7 research outputs found
Semantic Video Segmentation for Intracytoplasmic Sperm Injection Procedures
We present the first deep learning model for the analysis of intracytoplasmic
sperm injection (ICSI) procedures. Using a dataset of ICSI procedure videos, we
train a deep neural network to segment key objects in the videos achieving a
mean IoU of 0.962, and to localize the needle tip achieving a mean pixel error
of 3.793 pixels at 14 FPS on a single GPU. We further analyze the variation
between the dataset's human annotators and find the model's performance to be
comparable to human experts.Comment: Accepted at the 'Medical Imaging meets NeurIPS Workshop' at the 34th
Conference on Neural Information Processing System
Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning
STUDY QUESTION: Can machine learning predict the number of oocytes retrieved from controlled ovarian hyperstimulation (COH)? SUMMARY ANSWER: Three machine-learning models were successfully trained to predict the number of oocytes retrieved from COH. WHAT IS KNOWN ALREADY: A number of previous studies have identified and built predictive models on factors that influence the number of oocytes retrieved during COH. Many of these studies are, however, limited in the fact that they only consider a small number of variables in isolation. STUDY DESIGN, SIZE, DURATION: This study was a retrospective analysis of a dataset of 11,286 cycles performed at a single centre in France between 2009 and 2020 with the aim of building a predictive model for the number of oocytes retrieved from ovarian stimulation. The analysis was carried out by a data analysis team external to the centre using the Substra framework. The Substra framework enabled the data analysis team to send computer code to run securely on the centre's on-premises server. In this way, a high level of data security was achieved as the data analysis team did not have direct access to the data, nor did the data leave the centre at any point during the study. PARTICIPANTS/MATERIALS, SETTING, METHODS: The Light Gradient Boosting Machine algorithm was used to produce three predictive models: one that directly predicted the number of oocytes retrieved and two that predicted which of a set of bins provided by two clinicians the number of oocytes retrieved fell into. The resulting models were evaluated on a held-out test set and compared to linear and logistic regression baselines. In addition, the models themselves were analysed to identify the parameters that had the biggest impact on their predictions. MAIN RESULTS AND THE ROLE OF CHANCE: On average, the model that directly predicted the number of oocytes retrieved deviated from the ground truth by 4.21 oocytes. The model that predicted the first clinician's bins deviated by 0.73 bins whereas the model for the second clinician deviated by 0.62 bins. For all models, performance was best within the first and third quartiles of the target variable, with the model underpredicting extreme values of the target variable (no oocytes and large numbers of oocytes retrieved). Nevertheless, the erroneous predictions made for these extreme cases were still within the vicinity of the true value. Overall, all three models agreed on the importance of each feature which was estimated using Shapley Additive Explanation (SHAP) values. The feature with the highest mean absolute SHAP value (and thus the highest importance) was the antral follicle count, followed by basal AMH and FSH. Of the other hormonal features, basal TSH, LH, and testosterone levels were similarly important and baseline LH was the least important. The treatment characteristic with the highest SHAP value was the initial dose of gonadotropins. LIMITATIONS, REASONS FOR CAUTION: The models produced in this study were trained on a cohort from a single centre. They should thus not be used in clinical practice until trained and evaluated on a larger cohort more representative of the general population. WIDER IMPLICATIONS OF FINDINGS: These predictive models for the number of oocytes retrieved from COH may be useful in clinical practice, assisting clinicians in optimizing COH protocols for individual patients. Our work also demonstrates the promise of using the Substra framework for allowing external researchers to provide clinically relevant insights on sensitive fertility data in a fully secure, trustworthy manner and opens a number of exciting avenues for accelerating future research. STUDY FUNDING/COMPETING INTEREST(S): This study was funded by the French Public Bank of Investment as part of the Healthchain Consortium. T.Fe., C.He., J.C., C.J., C.-A.P., and C.Hi. are employed by Apricity. C.Hi. has received consulting fees and honoraria from Vitrolife, Merck Serono, Ferring, Cooper Surgical, Dibimed, Apricity, and Fairtility and travel support from Fairtility and Vitrolife, participates on an advisory board for Merck Serono, was the founder and organizer of the AI Fertility conference, has stock in Aria Fertility, TMRW, Fairtility, Apricity, and IVF Professionals, and received free equipment from Planar in exchange for first user feedback. C.J. has received a grant from BPI. J.C. has also received a grant from BPI, is a member of the Merck AI advisory board, and is a board member of Labelia Labs. C.He has a contract for medical writing of this manuscript by CHU Nantes and has received travel support from Apricity. A.R. haÈ received honoraria from Ferring and Organon. T.Fe. has received a grant from BPI. TRIAL REGISTRATION NUMBER: N/A
Les Facultés de droit de province aux xixe et xxe siÚcles. Tome 3
Les contributions qui composent cet ouvrage portent lâattention sur la vie deux fois sĂ©culaire des FacultĂ©s de droit de province, et plus prĂ©cisĂ©ment sur les efforts ou les choix qui ont contribuĂ© aux Ă©volutions, aux adaptations de ces derniĂšres. Elles pouvaient ĂȘtre Ă©tudiĂ©es comme des entitĂ©s institutionnelles, un corps vivant - dâenseignants mais aussi dâenseignements - comme des lieux de dynamiques sociales et politiques ou comme espaces de production scientifique. Finalement câest lâidĂ©e de conquĂȘtes qui se dĂ©gage assez naturellement. ConquĂȘtes universitaires, donc. Ou facultaires. Rien de martial dans ce mouvement. Rien dâuniforme non plus. Mais seulement la signification de rĂ©alitĂ©s restituĂ©es par la diversitĂ© des sujets traitĂ©s : jamais lâUniversitĂ© nâa cessĂ© dâĂȘtre un lieu de dĂ©bat tournĂ© vers ses objectifs, ses missions et ses mĂ©thodes. Jamais non plus, il ne semble quâelle ait considĂ©rĂ© une situation acquise comme indĂ©passable, rendant douteuse lâimage dâune institutio absolument hermĂ©tique et/ou prisonniĂšre dâelle mĂȘme. Sâimposer parmi plusieurs territoires, administratifs ou symboliques ? Gagner le cĆur des Ă©tudiants ou de leurs parents ? Etoffer les enseignements ? Adapter la recherche ? Les FacultĂ©s de droit semblent bien confrontĂ©es depuis leur rĂ©tablissement Ă ces questions vitales. Et si elles nâont cessĂ© de se rĂ©former, malgrĂ© leurs rĂ©sistances naturelles, elles lâont fait en sâinscrivant Ă la fois dans le champ des objectifs assignĂ©s par lâautoritĂ© publique, extĂ©rieure, et dans celui de leur propre culture
Le savant fou
Personnage complexe, le savant fou renvoie Ă une opposition remontant Ă l'AntiquitĂ© qui perçoit folie et gĂ©nie comme deux notions complĂ©mentaires. Cette complĂ©mentaritĂ© perdure et se nourrit des crises Ă©pistĂ©mologiques qui bouleversent la perception du monde et de lui-mĂȘme qu'a l'ĂȘtre humain. La figure du savant fou cristallise de nombreuses peurs diffuses qui peuvent ĂȘtre d'ordre politique, social, religieux, Ă©conomique ou idĂ©ologique et qui ont trait Ă la possibilitĂ© mĂȘme de se dĂ©finir en tant qu'ĂȘtre humain. La figure a par ailleurs accĂ©dĂ© au rang de figure mythique « moderne » avec le mythe de Faust qui rĂ©-active le mythe antique de PromĂ©thĂ©e. Cet ouvrage fournit lâoccasion dâexplorer plus particuliĂšrement les avatars contemporains du savant fou ainsi que la spĂ©cificitĂ© des questionnements quâil met en jeu dans le roman et les arts visuels de la fin du XXe siĂšcle et du dĂ©but du XXIe siĂšcle. Il permet Ă©galement dâaborder la dimension mythique de cette figure qui du Victor Frankenstein de Mary Shelley Ă nos jours ne se lasse pas de resurgir dans les reprĂ©sentations imaginaires et fictives
Financiarisation et travail
Le âCorpusâ de ce numĂ©ro de La Nouvelle Revue du Travail est consacrĂ© aux interdĂ©pendances entre la financiarisation de lâĂ©conomie et les transformations contemporaines du travail. LâentrĂ©e privilĂ©giĂ©e ici est la gestion, ses acteurs et ses dispositifs, instances intermĂ©diaires grĂące auxquelles le macro-Ă©conomique conforme le microsocial, Ă travers les mĂ©tamorphoses imprimĂ©es au travail. Câest en particulier autour de lâĂ©tude critique des actes dĂ©sormais omniprĂ©sents de mesures physiques et de valorisations comptables des activitĂ©s de travail conduits par les managers (ou les gestionnaires) que sâest construit ce numĂ©ro. Dit autrement, les articles ici rassemblĂ©s cherchent Ă Ă©clairer les diffĂ©rentes maniĂšres dont les impĂ©ratifs de rentabilitĂ© financiĂšre sont rĂ©interprĂ©tĂ©s en termes de dĂ©cisions de rationalisation du travail et marquent les organisations contemporaines â quâelles soient publiques ou privĂ©es â du mĂȘme rĂ©ductionnisme calculatoire, sans oublier les rĂ©actions ou les rĂ©sistances des personnels concernĂ©s. ProcĂ©der Ă un tel choix, câest renouer avec une ambition initiale de la sociologie, quand Durkheim et les membres de LâAnnĂ©e sociologique discutaient les impensĂ©s anthropologiques de lâĂ©conomie politique, ou quand Weber, Sombart ou Marx nous invitaient Ă nous intĂ©resser Ă la comptabilitĂ© pour comprendre comment le capitalisme entraĂźnait le travail dans les voies qui Ă©taient les siennes
Dictionnaire critique de la RSE
Tout le monde parle de la RSE mais qui sait vraiment Ă quoi renvoie ce phĂ©nomĂšne ? Si chacun s'accorde sur le fait qu'il bouleverse le mode traditionnel de nĂ©gociation sociale et de mĂ©diation publique, il n'existe en revanche aucune forme de consensus clair quant Ă ses contours et limites. AvancĂ©e pour les uns, recul pour les autres, phĂ©nomĂšne intriguant pour tous, la RSE interpelle, mobilise, divise⊠sans que l'on dispose dâun minimum de rĂ©fĂ©rent commun concernant le sens, la nature, lâextension, le potentiel, le contenu de la « ResponsabilitĂ© Sociale de lâEntreprise ». Câest ce vide que ce premier dictionnaire, critique, rĂ©flexif, pluridisciplinaire, vient combler. Ouvrage collectif constituĂ© de contributions des spĂ©cialistes reconnus de cette question dans le champ des sciences sociales, il se prĂ©sente comme un outil de rĂ©fĂ©rence pour les praticiens et les analystes de ce phĂ©nomĂšne qui change la donne en matiĂšre de rĂ©gulation sociale. Avec cet outil, la discussion peut commencer sur des bases communes