nodule | Node.js pub-sub key-value store | Key Value Database library

I am working on a d3 application - which features a bar chart with nodules on the top. I am keen to get this animated - so the bars grow to the point of rest and the nodules sprout like flowers.

So the nodules are either developed at the start and the bars just rise -- or the bars rise up and then the nodules flower.

//old js fiddle http://jsfiddle.net/s1f4hzpu/1/

//current animation attempts http://jsfiddle.net/9yvn8c4q/

I am using oidc-client.js in my angular project.

Right now users could log-in and log out and it works fine. the only problem is that when I want to call a service in app.component, in the interceptor the user is not authenticated.

auth.service is :

I'm following the instructions at Angular Froala WYSIWYG Editor and I'm using angular version 8.3.26.

app.nodule...

I have created a 3d numpy array of data (shape: 133 x 512 x 512) from dicom format. Given a center point of a nodule location, how would i extract a 3d volume of size 40mm x 40mm x 40mm. I am not sure how would the pixel to mm conversion happens? I have attached the data here. The location of nodule is (317, 363, 89) which is (x,y,z) where z denotes the number of slice. So for this example, the nodule is on slice 89. Here is the sample data. It is in nrrd format. In the dicom header information, the slice thickness was given 2.5 and pixel spacing as ['0.703125', '0.703125'].

It must be something pretty obvious, but for some reason, I am missing it after a couple of hours looking at it very closely..

I am building a multi-module Spring Boot project following hexagonal architecture with Gradle 5.1.1, so I have several adapters like this rest-adapter for which the gradle.build file is below :

I am new to TensorFlow and python. I am trying to run a code for lung cancer detection using CNN. Here is the script: I'm trying to train a CNN model. When I use model.fit while training I am getting an error

I have attempted training a model for binary classification problem using a dataset made of medical pictures(LIDC dataset to be exact), which, too my understanding, shouldnt be too far from the "dog vs cat" classification problem. ( distinguishing between benign and malignant nodules )

Problem is, the accuracy seems to stucks, from the beginning to about 65% and doesnt seem to change at all. Am i doing something wrong ? The example that was provided by my friend easily reaches more than 80% and the accuracy is improving epoch after epoch, but not mine :/

I am training on already extracted patches, all of the same size and separated into two classes.

The model i am using a VGG16 fine-tuned for this task ( i replaced the FC layers with new ones, freezed the previous layer and attempted training )

I have tried changing to binary_crossentropy, applying to_categorical, change the last layer from 1 to 2. At his point, i am confused on the correct combination of parameters for my problems. Sorry if i sound like an absolute beginner...

i tried skipping the less informative parts of the code, hope its readable

I'm running code in Python (I found it already built from a tutorial). I want to deploy the model on a web app (using Flask)

When I run my app I get the following error, see the traceback below:

jinja2.exceptions.UndefinedError: 'csrf_token' is undefined

Traceback:

Here is an example of my dataset.