  Liver tumor segmentation and classification are important tasks in computer
aided diagnosis. We aim to address three problems: liver tumor screening and
preliminary diagnosis in non-contrast computed tomography (CT), and
differential diagnosis in dynamic contrast-enhanced CT. A novel framework named
Pixel-Lesion-pAtient Network (PLAN) is proposed. It uses a mask transformer to
jointly segment and classify each lesion with improved anchor queries and a
foreground-enhanced sampling loss. It also has an image-wise classifier to
effectively aggregate global information and predict patient-level diagnosis. A
large-scale multi-phase dataset is collected containing 939 tumor patients and
810 normal subjects. 4010 tumor instances of eight types are extensively
annotated. On the non-contrast tumor screening task, PLAN achieves 95% and 96%
in patient-level sensitivity and specificity. On contrast-enhanced CT, our
lesion-level detection precision, recall, and classification accuracy are 92%,
89%, and 86%, outperforming widely used CNN and transformers for lesion
segmentation. We also conduct a reader study on a holdout set of 250 cases.
PLAN is on par with a senior human radiologist, showing the clinical
significance of our results.
