Jean de Kervasdoué Pierre Simon Isabelle Zablit-Schmitz Sandrine Degos Stéphanie Combes Guy Vallancien Maintenir cette décision serait contraire à l'intérêt bien compris des patients et incompréhensible à l'heure de la démocratie sanitaire. Ce serait un vrai "plus" de le leur offrir en libre accès dans Mon Espace Santé (MES), qui plus est une incitation supplémentaire à faire bon usage de cette plate-forme. Dans ce domaine qui les concerne au premier chef on leur doit la transparence. Il faudrait au contraire le rendre accessible au nom de l'open book policy à l'ensemble de nos concitoyens. C'est d'autant plus regrettable que c'est l'une des rares possibilités dont disposent nos concitoyens pour choisir un établissement de soins en connaissance de cause. L’accès aux données qui permettent sa réalisation vient d’être interdit. it is a loss making project that only American giants could afford): French cheap skate idiotic move selling away its security and innovation.Ģ) the Government and its comity CESREES officially declared they didn't like the methodology used by media outlets for 20 years so they asked to prevent access to the health data hub without providing solution or recommandations: censorship to maintain the flawed ideology and hide that the level of healthcare in hospitals was highly correlated to the level of budget of the best hospitals.ģ) the CNIL - beloved French allegedly independent committee to protect citizens and rightful access to public and private data - rejected for the first time in 20 years access to the Health Data Hub without justification: transparency for all but me.įrench decline is not foreigners or international competition, it's simply our own ideology-drivrn mistakes that pile up.Ĭhaque année depuis 2001, prenant le relais du Le Figaro qui avait initié cette démarche, Le Point publie le palmarès des hôpitaux. The article is a pearl to summarize what's wrong with France today.ġ) its anonymised public healthcare data (stored in "le Health Data Hub" as Molière would say) is hosted by Microsoft because no European cloud vendor was able to respond to the public tender (i.e. As shown below, the model does a good job in distinguishing the blurred cells and the performance improves if we use less blurred images as we would expect.French administration blocks 20-year old national ranking of its hospitals done by private Media outlets. We first evaluate the performance of our model on the validation sets at various out-of-focus levels. Therefore, our strategy will allow us to test the generalizability of the trained model. Although both are out of focus, images that are above or below the focal plane look different as their distances to the lens are different. After training, we will further assess the model’s performance on out-of-focus images that are below the focal plane at z = 32. We will set aside a small portion of these images as validation sets and use them to guide and evaluate the training process. We will train our neural network with out-of-focus images that are above the focal plane at z = 1 (the worst), 5 (intermediate), and 10 (close to in-focus)using z = 16 as the ground truth (y). We will use ResNet-34 pretrained on ImageNet as the downsampling path, which utilizes the technique termed transfer learning.įeature loss implementation by Jeremy Howard Training and testing ![]() Cross connections supplement the upsampling path with information from the downsampling path and are the major invention that makes U-net perform so well. U-net essentially consists of three components: a downsampling path that reduces the image size, a subsequent upsampling path that increases the image size, and cross connections that transfer activations from selected parts of the downsampling path to their corresponding parts in the upsampling path. for biomedical image segmentation problems. U-net was originally developed by Ronneberge et al. ) as the loss function to quantify the differences between the output of the neural network and its corresponding optimal focal plane image at z = 16, or the target. In addition, we will use feature loss (originally called perceptual loss by Johnson et al. ![]() ![]() We will base our neural network on the U-net architecture. The overall strategy is to build a convolutional neural network that takes out-of-focus images as input and generates in-focus images as output. A z-stack of 32 images (z = 16 at the optimal focal plane, 15 images above the focal plane, and 16 below) was taken for each of the 768 fields of view (384 wells, 2 fields of view per well). We will be using the Broad Bioimage Benchmark Collection 006 (BBBC006) image set, which was acquired from one 384-well microplate containing human cells whose nuclei were labeled by Hoechst stains.
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