Artificial Intelligence in Biomedical Image Analysis 2026

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1989

Special Issue Editor

Special Issue Information

Dear Colleagues,

The word “smart” that we frequently encounter in technology today is often powered by artificial intelligence (AI) and machine learning. Whether we are talking about a smart device or a smart application, AI and machine learning play a key role in the background. How effectively such a tool operates depends significantly on the selection of an appropriate AI technique, which enables best interpretation of the data and signals involved to achieve the desired outcome. This is a major reason why AI and machine learning are progressing rapidly and attracting growing attention from researchers and investors, especially in medicine.

This Special Issue, “Artificial Intelligence in Biomedical Image Analysis 2026,” will focus on utilizing advanced AI and machine learning methods in medical image analysis. Researchers are encouraged to submit original research articles or review articles discussing state-of-the-art AI techniques for biomedical imaging applications, including, but not limited to, the early diagnosis of various cancers and neurological disorders through imaging. Submitted articles are expected to provide a comprehensive description of the AI/machine learning methodology, the specific biomedical imaging application, and a clear presentation of the performance evaluation metrics used.

Dr. Jasjit S. Suri
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. Diagnostics 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

  • artificial intelligence
  • biomedical image analysis
  • diagnosis
  • cancer
  • neurological disorders

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Published Papers (1 paper)

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Research

13 pages, 10127 KB  
Article
Fine-Tuned Segment Anything Model with Low-Rank Adaptation for Chest X-Ray Images
by Saeed S. Alahmari, Michael R. Gardner, Fawaz Alqahtani and Tawfiq Salem
Diagnostics 2026, 16(6), 847; https://doi.org/10.3390/diagnostics16060847 - 12 Mar 2026
Viewed by 1120
Abstract
Background: This paper investigates the use of the Segment Anything Model (SAM) for chest X-ray (CXR) image segmentation, with a focus on improving its performance using low-rank adaptation (LoRA). Methods: We evaluate three versions of SAM: two zero-shot methods (using coordinate and bounding [...] Read more.
Background: This paper investigates the use of the Segment Anything Model (SAM) for chest X-ray (CXR) image segmentation, with a focus on improving its performance using low-rank adaptation (LoRA). Methods: We evaluate three versions of SAM: two zero-shot methods (using coordinate and bounding box prompts) and a fine-tuned SAM using LoRA. To support these approaches, we also trained two standard convolutional neural networks (CNNs), U-Net and DeepLabv3+, to generate draft lung segmentations that serve as input prompts for the SAM methods. Our fine-tuning approach uses LoRA to add lightweight trainable adapters within the Transformer blocks of the SAM, allowing only a small subset of parameters to be updated. The rest of the SAM remains frozen, helping preserve its pre-trained knowledge while reducing memory and computational needs. We tested all models on a dataset of CXR images labeled for COVID-19, viral pneumonia, and normal cases. Results: Results show that fine-tuned SAM with LoRA outperforms zero-shot SAM methods and CNN baselines in terms of segmentation accuracy and efficiency. Conclusions: This demonstrates the potential of combining LoRA with SAM for practical and effective medical image segmentation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Image Analysis 2026)
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