MaFewShotSegmentation

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

Few Shot Segmentation in Medical Imaging

Supervision: Prof. Dr. Nassir Navab, Dr. Shadi Albarqouni, Ari Tran

Abstract

Semantic segmentation does a pixel-wise classification to assign a class or background to each pixel of an image. This problem requires a very large data set of pixel level annotations, which is often unavailable or very costly to create. The aim of this project is to build a state of the art low shot deep learning technique for medical images, which can from few dense or sparse annotated medical image labels derive semantic segmentation of a new previously unseen class.

Requirements:

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

Location:


ProjectForm
Title: Few Shot Segmentation in Medical Imaging
Abstract: Semantic segmentation does a pixel-wise classification to assign a class or background to each pixel of an image. This problem requires a very large data set of pixel level annotations, which is often unavailable or very costly to create. The aim of this project is to build a state of the art low shot deep learning technique for medical images, which can from few dense or sparse annotated medical image labels derive semantic segmentation of a new previously unseen class.
Student: Abhijeet Parida
Director: Prof. Dr. Nassir Navab
Supervisor: Dr. Shadi Albarqouni, Ari Tran
Type: Master Thesis
Area:  
Status: finished
Start:  
Finish:  
Thesis (optional):  
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Edit | Attach | Refresh | Diffs | More | Revision r1.5 - 24 Aug 2019 - 17:44 - ShadiAlbarqouni