Learning Classifiers for Computer Aided Diagnosis Using Local Correlations
diagnosis (CAD) applications the goal is to detect structures
of interest to physicians in medical images: e.g. to identify
potentially malignant lesions in an image (mammography, lung
CT, Colon CT, heart ultrasound, etc.). In an almost universal
paradigm, this problem is addressed by a 5 stage system:
1. Segmentation to identify/extract the general area of
interest; 2. Candidate generation which identifies suspicious
unhealthy candidate regions of interest (ROI) from a medical
image; 3. feature extraction that computes descriptive features
for each candidate; 4. classification that differentiates
candidates based on candidate feature vectors; 5. visual
presentation of CAD findings to the radiologist in order for
him to accept or reject the CAD findings.
For the fourth stage, many standard algorithms (such as
support vector machines (SVM), back-propagation neural nets,
kernel Fisher discriminants) have been used to learn
classifiers for detecting malignant structures. However, these
general-purpose learning methods either make implicit
assumptions that are commonly violated in CAD applications, or
cannot effectively address the difficulties arisen when
learning a CAD system.
Non-IID Data Traditional learning methods almost universally
assume that the training samples are independently drawn from
an identical albeit unobservable underlying distribution (the
IID assumption), which is often not the case in CAD systems.
Due to spatial adjacency of the regions identified by a
candidate generator, both the features and the class labels of
several adjacent candidates are highly correlated.
In this talk we present two recent proposed machine learning
algorithms that successfully takes into account the correlation
among candidates to significantly improve classification