AUTOMATIC DIAGNOSIS OF MELANOMA WITH MODERN MACHINE LEARNING TECHNIQUES.

BASIC INFORMATION

Ph.D. Student: Eduardo Pérez
Advisors: Sebastián Ventura
Started on: March 2018
Keywords: early diagnosis of melanoma, deep learning models, flexible data representations

THESIS PROPOSAL

In the last two decades, the application of data mining and machine learning techniques for automating medical diagnosis has gained increasing attention by the scientific community. The main reason for applying this type of computational methods lies in their ability to extract useful knowledge in scenarios where it turns difficult, or even impossible, to draw a conclusion by. Furthermore, the increase of the overall patient information stored, for example in electronic health records (EHR), as well as the amount of information generated by most of the new diagnostic tests (gene arrays, MRI, etc.) is motivating a significant growth of the application of data analysis techniques, including big data, as a support tool for the analysis and automatic diagnosis in biomedicine.

The main goal of this thesis is the development of modern machine learning methods for the automatic (or semi-automatic) diagnosis of melanoma at early stages. This type of skin cancer has an increasing incidence in white people, causing close to 90% of skin cancer mortality. Furthermore, the incidence rate in Europe is around 10-25 new melanoma cases per 100,000 inhabitants; in USA, it is about 20-30 per 100,000 inhabitants; whereas in Australia it affects over 50-60 per 100,000 inhabitants.

This main goal is divided into the following partial objectives:

  • Design and develop predictive models based on the deep learning paradigm for the early diagnosis of melanoma.
  • Design and develop more effective, robust and scalable predictive models by means of using flexible data representations at diagnosing melanoma.

FUNDS

The development of this thesis is being supported by:

  • Strategic Action in Health 2017 of Spain, i-PFIS contract – IFI17/00015.
  • Spanish Ministry of Science and Competitiveness, project TIN2017-83445-P.

 

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