Transferring Learned Microcalcification Group Detection from 2D Mammography to 3D Digital Breast Tomosynthesis Using a Hierarchical Model and Scope-based Normalization Features

TL;DR

This paper presents a hierarchical model with scope-based normalization features for detecting microcalcification groups, successfully transferring from 2D mammography to 3D tomosynthesis with high accuracy and no retraining.

Contribution

The study introduces a novel hierarchical detection model with scope-based normalization that effectively transfers between imaging modalities without retraining.

Findings

Achieved 96% detection rate of cancerous lesions in 3D tomosynthesis

Detected microcalcification groups with 1.2 false positives per volume

Demonstrated state-of-the-art performance on independent test set

Abstract

A novel hierarchical model is introduced to solve a general problem of detecting groups of similar objects. Under this model, detection of groups is performed in hierarchically organized layers while each layer represents a scope for target objects. The processing of these layers involves sequential extraction of appearance features for an individual object, consistency measurement features for nearby objects, and finally the distribution features for all objects within the group. Using the concept of scope-based normalization, the extracted features not only enhance local contrast of an individual object, but also provide consistent characterization for all related objects. As an example, a microcalcification group detection system for 2D mammography was developed, and then the learned model was transferred to 3D digital breast tomosynthesis without any retraining or fine-tuning. The detection system demonstrated state-of-the-art performance and detected 96% of cancerous lesions at the rate of 1.2 false positives per volume as measured on an independent tomosynthesis test set.