chore(openchallenges): 2024-04-03 DB update (#2610)

Co-authored-by: vpchung <9377970+vpchung@users.noreply.github.com>

apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv

@@ -497,7 +497,7 @@
 "496","lightmycells","Light My Cells: Bright Field to Fluorescence Imaging Challenge","Enhance biology and microscopy","Join the Light My Cells France-Bioimaging challenge! Enhance biology and microscopy by contributing to the development of new image-to-image deep labelling methods. The task: predict the best-focused output images of several fluorescently labelled organelles from label-free transmitted light input images. Dive into the future of imaging with us! #LightMyCellsChallenge","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/750/logo_light_my_cells.png","https://lightmycells.grand-challenge.org/","upcoming","5","","\N","\N","\N","2024-01-31 22:49:33","2024-02-05 16:58:06"
 "497","hack-rare-disease","Harvard Rare Disease Hackathon 2024","Are you a student interested in using AI/ML to tackle rare diseases? Join us!","This March 2-3, join us for the 2024 Harvard Rare Disease Hackathon, where students will gather on Harvard''s campus to set forth their own data-driven solutions for rare diseases.  Participants will have the chance to analyze public and patient-sourced genomic and clinical datasets, and will be challenged to produce deliverables for participating patient organizations. These deliverables may take the form of a data report, computational tool, or web/mobile application that improves the lives of patients or furthers research progress.   Participation is free and open to all undergraduate and graduate students who register with their .edu email address.","","https://www.harvard-rarediseases.org/","completed","\N","","2024-03-02","2024-03-03","\N","2024-02-06 00:12:34","2024-02-06 0:41:24"
 "498","dreaming","Diminished Reality for Emerging Applications in Medicine through Inpainting","Dataset of Synthetic Surgery Scenes: Photorealistic Operating Room Simulations","The Diminished Reality for Emerging Applications in Medicine through Inpainting (DREAMING) challenge seeks to pioneer the integration of Diminished Reality (DR) into oral and maxillofacial surgery. While Augmented Reality (AR) has been extensively explored in medicine, DR remains largely uncharted territory.  DR involves virtually removing real objects from the environment by replacing them with their background. Recent inpainting methods present an opportunity for real-time DR applications without scene knowledge. DREAMING focuses on implementing such methods to fill obscured regions in surgery scenes with realistic backgrounds, emphasizing the complex facial anatomy and patient diversity.  The challenge provides a dataset of synthetic yet photorealistic surgery scenes featuring humans, simulating an operating room setting. Participants are tasked with developing algorithms that seamlessly remove disruptions caused by medical instruments and hands, offering surgeons an unimpeded ...","https://rumc-gcorg-p-public.s3.amazonaws.com/b/752/isbi_dreaming_banner_gc_297CU3H.x10.jpeg","https://dreaming.grand-challenge.org/","active","5","","2024-01-08","2024-04-27","\N","2024-02-12 21:56:27","2024-02-20 6:38:09"
-"499","brats-goat","BraTS-ISBI 2024 - Generalizability Across Tumors Challenge","BraTS-GoAT Challenge: Generalizability Across Brain Tumor Segmentation Tasks","The International Brain Tumor Segmentation (BraTS) challenge has been focusing, since its inception in 2012, on generating a benchmarking environment and a dataset for delineating adult brain gliomas. The focus of the BraTS 2023 challenge remained the same: generating a standard benchmark environment. At the same time, the dataset expanded into explicitly addressing 1) the same adult glioma population, as well as 2) the underserved sub-Saharan African brain glioma patient population, 3) brain/intracranial meningioma, 4) brain metastasis, and 5) pediatric brain tumor patients.  Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. Each segmentation method was evaluated exclusively on the patient population it was trained on in each sub-challenge.  In this challenge, we aim to organize the BraTS Generalizability Across Tumors (BraTS-GoAT) Challenge. The hypothesis is t...","","https://www.synapse.org/brats_goat","active","1","","2024-01-09","2024-03-29","\N","2024-02-19 18:20:32","2024-03-06 18:57:49"
+"499","brats-goat","BraTS-ISBI 2024 - Generalizability Across Tumors Challenge","BraTS-GoAT Challenge: Generalizability Across Brain Tumor Segmentation Tasks","The International Brain Tumor Segmentation (BraTS) challenge has been focusing, since its inception in 2012, on generating a benchmarking environment and a dataset for delineating adult brain gliomas. The focus of the BraTS 2023 challenge remained the same: generating a standard benchmark environment. At the same time, the dataset expanded into explicitly addressing 1) the same adult glioma population, as well as 2) the underserved sub-Saharan African brain glioma patient population, 3) brain/intracranial meningioma, 4) brain metastasis, and 5) pediatric brain tumor patients.  Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. Each segmentation method was evaluated exclusively on the patient population it was trained on in each sub-challenge.  In this challenge, we aim to organize the BraTS Generalizability Across Tumors (BraTS-GoAT) Challenge. The hypothesis is t...","","https://www.synapse.org/brats_goat","active","1","","2024-01-09","2024-04-06","\N","2024-02-19 18:20:32","2024-04-03 19:14:42"
 "500","ctc2024","Cell Tracking Challenge 2024","Develop novel, robust cell segmentation and tracking algorithms","Segmenting and tracking moving cells in time-lapse sequences is a challenging task, required for many applications in both scientific and industrial settings. Properly characterizing how cells change their shapes and move as they interact with their surrounding environment is key to understanding the mechanobiology of cell migration and its multiple implications in both normal tissue development and many diseases.  In this challenge, we objectively compare and evaluate state-of-the-art whole-cell and nucleus segmentation and tracking methods using both real and computer-generated (2D and 3D) time-lapse microscopy videos of cells and nuclei. With over a decade-long history and three detailed analyses of its results published in  Bioinformatics 2014, Nature Methods 2017, and Nature Methods 2023, the Cell Tracking Challenge has become a reference in cell segmentation and tracking algorithm development.  This ongoing benchmarking initiative calls for segmentation-and-tracking and segm...","http://celltrackingchallenge.net/files/extras/tracking-result.gif","http://celltrackingchallenge.net/ctc-vii/","active","\N","","2023-12-22","2024-04-05","\N","2024-03-06 18:57:14","2024-03-26 1:26:38"
 "501","isbi-bodymaps24-3d-atlas-of-human-body","ISBI BodyMaps24: 3D Atlas of Human Body","","Variations in organ sizes and shapes can indicate a range of medical conditions, from benign anomalies to life-threatening diseases. Precise organ volume measurement is fundamental for effective patient care, but manual organ contouring is extremely time-consuming and exhibits considerable variability among expert radiologists. Artificial Intelligence (AI) holds the promise of improving volume measurement accuracy and reducing manual contouring efforts. We formulate our challenge as a semantic segmentation task, which automatically identifies and delineates the boundary of various anatomical structures essential for numerous downstream applications such as disease diagnosis and treatment planning. Our primary goal is to promote the development of advanced AI algorithms and to benchmark the state of the art in this field.  The BodyMaps challenge particularly focuses on assessing and improving the generalizability and efficiency of AI algorithms in medical segmentation across divers...","","https://codalab.lisn.upsaclay.fr/competitions/16919","active","9","","2024-01-10","2024-04-15","\N","2024-03-06 20:12:50","2024-03-06 20:16:23"
 "502","precisionfda-automated-machine-learning-automl-app-a-thon","precisionFDA Automated Machine Learning (AutoML) App-a-thon","Unlock new insights into its potential applications in healthcare and medicine","Say goodbye to the days when machine learning (ML) access was the exclusive purview of data scientists and hello to automated ML (AutoML), a low-code ML technique designed to empower professionals without a data science background and enable their access to ML. Although ML and artificial intelligence (AI) have been highly discussed topics in healthcare and medicine, only 15% of hospitals are routinely using ML due to lack of ML expertise and a lengthy data provisioning process. Can AutoML help bridge this gap and expand ML throughout healthcare?  The goal of this app-a-thon is to evaluate the effectiveness of AutoML when applied to biomedical datasets. This app-a-thon aligns with the new Executive Order on Safe, Secure, and Trustworthy Development and Use of AI, which calls for agencies to promote competition in AI. The results of this app-a-thon will be used to help inform regulatory science by evaluating whether AutoML can match or improve the performance of traditional, human-c...","","https://precision.fda.gov/challenges/32","active","6","","2024-02-26","2024-04-26","\N","2024-03-11 22:58:43","2024-03-11 23:02:12"