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Artificial Intelligence in Oncologic Imaging: Advances in Medical Image Segmentation...
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Artificial Intelligence in Oncologic Imaging: Advances in Medical Image Segmentation and Predictive Modelling

A special issue of Medical Sciences (ISSN 2076-3271).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1308

Special Issue Editors

Dr. Orazio Gambino

E-Mail Website
Guest Editor
Department of Engineering, University of Palermo, 90133 Palermo, Italy
Interests: artificial intelligence; biomedical image processing; decision-making systems; DICOM standard; graphical user interface
Dr. Salvatore Contino

E-Mail Website
Guest Editor
Department of Engineering, University of Palermo, 90133 Palermo, Italy
Interests: image processing; deep learning; multimodal AI; cancer therapy; drug discovery; DICOM standard

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence (AI) has transformed oncology by introducing powerful new methods for diagnosing, segmenting, and monitoring medical images across a wide spectrum of clinical applications. This Special Issue seeks original contributions that advance the integration of AI into oncologic imaging and related domains. Key areas of interest include radiomic analysis through multimodal deep neural networks, medical image segmentation for lesion detection, boundary delineation, treatment planning, as well as predictive modelling using machine learning, deep learning, and generative frameworks such as Vision–Language Models. These innovations enhance our capacity to interpret complex biomedical data, offering new insights into disease characterisation and supporting personalised therapeutic strategies. By highlighting current challenges and opportunities, this Special Issue aims to foster deeper integration of AI technologies within precision medicine.

Dr. Orazio Gambino
Dr. Salvatore Contino
Guest Editors

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. Medical Sciences is an international peer-reviewed open access quarterly 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 1600 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
  • medical image segmentation
  • oncologic imaging
  • radiomics
  • deep learning
  • machine learning
  • predictive modelling
  • vision language models
  • multimodal imaging
  • precision medicine

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Published Papers (2 papers)

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Research

21 pages, 3100 KB  
Article
A Lightweight Cross-Gated Dual-Branch Attention Network for Colon and Lung Cancer Diagnosis from Histopathological Images
by Raquel Ochoa-Ornelas, Alberto Gudiño-Ochoa, Sergio Octavio Rosales-Aguayo, Jesús Ezequiel Molinar-Solís, Sonia Espinoza-Morales and René Gudiño-Venegas
Med. Sci. 2025, 13(4), 286; https://doi.org/10.3390/medsci13040286 - 26 Nov 2025
Viewed by 387
Abstract
Background/Objectives: Accurate histopathological classification of lung and colon tissues remains difficult due to subtle morphological overlap between benign and malignant regions. Deep learning approaches have advanced diagnostic precision, yet models often lack interpretability or require complex multi-stage pipelines. This study aimed to develop [...] Read more.
Background/Objectives: Accurate histopathological classification of lung and colon tissues remains difficult due to subtle morphological overlap between benign and malignant regions. Deep learning approaches have advanced diagnostic precision, yet models often lack interpretability or require complex multi-stage pipelines. This study aimed to develop an end-to-end dual-branch attention network capable of achieving high accuracy while preserving computational efficiency and transparency. Methods: The architecture integrates EfficientNetV2-B0 and MobileNetV3-Small backbones through a cross-gated fusion mechanism that adaptively balances global context and fine structural details. Efficient channel attention and generalized mean pooling enhance discriminative learning without external feature extraction or optimization stages. Results: The network achieved 99.84% accuracy, precision, recall, and F1-score, with an MCC of 0.998. Grad-CAM maps showed strong spatial correspondence with diagnostically relevant histological structures. Conclusions: The end-to-end framework enables the reliable, interpretable, and computationally efficient classification of lung and colon histopathology and has potential applicability to computer-assisted diagnostic workflows. Full article
(This article belongs to the Special Issue Artificial Intelligence in Oncologic Imaging: Advances in Medical Image Segmentation and Predictive Modelling)
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16 pages, 3443 KB  
Article
Automated Detection and Grading of Renal Cell Carcinoma in Histopathological Images via Efficient Attention Transformer Network
by Hissa Al-kuwari, Belqes Alshami, Aisha Al-Khinji, Adnan Haider and Muhammad Arsalan
Med. Sci. 2025, 13(4), 257; https://doi.org/10.3390/medsci13040257 - 1 Nov 2025
Viewed by 544
Abstract
Background: Renal Cell Carcinoma (RCC) is the most common type of kidney cancer and requires accurate histopathological grading for effective prognosis and treatment planning. However, manual grading is time-consuming, subjective, and susceptible to inter-observer variability. Objective: This study proposes EAT-Net (Efficient Attention Transformer [...] Read more.
Background: Renal Cell Carcinoma (RCC) is the most common type of kidney cancer and requires accurate histopathological grading for effective prognosis and treatment planning. However, manual grading is time-consuming, subjective, and susceptible to inter-observer variability. Objective: This study proposes EAT-Net (Efficient Attention Transformer Network), a dual-stream deep learning model designed to automate and enhance RCC grade classification from histopathological images. Method: EAT-Net integrates EfficientNetB0 for local feature extraction and a Vision Transformer (ViT) stream for capturing global contextual dependencies. The architecture incorporates Squeeze-and-Excitation (SE) modules to recalibrate feature maps, improving focus on informative regions. The model was trained and evaluated on two publicly available datasets, KMC-RENAL and RCCG-Net. Standard preprocessing was applied, and the model’s performance was assessed using accuracy, precision, recall, and F1-score. Results: EAT-Net achieved superior results compared to state-of-the-art models, with an accuracy of 92.25%, precision of 92.15%, recall of 92.12%, and F1-score of 92.25%. Ablation studies demonstrated the complementary value of the EfficientNet and ViT streams. Additionally, Grad-CAM visualizations confirmed that the model focuses on diagnostically relevant areas, supporting its interpretability and clinical relevance. Conclusion: EAT-Net offers an accurate, and explainable framework for RCC grading. Its lightweight architecture and high performance make it well-suited for clinical deployment in digital pathology workflows. Full article
(This article belongs to the Special Issue Artificial Intelligence in Oncologic Imaging: Advances in Medical Image Segmentation and Predictive Modelling)
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