. We release a new dataset of joint-modalities, CTA and MRA of the same patient cohort, both with annotations of the anatomy of CoW. Our challenge has two tracks for the same segmentation task, namely CTA track and MRA track. We made use of the clinical information from both modalities during our annotation. And participants can pick whichever modality they want, both CTA and MRA, and choose to tackle the task for either modality.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/733/TopCow_logo.jpg","https://topcow23.grand-challenge.org/","completed","5","","2023-08-20","2023-09-25","2023-08-09 17:16:22","2024-01-31 22:42:32" "185","circle-of-willis-intracranial-artery-classification-and-quantification-challenge-2023","Circle of Willis Intracranial Artery Classification and Quantification Challenge 2023","Classify the circle of Willis (CoW) configuration and quantification","The purpose of this challenge is to compare automatic methods for classification of the circle of Willis (CoW) configuration and quantification of the CoW major artery diameters and bifurcation angles.","","https://crown.isi.uu.nl/","completed","\N","","2023-05-01","2023-08-15","2023-08-09 22:13:24","2023-09-28 23:24:54" "186","making-sense-of-electronic-health-record-ehr-race-and-ethnicity-data","Making Sense of Electronic Health Record (EHR) Race and Ethnicity Data","Make sense of electronic health record race and ethnicity data","The urgency of the coronavirus disease 2019 (COVID-19) pandemic has heightened interest in the use of real-world data (RWD) to obtain timely information about patients and populations and has focused attention on EHRs. The pandemic has also heightened awareness of long-standing racial and ethnic health disparities along a continuum from underlying social determinants of health, exposure to risk, access to insurance and care, quality of care, and responses to treatments. This highlighted the potential that EHRs can be used to describe and contribute to our understanding of racial and ethnic health disparities and their solutions. The OMB Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity provides minimum standards for maintaining, collecting, and presenting data on race and ethnicity for all Federal reporting purposes, and defines the two separate constructs of race and ethnicity.","","https://precision.fda.gov/challenges/30","completed","6","","2023-05-31","2023-06-23","2023-08-10 18:28:06","2023-11-14 19:34:58" @@ -433,7 +433,7 @@ "432","auto-rtp","Fully Automated Radiotherapy Treatment Planning Challenge","Automated radiotherapy treatment planning in prostate cancer","Participants will be provided with simulation CTs for ten prostate cancer patients, together with a treatment intent/prescription (in a machine readable format). The cases will be a mix of prostate only and prostate + nodes. Participants are asked to generate a treatment plan in an as-automated-as-possible way, including contouring and plan generation. No manual intervention on contouring or planning should be performed, but manual steps to transfer data between systems are permitted if required. Freedom is given to participants with respect to the ""treatment machine"" the plan is designed for. However, it is expected that all participants produce a plan that is deliverable in clinically reasonable time frame.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/713/AUTO-RTP_Logo.png","https://auto-rtp.grand-challenge.org/","active","5","","2023-06-05","\N","2023-11-08 00:42:00","2023-11-16 17:39:25" "433","2023paip","PAIP 2023: TC prediction in pancreatic and colon cancer","Tumor cellularity prediction in pancreatic and colon cancer","Tumor cellularity (TC) is used to compute the residual tumor burden in several organs, such as the breast and colon. The TC is measured based on semantic cell segmentation, which accurately classifies and delineates individual cells. However, manual analysis of TC is impractical in clinics because of the large volumes of pathological images and is unreliable owing to inconsistent TC values among pathologists. Essentially, tumor cellularity should be calculated by individual cell counting; however, manual counting is impossible, and human pathologists cannot avoid individual differences in diagnostic performance. Automatic image analysis is the ideal method for solving this problem, and it can efficiently reduce the workload of pathologists.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/716/PAIP2023-640.png","https://2023paip.grand-challenge.org/","active","5","","2023-02-15","\N","2023-11-08 00:42:00","2023-11-16 17:39:26" "434","snemi3d","SNEMI3D: 3D Segmentation of neurites in EM images","IEEE ISBI 2013 challenge: multimodal segmentation","In this challenge, a full stack of electron microscopy (EM) slices will be used to train machine-learning algorithms for the purpose of automatic segmentation of neurites in 3D. This imaging technique visualizes the resulting volumes in a highly anisotropic way, i.e., the x- and y-directions have a high resolution, whereas the z-direction has a low resolution, primarily dependent on the precision of serial cutting. EM produces the images as a projection of the whole section, so some of the neural membranes that are not orthogonal to a cutting plane can appear very blurred. None of these problems led to major difficulties in the manual labeling of each neurite in the image stack by an expert human neuro-anatomist.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/717/logo.png","https://snemi3d.grand-challenge.org/","active","5","","2013-01-15","\N","2023-11-08 00:42:00","2023-11-16 17:39:27" -"435","han-seg2023","The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge","Endometrial carcinoma prediction on whole-slide images","Cancer in the region of the head and neck (HaN) is one of the most prominent cancers, for which radiotherapy represents an important treatment modality that aims to deliver a high radiation dose to the targeted cancerous cells while sparing the nearby healthy organs-at-risk (OARs). A precise three-dimensional spatial description, i.e. segmentation, of the target volumes as well as OARs is required for optimal radiation dose distribution calculation, which is primarily performed using computed tomography (CT) images. However, the HaN region contains many OARs that are poorly visible in CT, but better visible in magnetic resonance (MR) images. Although attempts have been made towards the segmentation of OARs from MR images, so far there has been no evaluation of the impact the combined analysis of CT and MR images has on the segmentation of OARs in the HaN region. The Head and Neck Organ-at-Risk Multi-Modal Segmentation Challenge aims to promote the development of new and applicatio...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/718/logo.jpg","https://han-seg2023.grand-challenge.org/","completed","5","","2023-03-26","2024-02-17","2023-11-08 00:42:00","2024-01-31 22:38:41" +"435","han-seg2023","The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge","Endometrial carcinoma prediction on whole-slide images","Cancer in the region of the head and neck (HaN) is one of the most prominent cancers, for which radiotherapy represents an important treatment modality that aims to deliver a high radiation dose to the targeted cancerous cells while sparing the nearby healthy organs-at-risk (OARs). A precise three-dimensional spatial description, i.e. segmentation, of the target volumes as well as OARs is required for optimal radiation dose distribution calculation, which is primarily performed using computed tomography (CT) images. However, the HaN region contains many OARs that are poorly visible in CT, but better visible in magnetic resonance (MR) images. Although attempts have been made towards the segmentation of OARs from MR images, so far there has been no evaluation of the impact the combined analysis of CT and MR images has on the segmentation of OARs in the HaN region. The Head and Neck Organ-at-Risk Multi-Modal Segmentation Challenge aims to promote the development of new and applicatio...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/718/logo.jpg","https://han-seg2023.grand-challenge.org/","active","5","","2023-03-26","\N","2023-11-08 00:42:00","2024-02-26 19:19:00" "436","endo-aid","Endometrial Carcinoma Detection in Pipelle biopsies","Non-rigid registration challenge for expansion microscopy","Evaluation platform as reference benchmark for algorithms that can predict endometrial carcinoma on whole-slide images of Pipelle sampled endometrial slides stained in H&E, based on the test data set used in our project.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/719/logo-challenge.png","https://endo-aid.grand-challenge.org/","active","5","","\N","\N","2023-11-08 00:42:00","2024-01-31 22:33:53" "437","rnr-exm","Robust Non-rigid Registration Challenge for Expansion Microscopy","Xray projectomic reconstruction with skeleton segmentation","Despite the wide adoption of ExM, there are few public benchmarks to evaluate the registration pipeline, which limits the development of robust methods for real-world deployment. To address this issue, we have launched RnR-ExM, a challenge that releases 24 pairs of 3D image volumes from three different species. Participants are asked to align these pairs and submit dense deformation fields for assessment. Half of the volume pairs (the validation and test set) have annotated cell structures (nuclei, blood vessels) as registration landmarks.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/720/RnR-ExM_Logo.png","https://rnr-exm.grand-challenge.org/","active","5","","2023-02-17","2028-03-16","2023-11-08 00:42:00","2023-11-16 17:39:32" "438","xpress","Xray Projectomic Reconstruction Extracting Segment with Skeleton","Automated lesion segmentation in PET/CT - domain generalization","In this task, we provide volumetric XNH images of cortical white matter axons from the mouse brain at 100 nm per voxel isotropic resolution. Additionally, we provide ground truth annotations for axon trajectories. Manual voxel-wise annotation of ground truth is a time-consuming bottleneck for training segmentation networks. On the other hand, skeleton-based ground truth is much faster to annotate, and sufficient to determine connectivity. Therefore, we encourage participants to develop methods to leverage skeleton-based training. To this end, we provide two types of training (validation) sets: a small volume of voxel-wise annotations and a larger volume with skeleton-based annotations. The participants will have the flexibility to use either or both of the provided annotations to train their models, and are challenged to submit an accurate voxel-wise prediction on the test volume. Entries will be evaluated on how accurately the submitted segmentations agree with the ground-truth s...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/721/XPRESS_logo_sq2-01.png","https://xpress.grand-challenge.org/","active","5","","2023-02-06","\N","2023-11-08 00:42:00","2023-11-16 17:39:34" @@ -460,7 +460,7 @@ "459","cameo-3d-modeling-of-structures-and-complexes-beta","CAMEO-3D: Modeling of Structures & Complexes - BETA","Protein structure prediction quality assessment","Cameo Continuously Applies Quality Assessment Criteria Established By The Protein Structure Prediction Community. Since The Accuracy Requirements For Different Scientific Applications Vary, There Is No ""One Fits All"" Score. Cameo Therefore Offers A Variety Of Scores - Assessing Different Aspects Of A Prediction (Coverage, Local Accuracy, Completeness, Etc.) To Reflect These Requirements.","","https://beta.cameo3d.org/complete-modeling/","active","18","","2023-11-04","\N","2023-11-11 01:29:20","2023-11-16 22:41:58" "460","pegs-dream-challenge","PEGS DREAM Challenge","","","","https://www.synapse.org/pegs","upcoming","1","","\N","\N","2023-11-13 22:48:02","2023-11-16 16:20:18" "461","fda-data-centric-challenge","FDA Data-Centric Challenge","","The Food and Drug Administration (FDA) - Center for Devices and Radiological Health (CDRH), Sage Bionetworks, and precisionFDA call on the scientific, industry, and data science communities to develop methods to augment the training data and improve the robustness of a baseline artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD).","","https://www.synapse.org/fda_data_centric","upcoming","1","","\N","\N","2023-11-13 22:49:41","2023-12-12 19:02:40" -"462","ai-institute-for-dynamic-systems","AI Institute for Dynamic Systems","","","","https://www.synapse.org/#!Synapse:syn52052735","upcoming","1","","\N","\N","2023-11-13 22:51:53","2023-11-17 0:13:33" +"462","ai-institute-for-dynamic-systems","AI Institute for Dynamic Systems","","","","https://www.synapse.org/#!Synapse:syn52052735","active","1","","2024-02-21","\N","2023-11-13 22:51:53","2024-02-26 19:13:25" "463","competition-nih-alzheimers-adrd-1","PREPARE Phase 1 - Find IT!","Help NIH break new ground in early Alzheimer's prediction and related dementias","The goal of the PREPARE Challenge (Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge) is to inform novel approaches to early detection that might ultimately lead to more accurate tests, tools, and methodologies for clinical and research purposes. Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for the prediction of biological, psychological (cognitive), socio-behavioral, functional, and clinical changes related to AD/ADRD. This first phase, Find IT!: Data for Early Prediction, is focused on finding, curating, or contributing data to create representative and open datasets that can be used for the early prediction of AD/ADRD.","","https://www.drivendata.org/competitions/253/competition-nih-alzheimers-adrd-1/","completed","19","","2023-09-01","2024-01-31","2023-11-16 21:57:03","2023-12-06 7:15:18" "464","prepare-phase-2-build-it","PREPARE Phase 2 - Build IT!","Help NIH break new ground in early Alzheimer's prediction and related dementias","The goal of the PREPARE Challenge (Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge) is to inform novel approaches to early detection that might ultimately lead to more accurate tests, tools, and methodologies for clinical and research purposes. Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for the prediction of biological, psychological (cognitive), socio-behavioral, functional, and clinical changes related to AD/ADRD. This second phase, Build IT!: Algorithms and Approaches, is focused on advancing algorithms and analytic approaches for early prediction of AD/ADRD, with an emphasis on explainability of predictions.","","","upcoming","19","","2024-09-01","\N","2023-11-17 00:09:25","2023-12-06 7:18:18" "465","prepare-phase-3-put-it-all-together","PREPARE Phase 3 - Put IT All Together!","Help NIH break new ground in early Alzheimer's prediction and related dementias","The goal of the PREPARE Challenge (Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA Challenge) is to inform novel approaches to early detection that might ultimately lead to more accurate tests, tools, and methodologies for clinical and research purposes. Advances in artificial intelligence (AI), machine learning (ML), and computing ecosystems increase possibilities of intelligent data collection and analysis, including better algorithms and methods that could be leveraged for the prediction of biological, psychological (cognitive), socio-behavioral, functional, and clinical changes related to AD/ADRD. This third phase, Put IT All Together!: Proof of Principle Demonstration, is for the top solvers from Phase 2 demonstrate algorithmic approaches on diverse datasets and share their results at an innovation event.","","","upcoming","19","","2025-03-01","\N","2023-11-17 00:09:26","2023-12-06 7:18:08" @@ -480,10 +480,10 @@ "479","vqa-answertherapy-2024","VQA-AnswerTherapy 2024","Grounding all answers for each visual question","Visual Question Answering (VQA) is a task of predicting the answer to a question about an image. Given that different people can provide different answers to a visual question, we aim to better understand why with answer groundings. To achieve this goal, we introduce the VQA-AnswerTherapy dataset, the first dataset that visually grounds each unique answer to each visual question. We offer this work as a valuable foundation for improving our understanding and handling of annotator differences. This work can inform how to account for annotator differences for other related tasks such as image captioning, visual dialog, and open-domain VQA (e.g., VQAs found on Yahoo!Answers and Stack Exchange). This work also contributes to ethical AI by enabling revisiting how VQA models are developed and evaluated to consider the diversity of plausible answer groundings rather than a single (typically majority) one.","https://evalai.s3.amazonaws.com/media/logos/e63bc0a0-cd35-4418-b32b-4ef2b9c61ce2.png","https://eval.ai/web/challenges/challenge-page/1910/overview","active","16","","2024-01-30","2199-12-26","2023-12-12 22:41:48","2024-01-31 23:05:00" "480","vqa-challenge-2021","VQA Challenge 2021","Answer open-ended, free-form natural language questions about images","Recent progress in computer vision and natural language processing has demonstrated that lower-level tasks are much closer to being solved. We believe that the time is ripe to pursue higher-level tasks, one of which is Visual Question Answering (VQA), where the goal is to be able to understand the semantics of scenes well enough to be able to answer open-ended, free-form natural language questions (asked by humans) about images. VQA Challenge 2021 is the 6th edition of the VQA Challenge on the VQA v2.0 dataset introduced in Goyal et al., CVPR 2017. The 2nd, 3rd, 4th and 5th editions of the VQA Challenge were organized in CVPR 2017, CVPR 2018, CVPR 2019 and CVPR 2020 on the VQA v2.0 dataset. The 1st edition of the VQA Challenge was organized in CVPR 2016 on the 1st edition (v1.0) of the VQA dataset introduced in Antol et al., ICCV 2015.","https://evalai.s3.amazonaws.com/media/logos/85d3b99e-b3a7-498a-a142-3325eab17138.png","https://eval.ai/web/challenges/challenge-page/830/overview","completed","16","","2021-02-24","2021-05-07","2023-12-12 22:42:59","2023-12-12 23:00:07" "481","ntx-hackathon-2023-sleep-states","NTX Hackathon 2023 - Sleep States","Speculate on possible use-cases of Neurotechnology and BCI","This competition is dedicated to advancing the use of machine learning and deep learning techniques in the realm of Brain-Computer Interface (BCI). It focuses on analyzing EEG data obtained from IDUN Guardian Earbuds. Electroencephalography (EEG) is a non-invasive method of recording electrical activity in the brain. Its high-resolution, real-time data is crucial in various clinical and consumer applications. In clinical environments, EEG is instrumental in diagnosing and monitoring neurological disorders like epilepsy, sleep disorders, and brain injuries. It's also used for assessing brain function in patients under anesthesia or in comas. The real-time aspect of EEG data is vital for clinicians to make informed decisions about diagnosis and treatment, such as pinpointing the onset and location of a seizure. Beyond clinical use, EEG has significant applications in understanding human cognition. Researchers utilize EEG to explore cognitive processes including attention, percepti...","https://miniodis-rproxy.lisn.upsaclay.fr/coda-v2-prod-public/logos/2023-12-02-1701542051/06a6dc054e4b/NTXHackathon23-Logo-Black-Blue-2048.png","https://www.codabench.org/competitions/1777/","completed","10","","2023-12-01","2023-12-15","2023-12-12 23:22:24","2023-12-12 23:30:24" -"482","ai2healhackathon","UF AI2Heal 2024","Rebooting medicine with next-gen artificial intelligence","AI2Heal 2024 aims to showcase innovative applications of AI and other emerging technologies, including but not limited to augmented, virtual, and other forms of extended reality (AR/VR/XR), digital twins, and internet of things (IOT), that highlight the potential of digital health solutions and next-generation AI-enabled healthcare to improve patient outcomes and address inefficiencies in the healthcare system. The AI2Heal 2024 Challenges fall under three primary Domains: 1) Building the Health Metaverse; 2) Smart Hospitals and Remote Healthcare Delivery; and 3) Intelligent Cognitive Support","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/704/686/datas/full_width.png","https://ai2healhackathon.devpost.com/","active","20","","2024-01-06","2024-02-24","2024-01-09 16:48:56","2024-01-31 22:52:19" +"482","ai2healhackathon","UF AI2Heal 2024","Rebooting medicine with next-gen artificial intelligence","AI2Heal 2024 aims to showcase innovative applications of AI and other emerging technologies, including but not limited to augmented, virtual, and other forms of extended reality (AR/VR/XR), digital twins, and internet of things (IOT), that highlight the potential of digital health solutions and next-generation AI-enabled healthcare to improve patient outcomes and address inefficiencies in the healthcare system. The AI2Heal 2024 Challenges fall under three primary Domains: 1) Building the Health Metaverse; 2) Smart Hospitals and Remote Healthcare Delivery; and 3) Intelligent Cognitive Support","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/704/686/datas/full_width.png","https://ai2healhackathon.devpost.com/","completed","20","","2024-01-06","2024-02-24","2024-01-09 16:48:56","2024-01-31 22:52:19" "483","data-hacks","Data Hacks: Equity in Healthcare","DataHacks with women in data science for middle and high schoolers","Hosted by CP Mentorship, 7EDU & Leadways School, Sponsored by WiDS - Women in Data Science & Microsoft, this one full day event is to support the worldwide Datathon competition, led by experts in data science fields. Winners will receive awards from Kaggle. Eligibility: middle to high schoolers (6th - 12th grade)","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/707/384/datas/full_width.png","https://data-hacks.devpost.com/","completed","20","","2024-01-26","2024-01-27","2024-01-09 16:58:53","2024-01-11 23:46:37" "484","mchacksnsbe","McHacks'24: Mario's Odyssey of Innovation","Spark change: innovate for mental health, education, feeding, and transport","Join our dynamic hackathon open to all students, regardless of major. This platform is designed to showcase your critical thinking and innovative prowess, enabling you to craft remarkable designs and comprehensive solutions for real-world challenges. Experience the power of collaboration as you team up with diverse talents, fostering an environment that promotes creative problem-solving. Beyond honing your skills, this hackathon serves as an ideal opportunity to engage with recruiters and companies, boosting your visibility within the workforce. Seize this avenue to shine, connect, and create a lasting impact. Your journey from idea to implementation starts here.","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/602/756/datas/full_width.jpg","https://mchacksnsbe.devpost.com/","completed","20","","2024-02-10","2024-02-10","2024-01-09 18:36:27","2024-01-11 23:46:46" -"485","ml-hacks-19785","ML Hacks","Create AI/ML applications to revolutionize the fields of science, art, and more","Machine learning/AI are tools that can be applied to almost every domain, from communications to health. In this hackathon, we want you, the participant, to find unqiue and inspiring ways to leverage machine learning & AI to make projects in these domains, and in doing so, change the world for the better.","","https://ml-hacks-19785.devpost.com/","active","20","","2024-02-16","2024-02-25","2024-01-09 18:45:21","2024-01-09 18:47:51" +"485","ml-hacks-19785","ML Hacks","Create AI/ML applications to revolutionize the fields of science, art, and more","Machine learning/AI are tools that can be applied to almost every domain, from communications to health. In this hackathon, we want you, the participant, to find unqiue and inspiring ways to leverage machine learning & AI to make projects in these domains, and in doing so, change the world for the better.","","https://ml-hacks-19785.devpost.com/","completed","20","","2024-02-16","2024-02-25","2024-01-09 18:45:21","2024-01-09 18:47:51" "486","frosthacks","Frost Hacks","Embark on a winter innovation odyssey","Welcome to Frost Hacks, Hyderabad's first 30-hour hackathon in collaboration with Major League Hacking (MLH) - an International Student League. Our mission is to cultivate a dynamic and collaborative environment students can immerse themselves in project-based learning, all while having an unforgettable time.","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/675/245/datas/full_width.png","https://frosthacks.devpost.com/","completed","20","","2024-01-20","2024-01-21","2024-01-09 18:48:59","2024-01-11 23:46:58" "487","ncit-hackfest-2024","NCIT Hackfest 2024","Lethargy breaking initiative driven to energize and engage NCIT students","HackFest 2024 is the intitiative from NOSK, Nepal Open Source Klub. HackFest NCIT 2024 is 72 hour long running hackathon will be held on January 11-13 , Hack Fest 2024 is the lethargy breaking initiative driven to energize and engage NCIT to take active role and participation in the Tech domain through innovative and competitive events, competitions, workshops and techno centric activities. We call for hackers, artists, coders, designers, tech evangelists, creatives, and developers from all disciplines to take a part to win awesome prizes and collaborate with other developers .","https://d112y698adiu2z.cloudfront.net/photos/production/challenge_photos/002/707/493/datas/full_width.png","https://ncit-hackfest-2024.devpost.com/","completed","20","","2024-01-11","2024-01-13","2024-01-09 18:51:10","2024-01-11 23:47:12" "488","spot-the-mask","Spot the Mask Challenge","Can you predict whether a person in an image is wearing a face mask?","Face masks have become a common public sight in the last few months. The Centers for Disease Control (CDC) recently advised the use of simple cloth face coverings to slow the spread of the virus and help people who may have the virus and do not know it from transmitting it to others. Wearing masks is broadly recognised as critical to reducing community transmission and limiting touching of the face. In a time of concern about slowing the transmission of COVID-19, increased surveillance combined with AI solutions can improve monitoring and reduce the human effort needed to limit the spread of this disease. The objective of this challenge is to create an image classification machine learning model to accurately predict the likelihood that an image contains a person wearing a face mask, or not. The total dataset contains 1,800+ images of people either wearing masks or not. Your machine learning solution will help policymakers, law enforcement, hospitals, and even commercial busines...","","https://zindi.africa/competitions/spot-the-mask","active","21","","\N","\N","2024-01-09 18:59:10","2024-01-09 19:08:11"