# Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network

arXiv: https://arxiv.org/abs/2307.08268 · HTML: https://mlsanity.com/paper/2307.08268/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers](https://mlsanity.com/paper/2012.00759/) (2020)
- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation](https://mlsanity.com/paper/2107.09842/) (2021)
- [Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images](https://mlsanity.com/paper/1911.00140/) (2019)
- [3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management](https://mlsanity.com/paper/2012.04701/) (2020)
- [E2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans](https://mlsanity.com/paper/2007.09791/) (2020)
- [A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans](https://mlsanity.com/paper/2306.16036/) (2023)
- [A Flexible Three-Dimensional Hetero-phase Computed Tomography Hepatocellular Carcinoma (HCC) Detection Algorithm for Generalizable and Practical HCC Screening](https://mlsanity.com/paper/2108.07492/) (2021)

## Built on by (most cited first, 5 of 5)

- [Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge](https://mlsanity.com/paper/2408.12534/) (2024)
- [LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning](https://mlsanity.com/paper/2407.13217/) (2024)
- [Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports](https://mlsanity.com/paper/2405.14230/) (2024)
- [PLUS: Plug-and-Play Enhanced Liver Lesion Diagnosis Model on Non-Contrast CT Scans](https://mlsanity.com/paper/2507.03872/) (2025)
- [Query Nearby: Offset-Adjusted Mask2Former enhances small-organ segmentation](https://mlsanity.com/paper/2506.05897/) (2025)

## Abstract (arXiv)

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.