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Breast cancer classification in point-of-care ultrasound imaging—the impact of training data

Journal of Medical Imaging 2025年第1期12卷 014502页

作者： Karlsson, Jennie Arvidsson, Ida Sahlin, Freja Åström, Kalle Overgaard, Niels Christian Lång, Kristina Heyden, Anders Lund University Centre for Mathematical Sciences Division of Computer Vision and Machine Learning Lund Sweden Lund University Division of Diagnostic Radiology Department of Translational Medicine Lund Sweden Skåne University Hospital Unilabs Mammography Unit Malmö Sweden

Purpose: The survival rate of breast cancer for women in low- and middle-income countries is poor compared with that in high-income countries. Point-of-care ultrasound (POCUS) combined with deep learning could potentially be a suitable solution enabling early detection of breast cancer. We aim to improve a classification network dedicated to classifying POCUS images by comparing different techniques for increasing the amount of training data. Approach: Two data sets consisting of breast tissue images were collected, one captured with POCUS and another with standard ultrasound (US). The data sets were expanded by using different techniques, including augmentation, histogram matching, histogram equalization, and cycle-consistent adversarial networks (CycleGANs). A classification network was trained on different combinations of the original and expanded data sets. Different types of augmentation were investigated and two different CycleGAN approaches were implemented. Results: Almost all methods for expanding the data sets significantly improved the classification results compared with solely using POCUS images during the training of the classification network. When training the classification network on POCUS and CycleGAN-generated POCUS images, it was possible to achieve an area under the receiver operating characteristic curve of 95.3% (95% confidence interval 93.4% to 97.0%). Conclusions: Applying augmentation during training showed to be important and increased the performance of the classification network. Adding more data also increased the performance, but using standard US images or CycleGAN-generated POCUS images gave similar results. © The Authors.

关键词： Convolutional neural networks

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Pulmofusion: Advancing Pulmonary Health with Efficient Multi-Modal Fusion

Pulmofusion: Advancing Pulmonary Health with Efficient Multi...

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IEEE International Symposium on Biomedical Imaging

作者： Ahmed Sharshar Yasser Attia Mohammad Yaqub Mohsen Guizani Department of Computer Vision MBZUAI UAE Department of Machine Learning MBZUAI UAE

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Traditional remote spirometry lacks the precision required for effective pulmonary monitoring. We present a novel, non-invasive approach using multimodal predictive models that integrate RGB or thermal video data with patient meta-data. Our method leverages energy-efficient Spiking Neural Networks (SNNs) for the regression of Peak Expiratory Flow (PEF) and classification of Forced Expiratory Volume (FEVl) and Forced Vital Capacity (FVC), using lightweight CNNs to overcome SNN limitations in regression tasks. Multimodal data integration is improved with a Multi-Head Attention Layer, and we employ K-Fold validation and en-semble learning to boost robustness. Using thermal data, our SNN models achieve 92% ± 2% accuracy on a breathing-cycle basis and 99.5% ± 0.5% patient-wise. PEF regression models attain Relative RMSEs of 0.11 ± 0.05 (thermal) and 0.26 ± 0.07 (RGB), with an MAE of 4.52% for FEV1/FVC predictions, establishing state-of-the-art performance. 1 1 Code and dataset can be found on https://***-sharshar/***

关键词： Spirometry Accuracy Lungs Scalability Spiking neural networks Infrared imaging Predictive models Metadata Robustness Monitoring

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PULMOFUSION: ADVANCING PULMONARY HEALTH WITH EFFICIENT MULTI-MODAL FUSION

arXiv

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arXiv 2025年

作者： Sharshar, Ahmed Attia, Yasser Yaqub, Mohammad Guizani, Mohsen Department of Computer Vision MBZUAI United Arab Emirates Department of Machine Learning MBZUAI United Arab Emirates

Traditional remote spirometry lacks the precision required for effective pulmonary monitoring. We present a novel, non-invasive approach using multimodal predictive models that integrate RGB or thermal video data with patient metadata. Our method leverages energy-efficient Spiking Neural Networks (SNNs) for the regression of Peak Expiratory Flow (PEF) and classification of Forced Expiratory Volume (FEV1) and Forced Vital Capacity (FVC), using lightweight CNNs to overcome SNN limitations in regression tasks. Multimodal data integration is improved with a Multi-Head Attention Layer, and we employ K-Fold validation and ensemble learning to boost robustness. Using thermal data, our SNN models achieve 92% ± 2% accuracy on a breathing-cycle basis and 99.5% ± 0.5% patient-wise. PEF regression models attain Relative RMSEs of 0.11 ± 0.05 (thermal) and 0.26 ± 0.07 (RGB), with an MAE of 4.52% for FEV1/FVC predictions, establishing state-of-the-art performance.1 © 2025, CC BY.

关键词： Lung cancer

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An Enhancement of Object Detection Using YOLO V8 and Mobile Net in Challenging Conditions

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SN computer Science 2025年第4期6卷 1-20页

作者： Pasupuleti, Shailaja Ramalakshmi, K. Gunasekaran, Hemalatha Arokiaraj, Rex Macedo Debnath, Saswati Jebaseeli, T. Jemima Department of Computer Science and Engineering Alliance University Karnataka Anekal Bangalore India AU-Centre of Excellence Department of Computer Vision Alliance University Karnataka Anekal Bangalore India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Alliance University Karnataka Bangalore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

Autonomous drones and deep learning neural networks are becoming popular tools to revolutionize aircraft operations by partially automating visual inspection processes in aircraft maintenance. The research aims to provide a trustworthy method for locating and classifying surface flaws on aircraft, such as dents, fractures, corrosion, and a few other anomalies. To assess the model performance with and without a lightweight network integration, the implementation merges YOLOv8 and Mobile Net architectures, using dropout regularization and data augmentation approaches. To guarantee the model's performance in low-light situations, the dataset has been carefully selected to contain images replicating such settings. Annotation, environment setup, model training, and online dataset scraping are all covered by the thorough technique. A web-scraped dataset is used to train and validate two popular semantic segmentation algorithms, YOLOv8 and Mobile Net, which show enough performance for segmenting and grading aircraft surface problems in practical settings. The results show that precision and recall values are improved using a hybrid technique that ensembles trained Mobile Net and YOLOv8 models. Using COCO metrics, in the proposed approach an assessment of Ensemble WBF, Mobile Net-YOLO, and YOLOv8 outcomes on test and validation sets, guaranteed reliable object recognition and classification abilities. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aircraft Classification Mobile-Net Object detection Weighted Box Fusion YOLOv8

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A Comprehensive Analysis of Mamba for 3D Volumetric Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Wang, Chaohan Xie, Yutong Chen, Qi Zhou, Yuyin Wu, Qi Australian Institute for Machine Learning The University of Adelaide Adelaide Australia Computer Vision Department MBZUAI Abu Dhabi United Arab Emirates Computer Science and Engineering Department UC Santa Cruz Santa Cruz United States

Mamba, with its selective State Space Models (SSMs), offers a more computationally efficient solution than Transformers for long-range dependency modeling. However, there is still a debate about its effectiveness in high-resolution 3D medical image segmentation. In this study, we present a comprehensive investigation into Mamba’s capabilities in 3D medical image segmentation by tackling three pivotal questions: Can Mamba replace Transformers? Can it elevate multi-scale representation learning? Is complex scanning necessary to unlock its full potential? We evaluate Mamba’s performance across three large public benchmarks—AMOS, TotalSegmentator, and BraTS. Our findings reveal that UlikeMamba, a U-shape Mamba-based network, consistently surpasses UlikeTrans, a U-shape Transformer-based network, particularly when enhanced with custom-designed 3D depthwise convolutions, boosting accuracy and computational efficiency. Further, our proposed multi-scale Mamba block demonstrates superior performance in capturing both fine-grained details and global context, especially in complex segmentation tasks, surpassing Transformer-based counterparts. We also critically assess complex scanning strategies, finding that simpler methods often suffice, while our Tri-scan approach delivers notable advantages in the most challenging scenarios. By integrating these advancements, we introduce a new network for 3D medical image segmentation, positioning Mamba as a transformative force that outperforms leading models such as nnUNet, CoTr, and U-Mamba, offering competitive accuracy with superior computational efficiency. This study provides key insights into Mamba’s unique advantages, paving the way for more efficient and accurate approaches to 3D medical imaging. © 2025, CC BY-NC-ND.

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UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer Model

arXiv

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arXiv 2025年

作者： Jankovic, Branislava Jangirova, Sabina Ullah, Waseem Khan, Latif U. Guizani, Mohsen Computer Vision Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Dangerous surroundings and difficult-to-reach landscapes introduce significant complications for adequate disaster management and recuperation. These problems can be solved by engaging unmanned aerial vehicles (UAVs) provided with embedded platforms and optical sensors. In this work, we focus on enabling onboard aerial image processing to ensure proper and real-time disaster detection. Such a setting usually causes challenges due to the limited hardware resources of UAVs. However, privacy, connectivity, and latency issues can be avoided. We suggest a UAV-assisted edge framework for disaster detection, leveraging our proposed model optimized for onboard real-time aerial image classification. The optimization of the model is achieved using post-training quantization techniques. To address the limited number of disaster cases in existing benchmark datasets and therefore ensure real-world adoption of our model, we construct a novel dataset, DisasterEye, featuring disaster scenes captured by UAVs and individuals on-site. Experimental results reveal the efficacy of our model, reaching high accuracy with lowered inference latency and memory use on both traditional machines and resource-limited devices. This shows that the scalability and adaptability of our method make it a powerful solution for real-time disaster management on resource-constrained UAV platforms. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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SIR-HCL: Semantic-Inconsistency Reasoning and Hybrid Contrastive learning for Efficient Cross-Emotion Anomaly Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Liu, Xin Chen, Qiyan Cheung, Yiu-Ming Peng, Shu-Juan Huaqiao University Department of Computer Science Xiamen361021 China Hong Kong Baptist University Department of Computer Science SAR Hong Kong Hong Kong Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen361021 China Huaqiao University Department of Artificial Intelligence Xiamen China Fujian Province University Key Laboratory of Computer Vision and Machine Learning Huaqiao University Xiamen361021 China

Cross-emotion anomaly detection is an emerging and challenging research topic in cognitive analysis field, which aims at identifying the abnormal emotion pair whose semantic patterns are inconsistent across different emotional modalities. To the best of our knowledge, this topic has yet to be well studied, which could potentially benefit lots of valuable cognitive applications such as autistic children diagnosis and criminal deception detection. To this end, this paper proposes an efficient cross-emotion anomaly detection approach via semanticinconsistency reasoning and hybrid contrastive learning (SIR-HCL), which is the first attempt to detect the anomalous emotional pairs across the audio-visual emotions. First, the proposed framework utilizes dual-branch network to obtain the deep emotional features in each modality, and then employs the shared residual block to derive the semantically compatible features. Subsequently, an efficient hybrid contrastive learning approach is designed to enlarge the semantic-inconsistency among abnormal emotional pair with different affective classes, while enhancing the semantic-consistency and increasing the feature correlation between normal emotional pair from the same affective class. At the same time, an efficient bidirectional learning scheme is employed to significantly improve the data utilization and a two-component Beta Mixture Model is adaptively utilized to reason the anomalous emotion pairs. Extensive experiments evaluated on two benchmark datasets show that the proposed SIR-HCL method can well detect the anomalous emotional pairs across audio-visual emotional data, and brings substantial improvements over the state-of-the-art competing methods. © 2016 IEEE.

关键词： Contrastive learning

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FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysis

arXiv

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arXiv 2025年

作者： Maani, Fadillah Saeed, Numan Saleem, Tausifa Farooq, Zaid Alasmawi, Hussain Diehl, Werner Mohammad, Ameera Waring, Gareth Valappi, Saudabi Bricker, Leanne Yaqub, Mohammad Department of Computer Vision Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Abu Dhabi United Arab Emirates

Foundation models are becoming increasingly effective in the medical domain, offering pre-trained models on large datasets that can be readily adapted for downstream tasks. Despite progress, fetal ultrasound images remain a challenging domain for foundation models due to their inherent complexity, often requiring substantial additional training and facing limitations due to the scarcity of paired multimodal data. To overcome these challenges, here we introduce FetalCLIP, a vision-language foundation model capable of generating universal representation of fetal ultrasound images. FetalCLIP was pre-trained using a multimodal learning approach on a diverse dataset of 210,035 fetal ultrasound images paired with text. This represents the largest paired dataset of its kind used for foundation model development to date. This unique training approach allows FetalCLIP to effectively learn the intricate anatomical features present in fetal ultrasound images, resulting in robust representations that can be used for a variety of downstream applications. In extensive benchmarking across a range of key fetal ultrasound applications, including classification, gestational age estimation, congenital heart defect (CHD) detection, and fetal structure segmentation, FetalCLIP outperformed all baselines while demonstrating remarkable generalizability and strong performance even with limited labeled data. We plan to release the FetalCLIP model publicly for the benefit of the broader scientific community © 2025, CC BY-NC-ND.

关键词： Ultrasonic imaging

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Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis

arXiv

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arXiv 2025年

作者： Saeed, Numan Hardan, Shahad Ridzuan, Muhammad Saadi, Nada Nandakumar, Karthik Yaqub, Mohammad Department of Computer Vision Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Michigan State University Michigan United States M42 Health Abu Dhabi United Arab Emirates

Cancer detection and prognosis relies heavily on medical imaging, particularly CT and PET scans. Deep Neural Networks (DNNs) have shown promise in tumor segmentation by fusing information from these modalities. However, a critical bottleneck exists: the dependency on CT-PET data concurrently for training and inference, posing a challenge due to the limited availability of PET scans. Hence, there is a clear need for a flexible and efficient framework that can be trained with the widely available CT scans and can be still adapted for PET scans when they become available. In this work, we propose a parameter-efficient multi-modal adaptation (PEMMA) framework for lightweight upgrading of a transformer-based segmentation model trained only on CT scans such that it can be efficiently adapted for use with PET scans when they become available. This framework is further extended to perform prognosis task maintaining the same efficient cross-modal fine-tuning approach. The proposed approach is tested with two well-known segementation backbones, namely UNETR and Swin UNETR. Our approach offers two main advantages. Firstly, we leverage the inherent modularity of the transformer architecture and perform low-rank adaptation (LoRA) as well as decomposed low-rank adaptation (DoRA) of the attention weights to achieve parameter-efficient adaptation. Secondly, by minimizing cross-modal entanglement, PEMMA allows updates using only one modality without causing catastrophic forgetting in the other. Our method achieves comparable performance to early fusion, but with only 8% of the trainable parameters, and demonstrates a significant +28% Dice score improvement on PET scans when trained with a single modality. Furthermore, in prognosis, our method improves the concordance index by +10% when adapting a CT-pretrained model to include PET scans, and by +23% when adapting for both PET and EHR data. © 2025, CC BY-NC-SA.

关键词： Image segmentation

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Enhancing the Prediction of Lymph Node Metastasis in Early Breast Cancer Using Deep learning on Routine Full-Breast Mammograms

Research Square

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Research Square 2025年

作者： Zhang, Daqu Dihge, Looket Bendahl, Pär-Ola Arvidsson, Ida Dustler, Magnus Ellbrant, Julia Gulis, Kim Hjärtström, Malin Ohlsson, Mattias Rejmer, Cornelia Schmidt, David Zackrisson, Sophia Edén, Patrik Rydén, Lisa Centre for Environmental and Climate Science Computational Science for Health and Environment Lund University Lund Sweden Department of Clinical Sciences Division of Surgery Lund University Skåne University Hospital Lund Sweden Department of Plastic and Reconstructive Surgery Skåne University Hospital Malmö Sweden Department of Clinical Sciences Division of Oncology Lund University Lund Sweden Centre for Mathematical Sciences Division of Computer Vision and Machine Learning Lund University Lund Sweden Department of Translational Medicine Diagnostic Radiology Lund University Malmö Lund Sweden Department of Surgery and Gastroenterology Skåne University Hospital Malmö Sweden Department of Surgery Kristianstad Central Hospital Kristianstad Sweden Department of Clinical Sciences Anesthesiology and Intensive Care Lund University Lund Sweden Department of Medical Imaging and Physiology Skåne University Hospital Malmö Sweden

Background: With a trend toward de-escalation of axillary surgery in breast cancer, prediction models incorporating imaging modalities can help reassess the need for surgical axillary staging. Although mammography is routinely performed for breast cancer imaging, its potential in nodal staging remains underutilized. This study aims to employ advancements in deep learning (DL) to comprehensively evaluate the potential of routine mammograms for predicting lymph node metastasis (LNM) in preoperative clinical settings. Methods: This retrospective study included 1,265 cN0 T1-T2 breast cancer patients, comprising 368 node-positive and 897 node-negative cases, diagnosed from 2009-2017 at three Swedish institutions. Patients diagnosed in 2017 were assigned to the independent test set (n=123, site 2) and the external test set (n=103, site 3), while the remaining patients (n=1,039, site 1 and 2) were used for model development and double cross-validation. A neck module, in conjunction with a ResNet backbone pretrained on unlabeled mammograms, was developed to extract global information from full-breast or region-of-interest (ROI) mammograms by predicting five cancer outcomes. Clinicopathological characteristics were combined with the learned mammogram features to predict LNM collaboratively. The models were evaluated using area under the receiver operating characteristic (ROC) curve (AUC), calibration, and decision curve analysis. Results: Compared to models using only clinical variables, incorporating full-breast mammograms with preoperative clinical variables improved the ROC AUC from 0.690 ± 0.063 (SD) to 0.774 ± 0.057 in the independent test set and from 0.584 ± 0.068 to 0.637 ± 0.063 in the external test set. The combined model showed good calibration and, at sensitivity ≥ 90%, achieved a better net benefit, and a higher sentinel lymph node biopsy reduction rate of 41.7% in the independent test set. Full-breast mammograms showed comparable ability to tumor ROIs in predic

关键词： Self-supervised learning

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