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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 September 2025 | Viewed by 16686

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Special Issue Editor

Dr. Shuyue Guan

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Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices, and Radiologic Health, United States Food and Drug Administration, Silver Spring, MD, USA
Interests: deep learning; medical and biomedical image processing; machine learning

Special Issue Information

Dear Colleagues,

Deep learning (DL) has been widely applied to various fields, such as computer vision, natural language processing, speech recognition, and bioinformatics. In particular, DL has shown its great potential and success in medical and biomedical image processing, which aims to analyze and interpret images acquired from different modalities, such as X-ray, CT, MRI, ultrasound, PET, and microscopy. DL can provide accurate and efficient solutions for various clinical applications, such as disease detection, diagnosis, prognosis, treatment planning, and evaluation. Some common tasks in medical and biomedical image processing are image classification, segmentation, registration, reconstruction, enhancement, and synthesis. DL can handle these tasks using different architectures and techniques, such as convolutional neural networks, recurrent neural networks, generative adversarial networks, attention mechanisms, and transfer learning. However, challenges and open issues in applying DL to medical and biomedical image processing exist, such as data availability, quality, diversity, model interpretability and explainability, model robustness and generalization, model validation and evaluation, model bias, and ethical aspects. Therefore, more research and collaborations are needed to address these challenges and further advance the DL field in medical and biomedical image processing.

Dr. Shuyue Guan
Guest Editor

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Keywords

deep learning
medical and biomedical image processing
model explainability and evaluation
generative model
machine learning

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Published Papers (7 papers)

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Research

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26 pages, 3982 KiB

Open AccessArticle

Building Better Deep Learning Models Through Dataset Fusion: A Case Study in Skin Cancer Classification with Hyperdatasets

by Panagiotis Georgiadis, Emmanouil V. Gkouvrikos, Eleni Vrochidou, Theofanis Kalampokas and George A. Papakostas

Diagnostics 2025, 15(3), 352; https://doi.org/10.3390/diagnostics15030352 - 3 Feb 2025

Cited by 1 | Viewed by 1522

Abstract

Background/Objectives: This work brings to light the importance of forming large training datasets with diverse images generated and proposes an image dataset merging application, namely, the Data Merger App, to streamline the management and synthesis of large-scale datasets. The Data Merger can recognize common classes across various datasets and provides tools to combine and organize them in a well-structured and easily accessible way. Methods: A case study is then presented, leveraging four different Convolutional Neural Network (CNN) models, VGG16, ResNet50, MobileNetV3-small, and DenseNet-161, and a Visual Transformer (ViT), to benchmark their performance to classify skin cancer images, when trained on single datasets and on enhanced hyperdatasets generated by the Data Merger App. Results: Extended experimental results indicated that enhanced hyperdatasets are efficient and able to improve the accuracies of classification models, whether the models are trained from scratch or by using Transfer Learning. Moreover, the ViT model was reported for higher classification accuracies compared to CNNs on datasets with a limited number of classes, reporting 91.87% accuracy for 9 classes, as well as in the case of enhanced hyperdatasets with multiple numbers of classes, reporting accuracy of 58% for 32 classes. Conclusions: In essence, this work demonstrates the great significance of data combination, as well as the utility value of the developed prototype web application as a critical tool for researchers and data scientists, enabling them to easily handle complex datasets, combine datasets into larger diverse versions, to further enhance the generalization ability of models and improve the quality and impact of their work. Full article

(This article belongs to the Special Issue Deep Learning in Medical and Biomedical Image Processing)

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15 pages, 2929 KiB

Open AccessArticle

TransRAUNet: A Deep Neural Network with Reverse Attention Module Using HU Windowing Augmentation for Robust Liver Vessel Segmentation in Full Resolution of CT Images

by Kyoung Yoon Lim, Jae Eun Ko, Yoo Na Hwang, Sang Goo Lee and Sung Min Kim

Diagnostics 2025, 15(2), 118; https://doi.org/10.3390/diagnostics15020118 - 7 Jan 2025

Viewed by 831

Abstract

Background: Liver cancer has a high mortality rate worldwide, and clinicians segment liver vessels in CT images before surgical procedures. However, liver vessels have a complex structure, and the segmentation process is conducted manually, so it is time-consuming and labor-intensive. Consequently, it would be extremely useful to develop a deep learning-based automatic liver vessel segmentation method. Method: As a segmentation method, UNet is widely used as a baseline, and a multi-scale block or attention module has been introduced to extract context information. In recent machine learning efforts, not only has the global context extraction been improved by introducing Transformer, but a method to reinforce the edge area has been proposed. However, the data preprocessing step still commonly uses general augmentation methods, such as flip, rotation, and mirroring, so it does not perform robustly on images of varying brightness or contrast levels. We propose a method of applying image augmentation with different HU windowing values. In addition, to minimize the false negative area, we propose TransRAUNet, which introduces a reverse attention module (RAM) that can focus edge information to the baseline TransUNet. The proposed architecture solves context loss for small vessels by applying edge module (RAM) in the upsampling phase. It can also generate semantic feature maps that allows it to learn edge, global context, and detail location by combining high-level edge and low-level context features. Results: In the 3Dricadb dataset, the proposed model achieved a DSC of 0.948 and a sensitivity of 0.944 in liver vessel segmentation. This study demonstrated that the proposed augmentation method is effective and robust by comparisons with the model without augmentation and with the general augmentation method. Additionally, an ablation study showed that RAM has improved segmentation performance compared to TransUNet. Compared to prevailing state-of-the-art methods, the proposed model showed the best performance for liver vessel segmentation. Conclusions: TransRAUnet is expected to serve as a navigation aid for liver resection surgery through accurate liver vessel and tumor segmentation. Full article

(This article belongs to the Special Issue Deep Learning in Medical and Biomedical Image Processing)

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11 pages, 749 KiB

Open AccessArticle

Caries Detection and Classification in Photographs Using an Artificial Intelligence-Based Model—An External Validation Study

by Elisabeth Frenkel, Julia Neumayr, Julia Schwarzmaier, Andreas Kessler, Nour Ammar, Falk Schwendicke, Jan Kühnisch and Helena Dujic

Diagnostics 2024, 14(20), 2281; https://doi.org/10.3390/diagnostics14202281 - 14 Oct 2024

Viewed by 1975

Abstract

Objective: This ex vivo diagnostic study aimed to externally validate a freely accessible AI-based model for caries detection, classification, localisation and segmentation using an independent image dataset. It was hypothesised that there would be no difference in diagnostic performance compared to previously published internal validation data. Methods: For the independent dataset, 718 dental images representing different stages of carious (n = 535) and noncarious teeth (n = 183) were retrieved from the internet. All photographs were evaluated by the dental team (reference standard) and the AI-based model (test method). Diagnostic performance was statistically determined using cross-tabulations to calculate accuracy (ACC), sensitivity (SE), specificity (SP) and area under the curve (AUC). Results: An overall ACC of 92.0% was achieved for caries detection, with an ACC of 85.5–95.6%, SE of 42.9–93.3%, SP of 82.1–99.4% and AUC of 0.702–0.909 for the classification of caries. Furthermore, 97.0% of the cases were accurately localised. Fully and partially correct segmentation was achieved in 52.9% and 44.1% of the cases, respectively. Conclusions: The validated AI-based model showed promising diagnostic performance in detecting and classifying caries using an independent image dataset. Future studies are needed to investigate the validity, reliability and practicability of AI-based models using dental photographs from different image sources and/or patient groups. Full article

(This article belongs to the Special Issue Deep Learning in Medical and Biomedical Image Processing)

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16 pages, 4377 KiB

Open AccessArticle

Hybrid Deep Learning Framework for Melanoma Diagnosis Using Dermoscopic Medical Images

by Muhammad Mateen, Shaukat Hayat, Fizzah Arshad, Yeong-Hyeon Gu and Mugahed A. Al-antari

Diagnostics 2024, 14(19), 2242; https://doi.org/10.3390/diagnostics14192242 - 8 Oct 2024

Cited by 1 | Viewed by 2194

Abstract

Background: Melanoma, or skin cancer, is a dangerous form of cancer that is the major cause of the demise of thousands of people around the world. Methods: In recent years, deep learning has become more popular for analyzing and detecting these medical issues. In this paper, a hybrid deep learning approach has been proposed based on U-Net for image segmentation, Inception-ResNet-v2 for feature extraction, and the Vision Transformer model with a self-attention mechanism for refining the features for early and accurate diagnosis and classification of skin cancer. Furthermore, in the proposed approach, hyperparameter tuning helps to obtain more accurate and optimized results for image classification. Results: Dermoscopic shots gathered by the worldwide skin imaging collaboration (ISIC2020) challenge dataset are used in the proposed research work and achieved 98.65% accuracy, 99.20% sensitivity, and 98.03% specificity, which outperforms the other existing approaches for skin cancer classification. Furthermore, the HAM10000 dataset is used for ablation studies to compare and validate the performance of the proposed approach. Conclusions: The achieved outcome suggests that the proposed approach would be able to serve as a valuable tool for assisting dermatologists in the early detection of melanoma. Full article

(This article belongs to the Special Issue Deep Learning in Medical and Biomedical Image Processing)

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12 pages, 1825 KiB

Open AccessArticle

Diagnostic Performance of a Deep Learning-Powered Application for Aortic Dissection Triage Prioritization and Classification

by Vladimir Laletin, Angela Ayobi, Peter D. Chang, Daniel S. Chow, Jennifer E. Soun, Jacqueline C. Junn, Marlene Scudeler, Sarah Quenet, Maxime Tassy, Christophe Avare, Mar Roca-Sogorb and Yasmina Chaibi

Diagnostics 2024, 14(17), 1877; https://doi.org/10.3390/diagnostics14171877 - 27 Aug 2024

Cited by 3 | Viewed by 2045

Abstract

This multicenter retrospective study evaluated the diagnostic performance of a deep learning (DL)-based application for detecting, classifying, and highlighting suspected aortic dissections (ADs) on chest and thoraco-abdominal CT angiography (CTA) scans. CTA scans from over 200 U.S. and European cities acquired on 52 scanner models from six manufacturers were retrospectively collected and processed by CINA-CHEST (AD) (Avicenna.AI, La Ciotat, France) device. The diagnostic performance of the device was compared with the ground truth established by the majority agreement of three U.S. board-certified radiologists. Furthermore, the DL algorithm’s time to notification was evaluated to demonstrate clinical effectiveness. The study included 1303 CTAs (mean age 58.8 ± 16.4 years old, 46.7% male, 10.5% positive). The device demonstrated a sensitivity of 94.2% [95% CI: 88.8–97.5%] and a specificity of 97.3% [95% CI: 96.2–98.1%]. The application classified positive cases by the AD type with an accuracy of 99.5% [95% CI: 98.9–99.8%] for type A and 97.5 [95% CI: 96.4–98.3%] for type B. The application did not miss any type A cases. The device flagged 32 cases incorrectly, primarily due to acquisition artefacts and aortic pathologies mimicking AD. The mean time to process and notify of potential AD cases was 27.9 ± 8.7 s. This deep learning-based application demonstrated a strong performance in detecting and classifying aortic dissection cases, potentially enabling faster triage of these urgent cases in clinical settings. Full article

(This article belongs to the Special Issue Deep Learning in Medical and Biomedical Image Processing)

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Review

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19 pages, 1152 KiB

Open AccessReview

Foundational Models for Pathology and Endoscopy Images: Application for Gastric Inflammation

by Hamideh Kerdegari, Kyle Higgins, Dennis Veselkov, Ivan Laponogov, Inese Polaka, Miguel Coimbra, Junior Andrea Pescino, Mārcis Leja, Mário Dinis-Ribeiro, Tania Fleitas Kanonnikoff and Kirill Veselkov

Diagnostics 2024, 14(17), 1912; https://doi.org/10.3390/diagnostics14171912 - 30 Aug 2024

Cited by 2 | Viewed by 2427

Abstract

The integration of artificial intelligence (AI) in medical diagnostics represents a significant advancement in managing upper gastrointestinal (GI) cancer, which is a major cause of global cancer mortality. Specifically for gastric cancer (GC), chronic inflammation causes changes in the mucosa such as atrophy, intestinal metaplasia (IM), dysplasia, and ultimately cancer. Early detection through endoscopic regular surveillance is essential for better outcomes. Foundation models (FMs), which are machine or deep learning models trained on diverse data and applicable to broad use cases, offer a promising solution to enhance the accuracy of endoscopy and its subsequent pathology image analysis. This review explores the recent advancements, applications, and challenges associated with FMs in endoscopy and pathology imaging. We started by elucidating the core principles and architectures underlying these models, including their training methodologies and the pivotal role of large-scale data in developing their predictive capabilities. Moreover, this work discusses emerging trends and future research directions, emphasizing the integration of multimodal data, the development of more robust and equitable models, and the potential for real-time diagnostic support. This review aims to provide a roadmap for researchers and practitioners in navigating the complexities of incorporating FMs into clinical practice for the prevention/management of GC cases, thereby improving patient outcomes. Full article

(This article belongs to the Special Issue Deep Learning in Medical and Biomedical Image Processing)

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25 pages, 3105 KiB

Open AccessReview

Deep Learning for Alzheimer’s Disease Prediction: A Comprehensive Review

by Isra Malik, Ahmed Iqbal, Yeong Hyeon Gu and Mugahed A. Al-antari

Diagnostics 2024, 14(12), 1281; https://doi.org/10.3390/diagnostics14121281 - 17 Jun 2024

Cited by 8 | Viewed by 5008

Abstract

Alzheimer’s disease (AD) is a neurological disorder that significantly impairs cognitive function, leading to memory loss and eventually death. AD progresses through three stages: early stage, mild cognitive impairment (MCI) (middle stage), and dementia. Early diagnosis of Alzheimer’s disease is crucial and can improve survival rates among patients. Traditional methods for diagnosing AD through regular checkups and manual examinations are challenging. Advances in computer-aided diagnosis systems (CADs) have led to the development of various artificial intelligence and deep learning-based methods for rapid AD detection. This survey aims to explore the different modalities, feature extraction methods, datasets, machine learning techniques, and validation methods used in AD detection. We reviewed 116 relevant papers from repositories including Elsevier (45), IEEE (25), Springer (19), Wiley (6), PLOS One (5), MDPI (3), World Scientific (3), Frontiers (3), PeerJ (2), Hindawi (2), IO Press (1), and other multiple sources (2). The review is presented in tables for ease of reference, allowing readers to quickly grasp the key findings of each study. Additionally, this review addresses the challenges in the current literature and emphasizes the importance of interpretability and explainability in understanding deep learning model predictions. The primary goal is to assess existing techniques for AD identification and highlight obstacles to guide future research. Full article

(This article belongs to the Special Issue Deep Learning in Medical and Biomedical Image Processing)

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