MaMedicalReports

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Understanding Medical Images to Generate Reliable Medical Report

Supervision: Prof. Dr. Nassir Navab, Dr. Shadi Albarqouni, Dr. Seong Tae Kim

Abstract

The reading and interpretation of medical images are usually conducted by specialized medical experts [1]. For example, radiology images are read by radiologists and they write textual reports to describe the findings regarding each area of the body examined in the imaging study. However, writing medical-imaging reports requires experienced medical experts (e.g. experienced radiologists or pathologists) and it is time-consuming [2]. To assist in the administrative duties of writing medical-imaging reports, in recent years, a few research efforts have been devoted to investigating whether it is possible to automatically generate medical image reports for given medical image [3-8]. These methods are usually based on the encoder-decoder architecture which has been widely used for image captioning [9-10]. In this project, a novel automatic medical report generation method is investigated. It is challenging to generate accurate medical reports with large variation due to the high complexity in the natural language [11]. So, the traditional captioning methods suffer a problem where the model duplicates a completely identical sentence of the training set. To address the aforementioned limitations, this project focuses on the development of a reliable medical report generation method.

Requirements:

  • Good understanding of statistics and machine learning methods.
  • Very good programming skills in Python & TensorFlow? / PyTorch?

Location:

  • Garching

Literature


ProjectForm
Title: Understanding Medical Images to Generate Reliable Medical Report
Abstract: The reading and interpretation of medical images are usually conducted by specialized medical experts [1]. For example, radiology images are read by radiologists and they write textual reports to describe the findings regarding each area of the body examined in the imaging study. However, writing medical-imaging reports requires experienced medical experts (e.g. experienced radiologists or pathologists) and it is time-consuming [2]. To assist in the administrative duties of writing medical-imaging reports, in recent years, a few research efforts have been devoted to investigating whether it is possible to automatically generate medical image reports for given medical image [3-8]. These methods are usually based on the encoder-decoder architecture which has been widely used for image captioning [9-10]. In this project, a novel automatic medical report generation method is investigated. It is challenging to generate accurate medical reports with large variation due to the high complexity in the natural language [11]. So, the traditional captioning methods suffer a problem where the model duplicates a completely identical sentence of the training set. To address the aforementioned limitations, this project focuses on the development of a reliable medical report generation method.
Student:  
Director: Prof. Dr. Nassir Navab
Supervisor: Dr. Shadi Albarqouni, Dr. Seong Tae Kim
Type: IDP
Area: Machine Learning, Medical Imaging
Status: open
Start:  
Finish:  
Thesis (optional):  
Picture:  


Edit | Attach | Refresh | Diffs | More | Revision r1.4 - 19 Jun 2019 - 15:13 - SeongTaeKim