Snke OS 3D Lung CT Segmentation Challenge
Notice bibliographique
Résumé
This is the structured challenge design document for the "Snke OS 3D Lung CT Segmentation Challenge". More details can be found on the challenge's website. The structured design was introduced by the <strong>B</strong>iomedical <strong>I</strong>mage <strong>A</strong>nalysis Challenge<strong>S</strong> (BIAS) initiative. <strong>Background:</strong> Since the outbreak of the global Covid19 pandemic, the number of confirmed COVID-19 cases has reached over 16 million globally [1, 2], affecting virtually every territory, and with a fatality rate ~2-3% among the cohort of PCR-positive cases. Given the high demand for effective diagnosis and treatment of cases, the WHO recently released a rapid advice guide in July 2020 [3], in which chest imaging is conditionally recommended for several purposes, e.g. to aid diagnosis in the absence/delay of PCR testing, to assess the need for ICU admission and to inform the therapeutic management of patients. <br> <strong>Purpose:</strong> In this challenge, we aim to aid radiologists and physicians through objective and quantitative computational assessment of chest imaging in the context of COVID-19. We provide access to a large dataset of 3D chest CT imaging of the lung, collected from several European and international radiological centers. We call the international research community to develop and test artificial intelligence algorithms on this dataset. <strong>Dataset:</strong> We provide access to low-dose chest CT imaging volumes from a mixed cohort of COVID-19 and non- COVID-19 cases. The dataset contains 113 labeled/segmented cases (79 COVID-19, 34 non-COVID-19), and >100 unlabeled volumes. A particular scientific challenge will lie in the effective use of unlabeled data through semi- and self-supervised training techniques. Labels represent five lung lobes and two lesions types, consolidation and ground-glass opacities. Labels are provided in a multi-hot encoding to allow region overlaps (e.g. lesions within lung lobes). For local development, we provide a realistic toy dataset of 96 synthetic volumes with 4D labelmaps. <strong>Infrastructure:</strong> To maintain privacy, the anonymized imaging data remains non-disclosed within a biobank. Participating teams can design their algorithms locally using the representative synthetic dataset. Once ready, teams can submit training and validation jobs on the real dataset through Eisen, a deep learning framework based on pyTorch. Models are trained in the cloud by sponsorship of AWS. We actively promote open science, and require all participating teams to provide their solutions open-source to the technical and medical research community. <strong>Participation:</strong> You can participate in two ways. <em>Hunters:</em> Participate as a team with a maximum of 3 members as a competing team in the challenge. The incentive to the hunters: AWS cloud credits worth 7,500 EUR. <em>Rangers:</em> Participate individually or in a team to help solve the Covid-19 challenge. You can submit tutorials, code or any educational material that is useful for the challenge. The incentive to the rangers: TBA. <strong>Requirements:</strong> After the registration, there will be a “micro challenge” with the task of segmentation based on our synthetic toy dataset, for all teams in order to qualify for the main task.<br> <strong>References</strong> [1] Bell, D.J. COVID-19. https://radiopaedia.org/articles/covid-19-4 [2] ACR. ACR Recommendations for the use of Chest Radiography and Computed Tomography (CT) for Suspected COVID-19 Infection. https://www.acr.org/Advocacy-and-Economics/ACR-Position-Statements/Recommendations-for-Chest-Radiography-and-CT-for-Suspected-COVID19-Infection [3] WHO - Radiation and health. Use of chest imaging in COVID-19. https://www.who.int/publications/i/item/useof-chest-imaging-in-covid-19 <strong>UPDATES</strong> 1st september 2020: Updated the schedule
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,374 | 0,057 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».