mlsanityData to 7 Oct 2026

A Perspective on Deep Imaging

arXiv 1609.04375 ↗First posted 10 Sep 2016q-bio.QM cs.CV cs.LGcited by 16

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Abstract (arXiv)

The combination of tomographic imaging and deep learning, or machine learning in general, promises to empower not only image analysis but also image reconstruction. The latter aspect is considered in this perspective article with an emphasis on medical imaging to develop a new generation of image reconstruction theories and techniques. This direction might lead to intelligent utilization of domain knowledge from big data, innovative approaches for image reconstruction, and superior performance in clinical and preclinical applications. To realize the full impact of machine learning on medical imaging, major challenges must be addressed.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Sep 2016.

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Citing papers per year: 2022: 4; 2023: 6; 2024: 1; 2025: 4; 2026: 1.

16 papers.

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  1. 2022Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Datacited by 17
  2. 2023LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurringcited by 12
  3. 2023DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imagingcited by 12
  4. 2022An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networkscited by 8
  5. 2023Diffusion Prior Regularized Iterative Reconstruction for Low-dose CTcited by 8
  6. 2023Parallel Diffusion Model-based Sparse-view Cone-beam Breast CTcited by 7
  7. 2024Low-dose CT Denoising with Language-engaged Dual-space Alignmentcited by 7
  8. 2022Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20× Speedupcited by 3
  9. 2025Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Enginecited by 3
  10. 2025LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Modelscited by 3
  11. 2023Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligencecited by 2
  12. 2022Segmentation-free PVC for Cardiac SPECT using a Densely-connected Multi-dimensional Dynamic Networkcited by 1
4 more
  1. 2023Learned reconstruction methods for inverse problems: sample error estimatescited by 1
  2. 2025Hallucinations in medical devicescited by 0
  3. 2025InSituRes: A Physics-Informed Same-Grid Model for Enhanced Dynamic X-ray Micro-CT Reconstructionscited by 0
  4. 2026Deep learning Based Correction Algorithms for 3D Medical Reconstruction in Computed Tomography and Macroscopic Imagingcited by 0