mlsanityData to 7 Oct 2026

MORE: Multi-Organ Medical Image REconstruction Dataset

arXiv 2510.26759 ↗First posted 30 Oct 2025eess.IV cs.CV cs.MMcited by 0

Not on the map. Why?

Abstract (arXiv)

CT reconstruction provides radiologists with images for diagnosis and treatment, yet current deep learning methods are typically limited to specific anatomies and datasets, hindering generalization ability to unseen anatomies and lesions. To address this, we introduce the Multi-Organ medical image REconstruction (MORE) dataset, comprising CT scans across 9 diverse anatomies with 15 lesion types. This dataset serves two key purposes: (1) enabling robust training of deep learning models on extensive, heterogeneous data, and (2) facilitating rigorous evaluation of model generalization for CT reconstruction. We further establish a strong baseline solution that outperforms prior approaches under these challenging conditions. Our results demonstrate that: (1) a comprehensive dataset helps improve the generalization capability of models, and (2) optimization-based methods offer enhanced robustness for unseen anatomies. The MORE dataset is freely accessible under CC-BY-NC 4.0 at our project page https://more-med.github.io/

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 37 references that are in the map. First posted on arXiv 30 Oct 2025.

Builds on

15 of 37 references.

References · 15

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  2. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  3. 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
  4. 2021NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstructioncited by 145
  5. 2022Solving 3D Inverse Problems using Pre-trained 2D Diffusion Modelscited by 130
  6. 2016A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstructioncited by 87
  7. 2024R2-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstructioncited by 65
  8. 2020COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Imagescited by 39
  9. 2018Radon Inversion via Deep Learningcited by 35
  10. 2022Generative Modeling in Sinogram Domain for Sparse-view CT Reconstructioncited by 27
  11. 2023Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstructioncited by 18
  12. 2019A Review on Deep Learning in Medical Image Reconstructioncited by 17
3 more
  1. 2024Learning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstructioncited by 16
  2. 20243DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representationcited by 7
  3. 2024Discretized Gaussian Representation for Tomographic Reconstructioncited by 6

Built on by

Not cited yet.