Abstract
The automated breast ultrasound (ABUS) had been
widely used in breast examination. However, it is time-consuming
for the physician to review hundreds of slices produced by ABUS.
In recent, the computer-aided system based on the convolutional
neural network (CNN) had been proven that it can assist
effectively the physician for tumor detection and diagnosis on the
medical image. Therefore, a computer-aided detection (CADe)
system based on the one-stage 3-D convolutional neural network
(CNN),
3-D You Only Look Once (3-D YOLO), is proposed in this
study to locate suspicious lesions for the physician reviewing image.
The particular characteristic of our system is that the whole ABUS
is detected quickly in one take and the average detecting time of
per ABUS image is
0.8 second. Moreover, the proposed system is
also designed for
smaller tumors to reduce the misdetection rate.
In the proposed system, for achieving better performance, not
only the
one-stage YOLOv3 (You Only Look Once Version 3)
object detection architecture is used and redesigned for smaller tumors, but
also
the essence of focal loss is applied to our loss function, and the
scheme of
cycle learning is designed for solving data imbalance
problem. Before the tumor detection, the ABUS images are resized
firstly to match the model shape. Then, the bounding boxes of
tumor candidates are generated by the detection system. After that,
the non-maximal suppression (NMS) is performed to eliminate the
overlapping for determining the final tumor bounding box. Finally,
our method achieves
sensitivities of 98%, 95%, 90% with 3.8, 2.0,
1.0 false positives (FP) per pass, respectively. Compared to the
previous works, the proposed CADe system is much better and
faster.
