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Advances of Deep Learning in Medical Image Interpretation—2nd Edition
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Advances of Deep Learning in Medical Image Interpretation—2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 12 May 2027 | Viewed by 1058

Special Issue Editor

Dr. Zongwei Zhou

E-Mail Website
Guest Editor
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
Interests: medical computer vision; language and graphics for cancer detection and diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issues, “Advances of Deep Learning in Medical Image Interpretation” (https://www.mdpi.com/journal/sensors/special_issues/medical_image_interpretation), we announce the next in the series, “Advances of Deep Learning in Medical Image Interpretation—2nd Edition”.

Deep learning is transforming medical image interpretation, with the potential to provide automated, quantitative assessments alongside routine imaging. Medical images carry unique sensor-derived information, such as precise intensity scales, physical pixel spacing, and multi-modal data, which can be exploited to enhance deep learning performance. However, challenges remain: data heterogeneity, partial labels, and variability across imaging devices and clinical sites demand robust, generalizable methods.

This Special Issue builds on our successful first volume to connect innovation and clinical utility. We invite contributions advancing deep learning tailored to medical images while addressing critical challenges: data quality, reproducibility and clinical integration.

Dr. Zongwei Zhou
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • applications of medical imaging
  • image segmentation, registration, and fusion
  • representation learning, feature extraction
  • image reconstruction, image enhancement
  • microscopy image analysis
  • machine learning, deep learning
  • computer-aided diagnosis
  • image-guided interventions and surgery

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Published Papers (1 paper)

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Research

18 pages, 13021 KB  
Article
Dynamic Transformer Based on Wavelet and Diffusion Prior Guidance for Cardiac Cine MRI Reconstruction
by Bolun Zhao and Jun Lyu
Sensors 2026, 26(9), 2842; https://doi.org/10.3390/s26092842 - 1 May 2026
Viewed by 759
Abstract
Cardiac magnetic resonance imaging (CMR) is widely used for the diagnosis and functional assessment of cardiovascular diseases because of its noninvasive nature and excellent soft-tissue contrast. However, accelerated cine magnetic resonance imaging (cine MRI) acquisition usually relies on undersampling, which may lead to [...] Read more.
Cardiac magnetic resonance imaging (CMR) is widely used for the diagnosis and functional assessment of cardiovascular diseases because of its noninvasive nature and excellent soft-tissue contrast. However, accelerated cine magnetic resonance imaging (cine MRI) acquisition usually relies on undersampling, which may lead to noise, aliasing artifacts, and detail loss in reconstructed images. To address this issue, we propose a wavelet-guided dynamic Transformer with diffusion priors for cardiac cine MRI reconstruction. Specifically, a diffusion model is introduced into a reduced latent feature space to generate high-frequency prior features with only 8 reverse sampling steps, thereby enhancing detail recovery while maintaining moderate computational cost. In addition, a wavelet-guided dynamic Transformer is designed to capture low-frequency structural information and temporal dependencies across adjacent frames. By combining wavelet-domain decomposition, diffusion priors, and dynamic spatiotemporal modeling, the proposed framework improves reconstruction quality while preserving temporal consistency. Experimental results on multiple cardiac cine MRI datasets show that the proposed method achieves superior reconstruction accuracy and temporal consistency over several competing approaches, while maintaining a favorable balance between computational efficiency and reconstruction performance. These findings indicate that the proposed framework is an effective and robust solution for accelerated cardiac cine MRI reconstruction. Full article
(This article belongs to the Special Issue Advances of Deep Learning in Medical Image Interpretation—2nd Edition)
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