# Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction

arXiv: https://arxiv.org/abs/2501.15610 · HTML: https://mlsanity.com/paper/2501.15610/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 26 Jan 2025.

## Builds on (most cited first, 20 of 20)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning](https://mlsanity.com/paper/1908.02983/) (2019)
- [Rich Human Feedback for Text-to-Image Generation](https://mlsanity.com/paper/2312.10240/) (2023)
- [The Unusual Effectiveness of Averaging in GAN Training](https://mlsanity.com/paper/1806.04498/) (2018)
- [DuDoNet: Dual Domain Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/1907.00273/) (2019)
- [Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models](https://mlsanity.com/paper/2012.08721/) (2020)
- [IQAGPT: Image Quality Assessment with Vision-language and ChatGPT Models](https://mlsanity.com/paper/2312.15663/) (2023)
- [Learning to Distill Global Representation for Sparse-View CT](https://mlsanity.com/paper/2308.08463/) (2023)
- [Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains](https://mlsanity.com/paper/2308.16742/) (2023)
- [Quad-Net: Quad-domain Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2207.11678/) (2022)
- [U-DuDoNet: Unpaired dual-domain network for CT metal artifact reduction](https://mlsanity.com/paper/2103.04552/) (2021)
- [IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2104.01405/) (2021)
- [Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction](https://mlsanity.com/paper/2307.12717/) (2023)
- [Prompted Contextual Transformer for Incomplete-View CT Reconstruction](https://mlsanity.com/paper/2312.07846/) (2023)
- [Investigation of domain gap problem in several deep-learning-based CT metal artefact reduction methods](https://mlsanity.com/paper/2111.12983/) (2021)

## Built on by (most cited first, 4 of 4)

- [Clinical Metadata Guided Limited-Angle CT Image Reconstruction](https://mlsanity.com/paper/2509.01752/) (2025)
- [Physically-Grounded Manifold Projection Model for Generalizable Metal Artifact Reduction in Dental CBCT](https://mlsanity.com/paper/2512.24260/) (2025)
- [Leveraging Image Editing Foundation Models for Data-Efficient CT Metal Artifact Reduction](https://mlsanity.com/paper/2604.05934/) (2026)
- [GraphMAR: Geometry-Aware Graph Learning Framework for Spatially Adaptive CT Metal Artifact Reduction](https://mlsanity.com/paper/2605.17343/) (2026)

## Abstract (arXiv)

Metal artifacts in computed tomography (CT) images can significantly degrade image quality and impede accurate diagnosis. Supervised metal artifact reduction (MAR) methods, trained using simulated datasets, often struggle to perform well on real clinical CT images due to a substantial domain gap. Although state-of-the-art semi-supervised methods use pseudo ground-truths generated by a prior network to mitigate this issue, their reliance on a fixed prior limits both the quality and quantity of these pseudo ground-truths, introducing confirmation bias and reducing clinical applicability. To address these limitations, we propose a novel Radiologist-In-the-loop SElf-training framework for MAR, termed RISE-MAR, which can integrate radiologists' feedback into the semi-supervised learning process, progressively improving the quality and quantity of pseudo ground-truths for enhanced generalization on real clinical CT images. For quality assurance, we introduce a clinical quality assessor model that emulates radiologist evaluations, effectively selecting high-quality pseudo ground-truths for semi-supervised training. For quantity assurance, our self-training framework iteratively generates additional high-quality pseudo ground-truths, expanding the clinical dataset and further improving model generalization. Extensive experimental results on multiple clinical datasets demonstrate the superior generalization performance of our RISE-MAR over state-of-the-art methods, advancing the development of MAR models for practical application. Code is available at https://github.com/Masaaki-75/rise-mar.