Journal of Imaging

Research

27 pages, 18530 KB

Open AccessArticle

Improving Parameter-Efficient Medical Image Classification with Lesion-Aware Hierarchical Knowledge Distillation

by Yarong Liu, Runmei Xie, Xiaolan Xie and Huilin Zheng

J. Imaging 2026, 12(9), 437; https://doi.org/10.3390/jimaging12090437 - 11 Sep 2026

Viewed by 109

Compact deep models are attractive for medical image classification, but conventional knowledge distillation mainly transfers class-level predictions and may not adequately preserve lesion-relevant spatial cues. To address this limitation, we propose LaHKD, a lesion-aware hierarchical knowledge distillation framework for parameter-efficient medical image classification. [...] Read more.

Compact deep models are attractive for medical image classification, but conventional knowledge distillation mainly transfers class-level predictions and may not adequately preserve lesion-relevant spatial cues. To address this limitation, we propose LaHKD, a lesion-aware hierarchical knowledge distillation framework for parameter-efficient medical image classification. LaHKD enables a lightweight student to learn hierarchical semantic representations together with lesion-focused guidance from a stronger teacher. We evaluate LaHKD on HAM10000 dermoscopic lesion classification and a brain tumor MRI classification benchmark. Across both datasets, LaHKD improves compact-student classification performance, with the clearest lesion-focused spatial benefits observed on HAM10000, where lesion morphology is central to diagnosis and direct lesion supervision is available. On the magnetic resonance imaging (MRI) benchmark, localization analysis is limited to an auxiliary recovered-mask subset and is therefore interpreted as exploratory; under this setting, consistent localization gains are not observed. Overall, LaHKD provides an effective framework for compact medical image classification, with spatial benefits most clearly supported in tasks with reliable lesion supervision. Full article

(This article belongs to the Special Issue AI-Driven Medical Image Processing and Analysis)

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20 pages, 600 KB

Open AccessArticle

Selective Confidence-Guided Projection-Based Encoding for Medical Image Classification

by Tao Chen, Chuan Zhou, Yifan Wang, Lubomir M. Hadjiiski and Qian Dong

J. Imaging 2026, 12(9), 436; https://doi.org/10.3390/jimaging12090436 - 11 Sep 2026

Viewed by 130

Abstract

Deep neural networks have achieved strong performance in medical image classification, but their deployment may be constrained by the computational cost of high-capacity models. Knowledge distillation (KD) addresses this problem by transferring knowledge from a teacher to a lightweight student. However, the reliability [...] Read more.

Deep neural networks have achieved strong performance in medical image classification, but their deployment may be constrained by the computational cost of high-capacity models. Knowledge distillation (KD) addresses this problem by transferring knowledge from a teacher to a lightweight student. However, the reliability of teacher supervision may vary across samples, potentially introducing noisy guidance and local conflicts with ground-truth supervision. We propose Selective Confidence-guided Projection-based Encoding (SCOPE), a conflict-aware KD framework comprising Selective Relation Alignment (SRA) and Gradient Conflict Resolution (GCR). SRA constructs reliability-aware relational supervision by combining teacher-derived relations with dataset-specific auxiliary priors, whereas GCR removes distillation-gradient components that conflict with the classification objective. Experiments on nine medical image datasets and multiple teacher–student architectures demonstrate competitive predictive performance, improved training stability, and low computational overhead. Full article

(This article belongs to the Special Issue AI-Driven Medical Image Processing and Analysis)

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21 pages, 8413 KB

Open AccessArticle

A Two-Stage Ensemble Machine Learning Pipeline for Breast Cancer Diagnosis from Digital Mammograms

by Fernando Martín-Rodríguez, Carmen Freire-Bouza, Mónica Fernández-Barciela, Ainhoa Morales-Fernández and María Marante-Boado

J. Imaging 2026, 12(8), 378; https://doi.org/10.3390/jimaging12080378 - 12 Aug 2026

Viewed by 329

Abstract

Breast cancer is the most common cancer among women, and early detection through mammography is essential for reducing mortality. Artificial intelligence can support radiologists by improving diagnostic accuracy. To develop and evaluate a two-stage ensemble machine learning pipeline for breast cancer diagnosis from [...] Read more.

Breast cancer is the most common cancer among women, and early detection through mammography is essential for reducing mortality. Artificial intelligence can support radiologists by improving diagnostic accuracy. To develop and evaluate a two-stage ensemble machine learning pipeline for breast cancer diagnosis from digital mammograms. The proposed framework combines image preprocessing, multiple convolutional neural networks trained under different conditions, and a second-stage classifier that integrates the CNN outputs. Several machine learning models and feature selection techniques were evaluated using publicly available mammography datasets. Results: The ensemble approach consistently outperformed the individual CNN models. The MLP classifier achieved the best overall balance between precision and recall, while the heuristic fusion method provided the highest sensitivity. Feature selection reduced model complexity while maintaining comparable performance, and cross-validation confirmed the robustness of the proposed methodology. Combining complementary information from multiple CNNs with classical machine learning improves diagnostic performance and provides a robust framework for computer-aided breast cancer diagnosis. The proposed two-stage ensemble offers an effective and interpretable approach for mammographic breast cancer classification. A demonstration application incorporating Grad-CAM explainability further supports its potential use as a clinical decision-support tool. Full article

(This article belongs to the Special Issue AI-Driven Medical Image Processing and Analysis)

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25 pages, 1685 KB

Open AccessArticle

Organ Segmentation with Machine Learning Models

by Alexandros Barmperis, Olga Menegaki, Anna Panagiotakopoulou, Andreas Vezakis, Ioannis Vezakis, Ioannis Kakkos and George K. Matsopoulos

J. Imaging 2026, 12(8), 335; https://doi.org/10.3390/jimaging12080335 - 24 Jul 2026

Viewed by 687

Abstract

Accurate segmentation of abdominal organs in Computed Tomography (CT) underpins radiotherapy planning, surgical planning, and disease monitoring. Existing benchmarks rank architectures by a single aggregate Dice score, without per-organ statistical testing or boundary-sensitive metrics, even though models are chosen organ by organ for [...] Read more.

Accurate segmentation of abdominal organs in Computed Tomography (CT) underpins radiotherapy planning, surgical planning, and disease monitoring. Existing benchmarks rank architectures by a single aggregate Dice score, without per-organ statistical testing or boundary-sensitive metrics, even though models are chosen organ by organ for clinical use. We benchmark ten architectures spanning convolutional, attention-based, transformer, and state–space (Mamba) families on the AMOS CT dataset under one identical nnU-Net-style pipeline; we report per-organ Dice, 95-percentile Hausdorff Distance (HD95), and Normalised Surface Dice, with pairwise significance tested on an independent external dataset (TotalSegmentator). A competitive cluster of convolutional and Mamba models leads; rankings are stable on large organs but reshuffle by 10–13% on the small, geometrically complex ones, and boundary fidelity separates the models into tiers that the Dice ranking hides. This ordering largely holds on the external set (Spearman

ρ = 0.84

). Selecting a model on aggregate Dice alone is therefore unsafe for organ-specific clinical tasks: per-organ overlap and boundary metrics should be the primary acceptance criteria for selecting a model before clinical deployment. Full article

(This article belongs to the Special Issue AI-Driven Medical Image Processing and Analysis)

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22 pages, 3083 KB

Open AccessArticle

NS-GUSL: Green U-Shaped Learning for Nuclei Segmentation from Histopathology Images

by Catherine Aurelia Christie Alexander, Vasileios Magoulianitis, Jiaxin Yang and C.-C. Jay Kuo

J. Imaging 2026, 12(7), 316; https://doi.org/10.3390/jimaging12070316 - 10 Jul 2026

Viewed by 423

Abstract

Nuclei segmentation is a key task in digital histopathology, highlighting important aspects of nuclear morphology and topology in many cancer-related evaluations and studies. Variability in nuclear appearance both within and across different organs, stain heterogeneity, and inconsistencies in acquisition procedures contribute to the [...] Read more.

Nuclei segmentation is a key task in digital histopathology, highlighting important aspects of nuclear morphology and topology in many cancer-related evaluations and studies. Variability in nuclear appearance both within and across different organs, stain heterogeneity, and inconsistencies in acquisition procedures contribute to the complexity of the task. The existing nuclei segmentation methods apply deep learning to address these challenges, using models with millions of parameters, thereby significantly increasing computational complexity. They also face limitations in generalizing to unseen organs and slide preparations. In this paper, we propose a transparent and lightweight Green U-Shaped Learning model for nuclei segmentation (NS-GUSL). NS-GUSL features a multi-scale architecture for coarse-to-fine refinement of probability maps, which are subsequently binarized using a novel low-confidence sample binarization (LCSB) technique. The model features a modular, feed-forward feature learning scheme with unsupervised representation learning and supervised feature selection and generation. A final morphological post-processing step refines the segmentation maps to improve instance separation while preserving nuclei convexity. The model was trained and tested on the MoNuSeg dataset and compared against other deep learning baselines for segmentation performance. In addition, external validation experiments were conducted to evaluate the proposed model’s generalizability to unseen organs and staining procedures. NS-GUSL exhibits the best panoptic segmentation performance and competitive detection quality across all datasets. Moreover, our model is shown to be compact, low in computational complexity, and to have a minimal carbon footprint, compared to other deep learning models, making it a suitable choice for deployment on edge devices. Full article

(This article belongs to the Special Issue AI-Driven Medical Image Processing and Analysis)

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54 pages, 15371 KB

Open AccessArticle

Explainable Two-Stage Xception-Swin Transformer Learning for Body-Part-Aware Fracture Detection in Musculoskeletal X-Rays

by Syed Baqir Hussain Shah, Musfarah Wajid, Syed Adil Hussain Shah, Silvia Godio, Karim Kassem, Gohar Bano Zaidi, Shahzad Ahmad Qureshi, Syed Taimoor Hussain Shah and Marco Agostino Deriu

J. Imaging 2026, 12(7), 298; https://doi.org/10.3390/jimaging12070298 - 3 Jul 2026

Viewed by 523

Abstract

Accurate automated interpretation of upper-extremity musculoskeletal radiographs remains challenging because fracture appearance varies across anatomical regions and can be subtle under class imbalance. This study proposes a two-stage deep learning framework for MURA-based X-ray analysis, aiming to improve body-part recognition and body-part-wise abnormality [...] Read more.

Accurate automated interpretation of upper-extremity musculoskeletal radiographs remains challenging because fracture appearance varies across anatomical regions and can be subtle under class imbalance. This study proposes a two-stage deep learning framework for MURA-based X-ray analysis, aiming to improve body-part recognition and body-part-wise abnormality detection. Multiple architectures were first compared for seven-class body-part classification, after which the selected hybrid Xception-Swin model was fine-tuned for abnormality detection within each anatomical subset. The framework combines Xception-derived local structural features with Swin Transformer contextual features using attention-based fusion, and performance was evaluated using accuracy, F1-score, AUC-ROC, Cohen’s kappa, calibration, component-level ablation, post hoc explainability, and zero-shot FracAtlas validation. For body-part classification, the model achieved accuracy = 0.9643, macro F1 = 0.9574, AUC-ROC = 0.9963, and kappa = 0.9579. For abnormality detection, accuracy ranged from 0.7289 to 0.8538, F1 from 0.7191 to 0.8508, AUC from 0.7693 to 0.9080, and kappa from 0.4449 to 0.7071. Ablation on hand and humerus radiographs showed the highest macro F1 with Hybrid Attention, while FracAtlas validation yielded AUC = 0.8247 and kappa = 0.5812. The results support complementary CNN-Transformer fusion and indicate preliminary cross-dataset generalizability. Implementation resources are available at Zenodo. Full article

(This article belongs to the Special Issue AI-Driven Medical Image Processing and Analysis)

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30 pages, 13254 KB

Open AccessArticle

MBRSNet: Boundary-Aware Multi-Task Learning with Signed Distance Field Regression for Polyp Segmentation

by Ruishi Lin and Liyong Ma

J. Imaging 2026, 12(7), 278; https://doi.org/10.3390/jimaging12070278 - 24 Jun 2026

Viewed by 673

Abstract

Accurate polyp segmentation in colonoscopic images remains challenging due to low contrast, irregular morphology, and significant distribution shifts across datasets, which often lead to unreliable boundary delineation and poor generalization. Existing methods typically treat boundary information as an auxiliary cue or incorporate boundary [...] Read more.

Accurate polyp segmentation in colonoscopic images remains challenging due to low contrast, irregular morphology, and significant distribution shifts across datasets, which often lead to unreliable boundary delineation and poor generalization. Existing methods typically treat boundary information as an auxiliary cue or incorporate boundary information through hand-crafted architectural designs, resulting in limited integration between boundary-sensitive features and region-aware representations. In this paper, we propose a boundary-aware multi-task learning framework, termed MBRSNet, which explicitly models and exploits the complementarity between the segmentation task and the auxiliary signed distance field (SDF) regression task. Specifically, we formulate boundary modeling as an auxiliary SDF regression task, providing dense and continuous structural supervision without requiring additional annotations. To effectively couple the two tasks, we design a cross-gated multi-task bottleneck that enables bidirectional and selective feature interaction, allowing each task to selectively leverage complementary information while suppressing task-irrelevant responses. Furthermore, a hierarchical cross-task guidance strategy is introduced in the decoding stage, where boundary-aware weighting and segmentation-guided alignment jointly refine multi-scale features, ensuring consistent integration of boundary cues and regional semantics. Extensive experiments on five benchmark datasets demonstrate that MBRSNet achieves competitive or superior performance compared with representative state-of-the-art methods in both segmentation accuracy and cross-dataset generalization. In particular, the proposed framework achieves superior boundary delineation under challenging conditions and exhibits strong robustness to domain shifts, highlighting the effectiveness of structured task interaction for boundary-aware medical image segmentation. Full article

(This article belongs to the Special Issue AI-Driven Medical Image Processing and Analysis)

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19 pages, 35689 KB

Open AccessArticle

Computed Fluid Dynamics-Based Blood Pressure Prediction for Coronary Artery Disease Diagnosis Using Coronary Computed Tomography Angiography

by Rene Lisasi, Huan Huang, William Pei, Michele Esposito and Chen Zhao

J. Imaging 2026, 12(5), 196; https://doi.org/10.3390/jimaging12050196 - 2 May 2026

Viewed by 689

Abstract

Computational fluid dynamics (CFD)-based simulation of coronary blood flow provides valuable hemodynamic markers, such as pressure gradients, for diagnosing coronary artery disease (CAD). However, CFD is computationally expensive, time-consuming, and difficult to integrate into large-scale clinical workflows. These limitations restrict the availability of [...] Read more.

Computational fluid dynamics (CFD)-based simulation of coronary blood flow provides valuable hemodynamic markers, such as pressure gradients, for diagnosing coronary artery disease (CAD). However, CFD is computationally expensive, time-consuming, and difficult to integrate into large-scale clinical workflows. These limitations restrict the availability of labeled hemodynamic data for training AI models and hinder the broad adoption of non-invasive, physiology-based CAD assessment. To address these challenges, we develop an end-to-end pipeline that automates coronary geometry extraction from coronary computed tomography angiography (CCTA), streamlines simulation data generation, and enables efficient learning of coronary blood pressure distributions. The pipeline reduces the manual burden associated with traditional CFD workflows while producing consistent training data. Furthermore, we introduce a diffusion-based regression model. Specifically, the inverted conditional diffusion (ICD) model is designed to predict coronary blood pressure directly from CCTA-derived features, thereby bypassing the need for computationally intensive CFD during inference. The proposed model is trained and validated on two CCTA datasets using the Adam optimizer with a weight decay of

1 \times 10^{- 3}

, a learning rate of

1 \times 10^{- 5}

, a batch size of 100, and Huber loss. It is then evaluated on a test set of ten simulated coronary hemodynamic cases. Experimental results demonstrate state-of-the-art performance. Compared with Long Short-Term Memory (LSTM), the proposed model improves the

R^{2}

score by 19.78%, reduces the root mean squared error (RMSE) by 19.44%, and lowers the normalized root mean squared error (NRMSE) by 18%. Compared with a multilayer perceptron (MLP), it improves the

R^{2}

score by 8.38%, reduces RMSE by 4.3%, and reduces NRMSE by 5.4%. This work represents a first step toward a scalable and accessible framework for rapid, non-invasive, CFD-based blood pressure prediction, with the potential to support CAD diagnosis. Full article

(This article belongs to the Special Issue AI-Driven Medical Image Processing and Analysis)

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