mlsanityData to 14 Oct 2026

A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans

arXiv 2306.16036 ↗First posted 28 Jun 2023eess.IV cs.CVcited by 5

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Abstract (arXiv)

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).

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 15 of its 39 references that are in the map. First posted on arXiv 28 Jun 2023.

Builds on

15 of 39 references.

References · 15

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  3. 2021The Medical Segmentation Decathloncited by 628
  4. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 509
  5. 2019A large annotated medical image dataset for the development and evaluation of segmentation algorithmscited by 373
  6. 2021A Survey on Deep Semi-supervised Learningcited by 251
  7. 2020AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?cited by 224
  8. 2018Synthetic Data Augmentation using GAN for Improved Liver Lesion Classificationcited by 136
  9. 2016Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fieldscited by 43
  10. 2020Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CTcited by 42
  11. 2018RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scanscited by 41
  12. 2019A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scanscited by 21
3 more
  1. 2017Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Methodcited by 19
  2. 2020Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learningcited by 7
  3. 2017Detection-aided liver lesion segmentation using deep learningcited by 2

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Cited by year

Citing papers per year: 2023: 3; 2024: 1; 2025: 1.

5 papers.

  1. 2023Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Networkcited by 5
  2. 2023Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scanscited by 2
  3. 2023Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologistscited by 1
  4. 2024Cross-Phase Mutual Learning Framework for Pulmonary Embolism Identification on Non-Contrast CT Scanscited by 1
  5. 2025PLUS: Plug-and-Play Enhanced Liver Lesion Diagnosis Model on Non-Contrast CT Scanscited by 1