DeepAI AI Chat
Log In Sign Up

Leveraging Disease Progression Learning for Medical Image Recognition

06/26/2018
by   Qicheng Lao, et al.
Concordia University
0

Unlike natural images, medical images often have intrinsic characteristics that can be leveraged for neural network learning. For example, images that belong to different stages of a disease may continuously follow certain progression pattern. In this paper, we propose a novel method that leverages disease progression learning for medical image recognition, where sequences of images ordered by disease stages are learned by a neural network that consists of a shared vision model for feature extraction and a long short-term memory network for the learning of stage sequences. Auxiliary vision outputs are also included to capture stage features that tend to be discrete along disease progression. Our proposed method is evaluated on a diabetic retinopathy dataset, and achieves about 3.3 compared to the baseline method that does not use disease progression learning.

READ FULL TEXT
01/31/2020

Unsupervised deep clustering for predictive texture pattern discovery in medical images

Predictive marker patterns in imaging data are a means to quantify disea...
12/04/2017

Chord Generation from Symbolic Melody Using BLSTM Networks

Generating a chord progression from a monophonic melody is a challenging...
04/04/2022

Generalized Zero Shot Learning For Medical Image Classification

In many real world medical image classification settings we do not have ...
12/21/2020

Disease Forecast via Progression Learning

Forecasting Parapapillary atrophy (PPA), i.e., a symptom related to most...
05/19/2022

A Sub-pixel Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily...
06/22/2020

Machine learning discrimination of Parkinson's Disease stages from walker-mounted sensors data

Clinical methods that assess gait in Parkinson's Disease (PD) are mostly...