  Liver cancer has high morbidity and mortality rates in the world. Multi-phase
CT is a main medical imaging modality for detecting/identifying and diagnosing
liver tumors. Automatically detecting and classifying liver lesions in CT
images have the potential to improve the clinical workflow. This task remains
challenging due to liver lesions' large variations in size, appearance, image
contrast, and the complexities of tumor types or subtypes. In this work, we
customize a multi-object labeling tool for multi-phase CT images, which is used
to curate a large-scale dataset containing 1,631 patients with four-phase CT
images, multi-organ masks, and multi-lesion (six major types of liver lesions
confirmed by pathology) masks. We develop a two-stage liver lesion detection
pipeline, where the high-sensitivity detecting algorithms in the first stage
discover as many lesion proposals as possible, and the lesion-reclassification
algorithms in the second stage remove as many false alarms as possible. The
multi-sensitivity lesion detection algorithm maximizes the information
utilization of the individual probability maps of segmentation, and the
lesion-shuffle augmentation effectively explores the texture contrast between
lesions and the liver. Independently tested on 331 patient cases, the proposed
model achieves high sensitivity and specificity for malignancy classification
in the multi-phase contrast-enhanced CT (99.2%, 97.1%, diagnosis setting) and
in the noncontrast CT (97.3%, 95.7%, screening setting).
