BRATS Africa: Building Inclusive AI in Radiology
Notice bibliographique
Résumé
Intro-From the RSNA, this is the Radiology Artificial Intelligence Podcast.My name is Paul Yi, and I'm a radiologist and co-host of the podcast.And my name is Ali Tejani, and I'm a radiologist and co-host of the podcast.Each month, we dive into the hottest topics in radiology AI and talk with leading experts, thought leaders, and movers and shakers in the field. Dr. Ali Tejani-Welcome back to theRadiology AI Podcast.Paul, you know, it's been a while since we've been together to record one of these episodes.How have you been?I feel like I haven't seen you in a while.Dr. Ali Tejani-You're joining us from somewhere that's not close by.Where are you today?And what are you doing?So currently I'm in Lagos, even though I still maintain my appointment at the University of Pennsylvania.The idea, the main reason why I'm currently in Lagos right now is the fact that there's a lot of advanced technologies that have been developed out there, and because of the infrastructural set of healthcare and all the women in the country, they are not able to laterally adopt those technologies.So we created my lab in 2022 with the assistance of Dr. Ana Soto, who is on the call with us today, to adapt those advanced technologies to meet resource constraint settings in Africa and other resource constraint settings in other parts of the world.Dr. Ali Tejani-I know we're going to have a huge conversation that we're, I'm sure all of us will learn quite a bit more about that as well.So thank you for joining us.And Udunna, where were you recently?I feel like you're probably traveling as well.Dr. Udunna Anazodo-Yeah.So I was where Marouf is right now.I was actually at my lab at the medical intelligence lab in Lagos.And also I visited a couple of sites where we're starting to do some research in Nigeria.I was at the region's healthcare center in Ower, which is southeastern part of Nigeria.So Lagos, for people that don't know Nigeria, Lagos is on the southwest.I think that's where most people that know Nigeria can pinpoint where the country is.And if you move five or six hours eastward, there is a town called Ower, and there is a fantastic private center, but actually on a multi-specialist hospital.And they have an MRI scanner now, which we're trying to optimize and essentially make sure that I could produce very similar type of imaging quality as we do here in the west.And I also traveled up north, my first time being up north in the country, to a town called Damaturu, which is in Yobesti.And that town right at the border of Chad and Cameroon, so it's at the border town.And it's also a town that was heavily aXected by the Boko Haram insurgency that happened a few years ago.So we're at the hospital that started a very fantastic dementia research study, and there we're trying to, again, see how well we can do imaging within the constraints that they have.So I've been moving around Nigeria, and I think we're going to talk about some of the work we're doing, especially the BRATS Africa project.Dr. Paul Yi-Yeah, so why don't we just jump right in?So you mentioned BRATS, which is short for brain tumor segmentation.And for many of our listeners, they know that this is an annual brain tumor segmentation data science challenge.It's been held for over a decade, if my recollection serves me correctly.Can you tell us more about that?What is BRATS?How did it get started?And how did you all get involved?I think you mentioned that there is an MRI scanner in the city that you had mentioned, which implies that maybe these things aren't so common.Tell us a story.Dr. Udunna Anazodo-Right.So BRATS, like you said, is a brain tumor segmentation challenge.It's a challenge that's been run by the Medical Image Computing and Computer
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,007 | 0,013 |
| Science ouverte | 0,003 | 0,017 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,013 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».