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TFG Medical Image Assistant



Application of deep learning in medical images analysis.


Implement and deploy an application... for medical images. The goal is to get a virtual assistant for medical images.


@IFCA: Lara Lloret <lloret@ifca.unican.es>

@Alumna: Carmen Garcia Bermejo <cgb60@alumnos.unican.es>



TFG will be presented by January 2019 at latest


What is Deep Learning?

It is a set of algorithms of machine learning that tries to model high-level abstractions in data using architectures composed of multiple non-linear transformations. To sum up, it is a way of learning an optimal representation for given samples in many phases. This representation that is done in layers, is learned through models (neural networks).

So... How it works?

DICOM Images

It is possible to check some medical images in the following links, in order to be used as the training of the convolutional neural network. More detailed in https://grid.ifca.es/wiki/Projects/DeepMedical/.

These are DICOM archives. That is, Digital Imaging and Communications in Medicine. DICOM it is used both as a communications protocol and a file format, so it can keep medical information with the patient's information, in one file.

Project Tracking

Tracking of the project. How will it be done. Add subsections as needed


6-Nov-2017 meeting to check progress

- python course started

- wiki TFG started

- deep learning intro (Master Data Science)

13-Nov-2017 meeting to check progress

- finished deep learning intro

- starting phyton learning (using a classifier program of numbers in Jupyter)

20-Nov-2017 meeting to check progress

- Classifier of cat and dogs

4-Dic-2017 meeting to check progress

- Starting Standford course of CNN

11-Dic-2017 meeting to check progress

- Piano and guitar classifier

18-Dic-2017 meeting to check progress

- Piano and guitar classifier

Papers found

About the used of deep learning in '''chest''' medical images analysis:

Application of deep learning in other medical images analysis:

Using Convolution neural network in other applications:

Standford classes

CS231n Lecture 1: Introduction and Historical Context

A brief history of computer vision, from the eyes evolution that takes millions of years to nowdays. Just taking a picture from a 10 MPX camera, has a potencial combination of pixels to form a picture in that is bigger than the number of atoms in the Universe.

CS321 overview, focused on image classification using CNN, which is a type of deep learning architecture.

The importance of data, that is the driving force for a high capacity model to enable the end-to-end trainning to help avoid overfitting.


CS231n Lecture 2: Data-driven approach, kNN, Linear Classification 1

Image classification and some of its challenges: illumination, deformation... Data-driven approach:

1- Collect a dataset of images and labels.

2- Use machine learning to train an image classifier

3- Evaluate the classifier

K-Nearest Neighbour Classifier (it finds the k nearest images, for eg: a five nearest neighbour will give us the five most similar images in the training data).

Linear Classification.


CS231n Lecture 3: Linear Classification 2, Optimization

Loss function.

Multiclass SVM loss (given an example (xi,yi) where xi is the image and where yi is the (integer) label, and using the shorthand for the scores vector: s=f(xi, W))).

Weight regularization: R(W)


In summary:

- Numerical gradient: approximate, slow, easy to write. - Analytic gradient: exact, fast, error-prone

Gradient descent


CS231n Lecture 4: Backpropagation, Neural Networks 1


eciencia: Projects/TFG-MEDICAL-IMAGE-ASSISTANT (last edited 2018-01-06 22:38:39 by cgbermejo)