# Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning

arXiv: https://arxiv.org/abs/2006.15691 · HTML: https://mlsanity.com/paper/2006.15691/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans](https://mlsanity.com/paper/2301.12291/) (2023)
- [Medical Vision Generalist: Unifying Medical Imaging Tasks in Context](https://mlsanity.com/paper/2406.05565/) (2024)
- [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 Unified Multi-Phase CT Synthesis and Classification Framework for Kidney Cancer Diagnosis with Incomplete Data](https://mlsanity.com/paper/2312.05548/) (2023)
- [Hetero-Modal Learning and Expansive Consistency Constraints for Semi-Supervised Detection from Multi-Sequence Data](https://mlsanity.com/paper/2103.12972/) (2021)
- [LiMT: A Multi-task Liver Image Benchmark Dataset](https://mlsanity.com/paper/2511.19889/) (2025)
- [External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data](https://mlsanity.com/paper/2201.00942/) (2022)

## Abstract (arXiv)

Non-invasive radiological-based lesion characterization and identification, e.g., to differentiate cancer subtypes, has long been a major aim to enhance oncological diagnosis and treatment procedures. Here we study a specific population of human subjects, with the hope of reducing the need for invasive surgical biopsies of liver cancer patients, which can cause many harmful side-effects. To this end, we propose a fully-automated and multi-stage liver tumor characterization framework designed for dynamic contrast computed tomography (CT). Our system comprises four sequential processes of tumor proposal detection, tumor harvesting, primary tumor site selection, and deep texture-based tumor characterization. Our main contributions are that, (1) we propose a 3D non-isotropic anchor-free detection method for liver lesions; (2) we present and validate spatially adaptivedeep texture (SaDT) learning, which allows for more precise characterization of liver lesions; (3) using a semi-automatic process, we bootstrap off of 200 gold standard annotations to curate another 1001 patients. Experimental evaluations demonstrate that our new data curation strategy, combined with the SaDT deep dynamic texture analysis, can effectively improve the mean F1 scores by >8.6% compared with baselines, in differentiating four major liver lesion types. Our F1 score of (hepatocellular carcinoma versus remaining subclasses) is 0.763, which is higher than reported human observer performance using dynamic CT and comparable to an advanced magnetic resonance imagery protocol. Apart from demonstrating the benefits of our data curation approach and physician-inspired workflow, these results also indicate that analyzing texture features, instead of standard object-based analysis, is a promising strategy for lesion differentiation.