Merge branch 'main' into feature-cache-purging-fds-1445

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

@@ -505,7 +505,7 @@
 "504","fets-2024","Federated Tumor Segmentation (FeTS) 2024 Challenge","Benchmarking weight aggregation methods for federated training","Contrary to previous years, this time we only focus on one task and invite participants to compete in “Federated Training” for effective weight aggregation methods for the creation of a consensus model given a pre-defined segmentation algorithm for training, while also (optionally) accounting for network outages.  The same data is used as in FeTS 2022 challenge, but this year the epmhasis is on instance segmentation of brain tumors.","","https://www.synapse.org/fets2024","active","1","","2024-04-01","2024-07-01","\N","2024-04-22 22:07:18","2024-04-22 22:07:18"
 "505","mario","🕹️ 🍄 MARIO : Monitoring AMD progression in OCT","Improve the planning of anti-VEGF treatments","Age-related Macular Degeneration (AMD) is a progressive degeneration of the macula, the central part of the retina, affecting nearly 196 million people worldwide 1. It can appear from the age of 50, and more frequently from the age of 65 onwards, causing a significant weakening of visual capacities, without destroying them. It is a complex and multifactorial pathology in which genetic and environmental risk factors are intertwined. Advanced stages of the disease (atrophy and neovascularization) affect nearly 20% of patients: they are the first cause of severe visual impairment and blindness in developed countries. Since their introduction in 2007, Anti–vascular endothelial growth factor (anti-VEGF) treatments have proven their ability to slow disease progression and even improve visual function in neovascular forms of AMD 2. This effectiveness is optimized by ensuring a short time between the diagnosis of the pathology and the start of treatment as well as by performing regular ch...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/666/square_image_mario_t8tUYoc.png","https://mario.grand-challenge.org/","active","5","","2024-04-01","2024-07-10","\N","2024-04-29 18:13:15","2024-04-29 18:13:26"
 "506","hntsmrg24","Head and Neck Tumor Segmentation for MR-Guided Applications","","This challenge focuses on developing algorithms to automatically segment head and neck cancer gross tumor volumes on multi-timepoint MRI","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/745/logo_v0.png","https://hntsmrg24.grand-challenge.org/","active","5","","2024-05-01","2024-09-15","\N","2024-04-29 18:15:37","2024-04-29 18:21:53"
-"507","acouslic-ai","Abdominal Circumference Operator-agnostic UltraSound measurement","","Fetal growth restriction (FGR), affecting up to 10% of pregnancies, is a critical factor contributing to perinatal morbidity and mortality (1-3). Strongly linked to stillbirths, FGR can also lead to preterm labor, posing risks to the mother (4,5). This condition often results from an impediment to the fetus' genetic growth potential due to various maternal, fetal, and placental factors (6). Measurements of the fetal abdominal circumference (AC) as seen on prenatal ultrasound are a key aspect of monitoring fetal growth. When smaller than expected, these measurements can be indicative of FGR, a condition linked to approximately 60% of fetal deaths (4). FGR diagnosis relies on repeated measurements of either the fetal abdominal circumference (AC), the expected fetal weight, or both. These measurements must be taken at least twice, with a minimum interval of two weeks between them for a reliable diagnosis (7). Additionally, an AC measurement that falls below the third percentile is, b...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/753/acouslicai-logo_tjZmpqL.png","https://acouslic-ai.grand-challenge.org/","upcoming","5","","2024-05-05","2024-07-31","\N","2024-04-29 18:21:37","2024-04-29 18:27:39"
+"507","acouslic-ai","Abdominal Circumference Operator-agnostic UltraSound measurement","","Fetal growth restriction (FGR), affecting up to 10% of pregnancies, is a critical factor contributing to perinatal morbidity and mortality (1-3). Strongly linked to stillbirths, FGR can also lead to preterm labor, posing risks to the mother (4,5). This condition often results from an impediment to the fetus' genetic growth potential due to various maternal, fetal, and placental factors (6). Measurements of the fetal abdominal circumference (AC) as seen on prenatal ultrasound are a key aspect of monitoring fetal growth. When smaller than expected, these measurements can be indicative of FGR, a condition linked to approximately 60% of fetal deaths (4). FGR diagnosis relies on repeated measurements of either the fetal abdominal circumference (AC), the expected fetal weight, or both. These measurements must be taken at least twice, with a minimum interval of two weeks between them for a reliable diagnosis (7). Additionally, an AC measurement that falls below the third percentile is, b...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/753/acouslicai-logo_tjZmpqL.png","https://acouslic-ai.grand-challenge.org/","active","5","","2024-05-05","2024-07-31","\N","2024-04-29 18:21:37","2024-04-29 18:27:39"
 "508","leopard","The LEOPARD Challenge","","Recently, deep learning was shown (H. Pinckaers et al., 2022; O. Eminaga et. al., 2024) to be able to predict the biochemical recurrence of prostate cancer.  Hypothesizing that deep learning could uncover finer morphological features' prognostic value, we are organizing the LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes (LEOPARD) challenge. The goal of this challenge is to yield top-performance deep learning solutions to predict the time to biochemical recurrence from H&E-stained histopathological tissue sections, i.e. based on morphological features.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/754/logo.png","https://leopard.grand-challenge.org/","active","5","","2024-04-10","2024-08-01","\N","2024-04-29 18:28:44","2024-04-29 18:30:12"
 "509","autopet-iii","AutoPET III","","We invite you to participate in the third  autoPET Challenge. The focus of this year's challenge is to further refine the automated segmentation of tumor lesions in Positron Emission Tomography/Computed Tomography (PET/CT) scans in a multitracer multicenter setting.  Over the past decades, PET/CT has emerged as a pivotal tool in oncological diagnostics, management and treatment planning. In clinical routine, medical experts typically rely on a qualitative analysis of the PET/CT images, although quantitative analysis would enable more precise and individualized tumor characterization and  therapeutic decisions. A major barrier to clinical adoption  is  lesion segmentation, a necessary step for quantitative image analysis. Performed manually,  it's tedious, time-consuming and costly. Machine Learning offers the potential for fast and fully automated quantitative analysis of PET/CT images, as previously demonstrated in the first two autoPET challenges.  Building upon the insights gai...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/755/autopet-2024.png","https://autopet-iii.grand-challenge.org/","upcoming","5","","2024-06-30","2024-09-15","\N","2024-04-29 18:29:47","2024-04-29 18:32:40"
 "510","ai4life-mdc24","AI4Life Microscopy Denoising Challenge","","Wellcome to AI4Life-MDC24! In this challenge, we want to focus on an unsupervised denoising of microscopy images. By participating, researchers can contribute to a critical area of scientific research, aiding in interpreting microscopy images and potentially unlocking discoveries in biology and medicine.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/756/Challenge_square.png","https://ai4life-mdc24.grand-challenge.org/","active","5","","2024-05-04","2024-10-06","\N","2024-04-29 18:32:57","2024-04-29 18:34:25"