# 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network

arXiv: https://arxiv.org/abs/1802.05656 · HTML: https://mlsanity.com/paper/1802.05656/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2018.

## Built on by (most cited first, 30 of 47)

- [CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization](https://mlsanity.com/paper/2304.01814/) (2023)
- [DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising](https://mlsanity.com/paper/2108.10772/) (2021)
- [Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models](https://mlsanity.com/paper/2106.04180/) (2021)
- [MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images](https://mlsanity.com/paper/2310.03559/) (2023)
- [Eformer: Edge Enhancement based Transformer for Medical Image Denoising](https://mlsanity.com/paper/2109.08044/) (2021)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [IQAGPT: Image Quality Assessment with Vision-language and ChatGPT Models](https://mlsanity.com/paper/2312.15663/) (2023)
- [ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising](https://mlsanity.com/paper/2307.12225/) (2023)
- [LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring](https://mlsanity.com/paper/2302.10630/) (2023)
- [Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction](https://mlsanity.com/paper/2506.22012/) (2025)
- [Mx2M: Masked Cross-Modality Modeling in Domain Adaptation for 3D Semantic Segmentation](https://mlsanity.com/paper/2307.04231/) (2023)
- [Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?](https://mlsanity.com/paper/2209.07026/) (2022)
- [Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection](https://mlsanity.com/paper/2104.08845/) (2021)
- [Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels](https://mlsanity.com/paper/2203.07725/) (2022)
- [Low-dose CT Denoising with Language-engaged Dual-space Alignment](https://mlsanity.com/paper/2403.06128/) (2024)
- [Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform](https://mlsanity.com/paper/2110.11684/) (2021)
- [Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond](https://mlsanity.com/paper/2103.14528/) (2021)
- [TiAVox: Time-aware Attenuation Voxels for Sparse-view 4D DSA Reconstruction](https://mlsanity.com/paper/2309.02318/) (2023)
- [Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignment](https://mlsanity.com/paper/2302.13251/) (2023)
- [MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising](https://mlsanity.com/paper/2103.12995/) (2021)
- [Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation Study](https://mlsanity.com/paper/2104.15050/) (2021)
- [Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training](https://mlsanity.com/paper/2201.01426/) (2022)
- [Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges](https://mlsanity.com/paper/2211.14732/) (2022)
- [LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models](https://mlsanity.com/paper/2507.06140/) (2025)
- [Belief Propagation as Diffusion](https://mlsanity.com/paper/2107.12230/) (2021)
- [Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography](https://mlsanity.com/paper/2111.06890/) (2021)
- [Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate](https://mlsanity.com/paper/2202.10244/) (2022)
- [Blind CT Image Quality Assessment Using DDPM-derived Content and Transformer-based Evaluator](https://mlsanity.com/paper/2310.03118/) (2023)
- [HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesis](https://mlsanity.com/paper/2405.04902/) (2024)
- [No-reference denoising of low-dose CT projections](https://mlsanity.com/paper/2102.02662/) (2021)

## Abstract (arXiv)

Low-dose computed tomography (CT) has attracted a major attention in the medical imaging field, since CT-associated x-ray radiation carries health risks for patients. The reduction of CT radiation dose, however, compromises the signal-to-noise ratio, and may compromise the image quality and the diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in low-dose CT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN). This article introduces a Contracting Path-based Convolutional Encoder-decoder (CPCE) network in 2D and 3D configurations within the GAN framework for low-dose CT denoising. A novel feature of our approach is that an initial 3D CPCE denoising model can be directly obtained by extending a trained 2D CNN and then fine-tuned to incorporate 3D spatial information from adjacent slices. Based on the transfer learning from 2D to 3D, the 3D network converges faster and achieves a better denoising performance than that trained from scratch. By comparing the CPCE with recently published methods based on the simulated Mayo dataset and the real MGH dataset, we demonstrate that the 3D CPCE denoising model has a better performance, suppressing image noise and preserving subtle structures.