Partagez sur
STAGE Bone Age Assessment - Gleamer internship
Date de mise à jour de l’offre
Gleamer :
Société développant des outils d'intelligence artificielle pour les radiologues
Description de la mission
Bone age is a metric used to infer the biological maturity of children and adolescents. Although
correlated with the true age of an individual, abnormal discrepancies between true age and bone age
is a strong indicator of growth disorders. Bone age assessment (BAA) is thus a central part of the
diagnosis of several endocrine and metabolic disorders affecting children. As of today, most BAA rely
on either the TW2[1] or GP[2] methods, which are based on the comparison of a radio of a patient’s
hand to huge atlases of radios of “normally-developed bones. BAA is both tiresome and
time-consuming for radiologists, and the results often subjective and varying from a practitioner to
the other.
On the other hand, in the recent years, deep neural networks and in particular deep convolutional
neural networks have managed to reach exceptional performances on a wide variety of image-based
benchmarks, including medical imagery analysis tasks. In particular, some end-to-end solutions to the
BAA problem have appeared in the literature [4, 5, 6]. The system is first trained on a set of radios
annotated by radiologists who based their estimations on the atlases mentioned above. Once
trained, it is able to predict with high accuracy the bone age that a radiologist would have associated
to a given hand radiograph.
This internship would first focus on replicating the results of the aforementioned methods, and
improving the state of the art using several classical computer vision techniques (attention
mechanisms, multi-task learning, transfer learning) as well as ideas and suggestions from the intern.
correlated with the true age of an individual, abnormal discrepancies between true age and bone age
is a strong indicator of growth disorders. Bone age assessment (BAA) is thus a central part of the
diagnosis of several endocrine and metabolic disorders affecting children. As of today, most BAA rely
on either the TW2[1] or GP[2] methods, which are based on the comparison of a radio of a patient’s
hand to huge atlases of radios of “normally-developed bones. BAA is both tiresome and
time-consuming for radiologists, and the results often subjective and varying from a practitioner to
the other.
On the other hand, in the recent years, deep neural networks and in particular deep convolutional
neural networks have managed to reach exceptional performances on a wide variety of image-based
benchmarks, including medical imagery analysis tasks. In particular, some end-to-end solutions to the
BAA problem have appeared in the literature [4, 5, 6]. The system is first trained on a set of radios
annotated by radiologists who based their estimations on the atlases mentioned above. Once
trained, it is able to predict with high accuracy the bone age that a radiologist would have associated
to a given hand radiograph.
This internship would first focus on replicating the results of the aforementioned methods, and
improving the state of the art using several classical computer vision techniques (attention
mechanisms, multi-task learning, transfer learning) as well as ideas and suggestions from the intern.
Profil recherché
Research, design, implement and iteratively enhance state-of-the-art deep learning
approaches.
- Work in a close collaboration with expert medical doctors, AI doctoral researcher and
research engineers to elaborate the best algorithmic solutions suited for real-world medical
problems.
- Being infused with ambitious long-term research goals to achieve transformative impact on
healthcare through AI.
approaches.
- Work in a close collaboration with expert medical doctors, AI doctoral researcher and
research engineers to elaborate the best algorithmic solutions suited for real-world medical
problems.
- Being infused with ambitious long-term research goals to achieve transformative impact on
healthcare through AI.
Niveau de qualification requis
Bac + 4/5 et +
Les offres de stage ou de contrat sont définies par les recruteurs eux-mêmes.
En sa qualité d’hébergeur dans le cadre du dispositif des « 100 000 stages », la Région Île-de-France est soumise à un régime de responsabilité atténuée prévu aux articles 6.I.2 et suivants de la loi n°2204-575 du 21 juin 2004 sur la confiance dans l’économie numérique.
La Région Île-de-France ne saurait être tenue responsable du contenu des offres.
Néanmoins, si vous détectez une offre frauduleuse, abusive ou discriminatoire vous pouvez la signaler
en cliquant sur ce lien.
-
EmployeurGleamer
-
Secteur d’activité de la structureSanté - Social - Citoyenneté - Sécurité
-
Effectif de la structureDe 0 à 10 salariés
-
Type de stage ou contratStage pour lycéens et étudiants en formation initiale
-
Date prévisionnelle de démarrage
-
Durée du stage ou contratPlus de 4 mois et jusqu'à 6 mois
-
Le stage est-il rémunéré ?Oui
-
Niveau de qualification requis
Bac + 4/5 et + -
Lieu du stageAgoranov
96 bis Bd Raspail
75006 PARIS 6E ARRONDISSEMENT -
Accès et transportsMetro Ligne 4