A scoping review of transfer learning research on medical image analysis using ImageNet

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562

PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation

Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., et al. (2018) · Annals of Internal Medicine · doi:10.7326/m18-0850 · Cited by 43,565

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380

Scoping studies: towards a methodological framework

Arksey, H., & O'Malley, L. (2005) · International Journal of Social Research Methodology · doi:10.1080/1364557032000119616 · Cited by 36,742

Untitled work

(2014) · Cited by 33,419

Breast Cancer Detection in the Equivocal Mammograms by AMAN Method

Ibrahim, N. M., Ali, B., Jawad, F. A., Qanbar, M. A., Aleisa, R. I., Alhmmad, S. A., Alhindi, K. R., Altassan, M., Almuhanna, A., Algofari, H. M., et al. (2023) · Applied Sciences · doi:10.3390/app13127183 · Cited by 11

BarlowTwins-CXR: enhancing chest X-ray abnormality localization in heterogeneous data with cross-domain self-supervised learning

Sheng, H., Ma, L., Samson, J.-F., & Liu, D. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02529-9 · Cited by 14

On the Generalization for Transfer Learning: An Information-Theoretic Analysis

Wu, X., Manton, J. H., Aickelin, U., & Zhu, J. (2024) · IEEE Transactions on Information Theory · doi:10.1109/tit.2024.3441574 · Cited by 18

Skin Lesions Classification Using Deep Learning Techniques: Review

Kareem, O. S., Abdulazeez, A. M., & Zeebaree, D. Q. (2021) · Asian Journal of Research in Computer Science · doi:10.9734/ajrcos/2021/v9i130210 · Cited by 12

Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries

Gao, W., Wang, D., & Huang, Y. (2023) · Cancer Informatics · doi:10.1177/11769351231214446 · Cited by 13

Data pyramid structure for optimizing EUS-based GISTs diagnosis in multi-center analysis with missing label

Fan, L., Gong, X., Zheng, C., & Li, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107897 · Cited by 11

Benchmarking Supervised and Self-Supervised Learning Methods in a Large Ultrasound Muti-Task Images Dataset

Liu, P., Zhang, J., Wu, X., Liu, S., Wang, Y., Feng, L., Diao, Y., Liu, Z., Lyu, G., & Chen, Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3382604 · Cited by 10

Deep learning with adaptive convolutions for classification of retinal diseases via optical coherence tomography

Karthik, K., & Mahadevappa, M. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105044 · Cited by 11

Development of Hybrid Models Based on AlexNet and Machine Learning Approaches for Strip Steel Surface Defect Classification

Boudiaf, A., Benlahmidi, S., Dahane, A., & Bouguettaya, A. (2024) · Journal of Failure Analysis and Prevention · doi:10.1007/s11668-024-01927-5 · Cited by 10

Cloud-based optimized deep learning framework for automated glaucoma detection using stationary wavelet transform and improved grey-wolf-optimization with ELM approach

Muduli, D., Yaqoob, S. I., Sharma, S. K., Kanade, A. S., Shameem, M., Jangwan, H. S., Kumar, P. M. A., & Zamani, A. T. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.104682 · Cited by 10

High-accuracy detection of supraspinatus fatty infiltration in shoulder MRI using convolutional neural network algorithms

Saavedra, J. P., Droppelmann, G., García, N., Jorquera, C., & Feijoo, F. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1070499 · Cited by 10

Predicting Brain Amyloid Positivity from T1 weighted brain MRI and MRI-derived Gray Matter, White Matter and CSF maps using Transfer Learning on 3D CNNs*Preprint

Chattopadhyay, T., Ozarkar, S. S., Buwa, K., Thomopoulos, S. I., Thompson, P. M., & Initiative, T. A. D. N. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.15.528705 · Cited by 11

Feature extraction of particle morphologies of pharmaceutical excipients from scanning electron microscope images using convolutional neural networks

Iwata, H., Hayashi, Y., Koyama, T., Hasegawa, A., Ohgi, K., Kobayashi, I., & Okuno, Y. (2024) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2024.123873 · Cited by 9

Advancing maxillofacial prosthodontics by using pre‐trained convolutional neural networks: Image‐based classification of the maxilla

Ali, I. E., Sumita, Y. I., & Wakabayashi, N. (2024) · Journal of Prosthodontics · doi:10.1111/jopr.13853 · Cited by 9

Sex estimation from maxillofacial radiographs using a deep learning approach

Hiroki, H. A. S. E., Mine, Y., Okazaki, S., Yoshimi, Y., Ito, S., Peng, T., Mizuho, S. A. N. O., KOIZUMI, Y., Kakimoto, N., Tanimoto, K., et al. (2024) · Dental Materials Journal · doi:10.4012/dmj.2023-253 · Cited by 10

Image Quality Assessment Using Convolutional Neural Network in Clinical Skin Images

Jeong, H. K., Park, C., Jiang, S. W., Nicholas, M. W., Chen, S. C., Henao, R., & Kheterpal, M. (2024) · JID Innovations · doi:10.1016/j.xjidi.2024.100285 · Cited by 9

Convolutional Neural Network to Classify Infrared Thermal Images of Fractured Wrists in Pediatrics

Shobayo, O., Saatchi, R., & Ramlakhan, S. L. (2024) · Healthcare · doi:10.3390/healthcare12100994 · Cited by 11

MoMA: Momentum contrastive learning with multi-head attention-based knowledge distillation for histopathology image analysis

Vuong, T. T. L., & Kwak, J. T. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103421 · Cited by 13

Artificial intelligence in pediatric osteopenia diagnosis: evaluating deep network classification and model interpretability using wrist X-rays

Harris, C., Liu, L., Almeida, L., Kassick, C., & Makrogiannis, S. K. (2025) · Bone Reports · doi:10.1016/j.bonr.2025.101845 · Cited by 9

Generalizability of electrocardiographic artificial intelligence

Karabayir, İ., & Akbilgiç, O. (2025) · npj Cardiovascular Health · doi:10.1038/s44325-025-00078-2 · Cited by 10

Evaluation of machine learning and deep learning models for the classification of a single extracellular vesicles spectral library

Mata, C. D. R., Lu, Y. P., Jalali, M., Bocan, A., Khatami, M., Montermini, L., McCormack-Ilersich, J., Reisner, W., Garzia, L., Rak, J. W., et al. (2025) · Sensors & Diagnostics · doi:10.1039/d5sd00091b · Cited by 9

Microfluidics in biomanufacturing process development

Moreno‐Sibaja, F., Zou, D., Liu, S., Zhao, C., & Gerstweiler, L. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108715 · Cited by 9

Class dependency-based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of tuberculosis from chest X-rays

Chowdary, G. J., Suganya, G., Premalatha, M., & Karunamurthy, K. (2022) · doi:10.1049/pbhe040e_ch3 · Cited by 9

Boosting Advanced Nasopharyngeal Carcinoma Stage Prediction Using a Two-Stage Classification Framework Based on Deep Learning

Huang, J., He, R., Chen, J., Li, S., Deng, Y., & Wu, X. (2021) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-021-00026-9 · Cited by 9

Transfer learning for fluence map prediction in adrenal stereotactic body radiation therapy

Wang, W., Sheng, Y., Palta, M., Czito, B. G., Willett, C. G., Yin, F.-F., Wu, Q., Ge, Y., & Wu, J. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac3c14 · Cited by 10

Enhancing motor imagery decoding via transfer learning

George, O., Dabas, S., Sikder, A., Smith, R. O., Madiraju, P., Yahyasoltani, N., & Ahamed, S. I. (2022) · Smart Health · doi:10.1016/j.smhl.2022.100339 · Cited by 8

Towards Transfer Learning for Large-Scale Image Classification Using Annealing-Based Quantum Boltzmann Machines

Schuman, D., Sünkel, L., Altmann, P. J., Stein, J., Roch, C., Gabor, T., & Linnhoff–Popien, C. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.10182 · Cited by 12

ImSpect: Image-driven self-supervised learning for surgical margin evaluation with mass spectrometry

Connolly, L., Fooladgar, F., Jamzad, A., Kaufmann, M., Syeda, A., Ren, K. Y. M., Abolmaesumi, P., Rudan, J. F., McKay, D., Gabor Fichtinger, A., et al. (2024) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-024-03106-1 · Cited by 9

Augmenting Radiological Diagnostics with AI for Tuberculosis and COVID-19 Disease Detection: Deep Learning Detection of Chest Radiographs

Kolhar, M., Rajeh, A. M. A., & Kazi, R. N. A. (2024) · Diagnostics · doi:10.3390/diagnostics14131334 · Cited by 8

Transfer Learning Enabled Imagined Speech Interpretation Using Phase-Based Brain Functional Connectivity and Power Analysis

Bisla, M., & Anand, R. S. (2024) · IEEE Access · doi:10.1109/access.2024.3438873 · Cited by 10

DADFN: dynamic adaptive deep fusion network based on imaging genomics for prediction recurrence of lung cancer

Jia, L., Wu, W., Hou, G., Zhang, Y., Zhao, J., Qiang, Y., & Wang, L. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acc168 · Cited by 8

Semi-supervised model based on implicit neural representation and mutual learning (SIMN) for multi-center nasopharyngeal carcinoma segmentation on MRI

Han, X., Chen, Z., Lin, G., Lv, W., Zheng, C., Lu, W., Sun, Y. H., & Lu, L. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108368 · Cited by 11

Random Forest Based Deep Hybrid Architecture for Histopathological Breast Cancer Images Classification

Nakach, F.-Z., Zerouaoui, H., & Idri, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-10450-3_1 · Cited by 8

Multi-perspective region-based CNNs for vertebrae labeling in intraoperative long-length images

Huang, Y., Jones, C. K., Zhang, X., Johnston, A., Waktola, S., Aygün, N., Witham, T. F., Bydon, A., Theodore, N., Helm, P. A., et al. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107222 · Cited by 8

Gastro-BaseNet: A Specialized Pre-Trained Model for Enhanced Gastroscopic Data Classification and Diagnosis of Gastric Cancer and Ulcer

Lee, G. P., Kim, Y. J., Park, D. J., Kim, Y. H., Han, S., & Kim, K. G. (2023) · Diagnostics · doi:10.3390/diagnostics14010075 · Cited by 7

An overview of computer—aided medical image classification

Boafo, Y. G. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19558-1 · Cited by 7

Transfer learning in spirometry: CNN models for automated flow-volume curve quality control in paediatric populations

Martins, C., Barros, H., & Moreira, A. M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109341 · Cited by 8

Update of machine learning for ultrasound diagnosis of metabolic dysfunction-associated steatotic liver disease: a bright future for deep learning

Li, J., Chen, J., Zeng, X., Lyu, G., Shu, L., & He, S. (2025) · PeerJ · doi:10.7717/peerj.19645 · Cited by 7

Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning

Dehkordi, A. A., Neshat, M., Khosravian, A., Thilakaratne, M., Sadiq, A. S., & Mirjalili, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-02890-3 · Cited by 8

Optimized CNN framework for malaria detection using Otsu thresholding−based image segmentation

Singh, R., Prabha, C., & Abdulla, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-23961-5 · Cited by 7

Enhancing the Accuracy of an Image Classification Model Using Cross-Modality Transfer Learning

Liu, J., Chui, K. T., & Lee, L. (2023) · Electronics · doi:10.3390/electronics12153316 · Cited by 7

DETECTION OF CHRONIC VENOUS INSUFFICIENCY CONDITION USING TRANSFER LEARNING WITH CONVOLUTIONAL NEURAL NETWORKS BASED ON THERMAL IMAGES

Krishnan, N., & Muthu, P. (2023) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237223500308 · Cited by 7

Risk prediction of pulse wave for hypertensive target organ damage based on frequency-domain feature map

Yang, J., Lü, J., Qiu, Z., Zhang, M., & Yan, H. (2024) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2024.104161 · Cited by 7

Multi-task neural networks by learned contextual inputs

Sandnes, A. T., Grimstad, B., & Kolbjørnsen, O. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106528 · Cited by 9

Breast cancer classification based on breast tissue structures using the Jigsaw puzzle task in self-supervised learning

Sugawara, K., Takaya, E., Inamori, R., Konaka, Y., Sato, J., Shiratori, Y., Hario, F., Kobayashi, T., Ueda, T., & Okamoto, Y. (2025) · Radiological Physics and Technology · doi:10.1007/s12194-024-00874-y · Cited by 8

Global Trends in the Use of Artificial Intelligence in Dental Education: A Bibliometric Analysis

Iniesta, M., & Pérez‐Higueras, J. J. (2025) · European Journal Of Dental Education · doi:10.1111/eje.13133 · Cited by 8

A Fair Performance Comparison between Complex-Valued and Real-Valued Neural Networks for Disease Detection

Jojoa, M., García-Zapirain, B., & Percybrooks, W. (2022) · Diagnostics · doi:10.3390/diagnostics12081893 · Cited by 7

Cardiac MRI segmentation of the atria based on UU-NET

Wang, Y., Li, S., Huang, J., Lai, Q.-Q., Guo, Y., Huang, Y.-H., & Li, Y. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.1011916 · Cited by 7

Robust Yet Simple Deep Learning-Based Ensemble Approach for Assessing Diabetic Retinopathy in Fundus Images

Khalid, S., Abdulwahab, S., Rashwan, H. A., Abdel‐Nasser, M., Sharaf, N., & Puig, D. (2022) · International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT) · doi:10.1109/impact55510.2022.10029219 · Cited by 7

A deep convolutional neural network model for medical data classification from computed tomography images

Sreelakshmi, S., & Anoop, V. S. (2023) · Expert Systems · doi:10.1111/exsy.13427 · Cited by 7

3D Attention U-Net with Pretraining: A Solution to CADA-Aneurysm Segmentation Challenge

Su, Z., Jia, Y., Liao, W., Lv, Y., Dou, J., Sun, Z., & Li, X. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72862-5_6 · Cited by 7

Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoringPreprint

Horry, M. J., Chakraborty, S., Pradhan, B., Fallahpoor, M., Chegeni, H., & Paul, M. (2021) · doi:10.31224/osf.io/tfqa4 · Cited by 7

Kidney Disease Classification and Diagnosis: A Comprehensive Review of Current AI Techniques

Esmail, E. E. A., Mohammed, A. A. A., Mohammed, R. G., Almuhaya, B., Fadhl, A., & Bazel, M. A. (2024) · International Conference on Emerging Smart Technologies and Applications (eSmarTA) · doi:10.1109/esmarta62850.2024.10638841 · Cited by 6

Enhancing Weed Detection Performance by Means of GenAI-Based Image Augmentation

Modak, S., & Stein, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91835-3_17 · Cited by 12

Deep learning for autism detection using clinical notes: A comparison of transfer learning for a transparent and black-box approach

Gondy Leroy, A., Bisht, P., Kandula, S. M., Maltman, N., & Rice, S. A. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103318 · Cited by 6

Hybrid morphological-convolutional neural networks for computer-aided diagnosis

Canales-Fiscal, M. R., & Tamez‐Peña, J. G. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1253183 · Cited by 7

A cost focused framework for optimizing collection and annotation of ultrasound datasets

Lawley, A., Hampson, R., Worrall, K., & Dobie, G. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106048 · Cited by 8

Multimodal Deep Learning Fusing Clinical and Radiomics Scores for Prediction of Early-Stage Lung Adenocarcinoma Lymph Node Metastasis

Xia, C., Zuo, M., Lin, Z., Deng, L., Rao, Y., Chen, W., Chen, J., Yao, W., & Hu, M. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.12.018 · Cited by 6

Investigation of Pneumonia Detection using Convolutional Neural Networks

Cicenas, B., & Abromavičius, V. (2022) · IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream) · doi:10.1109/estream56157.2022.9781683 · Cited by 6

A Transferable Learning Classification Model and Carbon Sequestration Estimation of Crops in Farmland Ecosystem

Wang, L., Bai, Y., Wang, J., Qin, F., Liu, C., Zhou, Z. Y., & Jiao, X. (2022) · Remote Sensing · doi:10.3390/rs14205216 · Cited by 6

Comparative analysis of popular predictors for difficult laryngoscopy using hybrid intelligent detection methods

Liu, X., Flanagan, C., Fang, J., Lei, Y., McGrath, L., Wang, J., Guo, X., Guo, J., McGrath, H., & Han, Y. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e11761 · Cited by 6

Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models

KİRAZ, A. H., Djibrillah, F. O., & Yüksel, M. E. (2023) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.55730/1300-0632.4037 · Cited by 7

Automating CT Diagnostic Image Quality Control Using Transfer Learning

Gupta, A., Shehata, M., Braun, W. J., Feldman, R. E., & Bjarnason, T. A. (2023) · International Conference on Modeling, Simulation & Intelligent Computing (MoSICom) · doi:10.1109/mosicom59118.2023.10458738 · Cited by 6

Leveraging Deep Learning for Automated Analysis of Colorectal Cancer Histology Images to Elevate Diagnosis Precision

Uddin, S. M. I., Mozumder, M. A. I., Sumon, R. I., Mon–il, J., & Kim, H. (2024) · International Conference on Advanced Communications Technology (ICACT) · doi:10.23919/icact60172.2024.10471943 · Cited by 6

Osteosarcoma Tumor Detection using Different Transfer Learning Models

Meem, R. F., & Hasan, K. T. (2019) · Clinical Imaging and Case Reports · doi:10.52338/cicasrep.2024.3962 · Cited by 8

Simply Fine-Tuned Deep Learning-Based Classification for Breast Cancer with Mammograms

Mudeng, V., Jeong, J.-W., & Choe, S. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.031046 · Cited by 6

Factors determining generalization in deep learning models for scoring COVID-CT images

Horry, M. J., Chakraborty, S., Pradhan, B., Fallahpoor, M., Chegeni, H., & Paul, M. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2021456 · Cited by 13

Class imbalance-aware domain specific transfer learning approach for medical image classification: Application on COVID-19 detection

Jindal, M., & Singh, B. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110583 · Cited by 6

Weakly Supervised Deep Learning for Monitoring Sleep Apnea Severity Using Coarse-Grained Labels

Zan, X., Wang, D., Song, C., Liu, F., Xian, X., & Berry, R. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3566682 · Cited by 5

Deep learning approaches for pathological image classification

Tsuneki, M. (2025) · Journal of Oral Biosciences · doi:10.1016/j.job.2025.100696 · Cited by 5

Large scale models in radiology: revolutionizing the future of medical imaging

Li, Y., Liu, J., Zhang, Y., Wei, J., Yang, L., Yang, X., Shi, Y., Zhao, L., Yang, W., Li, H., et al. (2024) · Radiology Science · doi:10.15212/radsci-2023-0018 · Cited by 6

Distribution-Aware Loss for Lesions Detection Using White-Light Endoscopy in Colorectal Region

Sun, W., Zhao, R., Zhang, K., Gao, J., & Qu, G. (2024) · IEEE Access · doi:10.1109/access.2024.3381614 · Cited by 5

Img2Side: A Transfer Learning Based Model for Predicting Drug Side Effects Using 2D Chemical Structural Images

Arshed, M. A., Ibrahim, M., Mumtaz, S., Ali, T., & Choi, G. S. (2024) · IEEE Access · doi:10.1109/access.2024.3382936 · Cited by 5

CMT-CNN: colposcopic multimodal temporal hybrid deep learning model to detect cervical intraepithelial neoplasia

Mukku, L., & Thomas, J. (2024) · International Journal of Advances in Intelligent Informatics · doi:10.26555/ijain.v10i2.1527 · Cited by 5

Segmentation and Classification of Breast Masses From the Whole Mammography Images Using Transfer Learning and BI‐RADS Characteristics

Oudjer, H., Cherfa, A. B., Cherfa, Y., & Belkhamsa, N. (2024) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.23216 · Cited by 5

Research on void identification of concrete filled steel tube under data imbalance and constraint condition change

Xie, K., Qin, Y., Luo, X., Liang, D., Luo, Z., Li, H., Long, G., & Fu, T. (2025) · Structures · doi:10.1016/j.istruc.2025.108245 · Cited by 5

A Novel Cross-Validation Fusion Model Combining Vision Transformer and DenseNet161 for Enhanced Cervical Lesion Classification

Mukku, L., & Thomas, J. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-2697-7_32 · Cited by 5

Transfer Learning based Gender Identification using Arbitrary Celebrity Image Sets

Mercado, E. A. B., Das, S. R., Hassan, B., & Patel, P. (2025) · International Conference on Pervasive Computing and Social Networking (ICPCSN) · doi:10.1109/icpcsn65854.2025.11035995 · Cited by 5

Emerging modalities for neuroprognostication in neonatal encephalopathy: harnessing the potential of artificial intelligence

Chawla, V., Çizmeci, M. N., Sullivan, K. M., Gritz, E. C., Cardona, V. Q., Menkiti, O. R., Natarajan, G., Rao, R., McAdams, R. M., & Dizon, M. L. V. (2025) · Pediatric Research · doi:10.1038/s41390-025-04336-y · Cited by 5

Autonomy in robotic prostate biopsy through AI-assisted fusion

Palladino, L., Maris, B., Antonelli, A., & Fiorini, P. (2021) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar53236.2021.9659396 · Cited by 5

Classifying Malignancy in Prostate Glandular Structures from Biopsy Scans with Deep Learning

Fogarty, R., Goldgof, D. B., Hall, L., Lopez, A. S., Johnson, J., Gadara, M., Stoyanova, R. S., Punnen, S. P., Pollack, A. S., Pow‐Sang, J. M., et al. (2023) · Cancers · doi:10.3390/cancers15082335 · Cited by 6

Hyperspectral imaging: a new method for diagnosing benign and malignant lung cancer

Tian, C., meng, X., zhenlei, Z., zhu, H., An, H., Li, W., & Yuan, S. (2023) · International Conference on Optics and Image Processing (ICOIP) · doi:10.1117/12.2689178 · Cited by 5

A deep learning model fusion algorithm for the diagnosis of gastric Mucosa-associated lymphoid tissue lymphoma

Quan, J., Ye, J., Lan, J., Wang, J., Hu, Z., Guo, Z., Wang, T., Han, Z., Wu, Z., Tan, T., et al. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106064 · Cited by 7

Enhancing Human Activity Recognition in Wrist-Worn Sensor Data Through Compensation Strategies for Sensor Displacement

Wang, H., Wang, X., Lu, C., Yuan, M., Wang, Y., Yu, H., & Li, H. (2024) · IEEE Access · doi:10.1109/access.2024.3422256 · Cited by 7

Application of radiomics for diagnosis, subtyping, and prognostication of medulloblastomas: a systematic review

Fotouhi, M., Shahbandi, A., Mehr, F. S. K., Shahla, M. M., Nouredini, S. M., Kankam, S. B., Khorasanizadeh, M., & Chambless, L. B. (2024) · Neurosurgical Review · doi:10.1007/s10143-024-03060-1 · Cited by 4

Effective data selection via deep learning processes and corresponding learning strategies in ultrasound image classification

Lee, H., Kwak, J. Y., & Lee, E. (2025) · Scientific Reports · doi:10.1038/s41598-025-00416-5 · Cited by 4

Effects of network selection and acoustic environment on bounding-box object detection of delphinid whistles using a deep learning tool

Sugarman, P. C., Ferguson, E. L., Alongi, G. C., Schallert, J. P., & Lyn, H. (2025) · The Journal of the Acoustical Society of America · doi:10.1121/10.0036942 · Cited by 5

Hyperbolic Space Feature Enhancement and Interaction Mechanism for Classification on Small Multimodal Medical Datasets

de Lima, L. M., & Krohling, R. A. (2025) · Expert Systems · doi:10.1111/exsy.70130 · Cited by 4

Artificial intelligence-driven advances in automatic segmentation of meningioma brain tumors: A systematic review

Sadeghzadeh, N., Correia, J. A., Holdsworth, S. J., Nielsen, P. M. F., & Abbasi, H. (2026) · Brain Network Disorders · doi:10.1016/j.bnd.2026.01.001 · Cited by 4

Specular Reflection Removal Techniques in Cervix Image: A Comprehensive Review

Mukku, L., & Thomas, J. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-7633-1_36 · Cited by 4

Artificial Intelligence and Machine Learning Based Human Age Detection from Face Image

Garg, A., Venkat, S., Shanmugaraj, M., Aggarwal, D., Karthikeyan, T., & Chamundeeswari, V. (2023) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses60034.2023.10465295 · Cited by 4

Deep-Learning-based Automated Identification of Ventriculoperitoneal-Shunt Valve Models from Skull X-rays

Vach, M., Weiß, D., Ivan, V. L., Boschenriedter, C., Wolf, L., Beez, T., Hofmann, B. B., Rubbert, C., & Caspers, J. (2025) · Clinical Neuroradiology · doi:10.1007/s00062-024-01490-4 · Cited by 4

Development of Artificial Intelligence-based Real-time Automatic Fusion of Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasonography of the Prostate

Cianflone, F., Maris, B., Bertolo, R. G., Veccia, A., Artoni, F., Pettenuzzo, G., Montanaro, F., Porcaro, A. B., Bianchi, A., Malandra, S., et al. (2025) · Urology · doi:10.1016/j.urology.2025.03.004 · Cited by 4

Application of AI-Driven Software Diagnocat in Managing Diagnostic Imaging in Dentistry: A Retrospective Study

Mema, H., Gaxhja, E., Alicka, Y., Gugu, M., Topi, S., Giannoni, M., Pietropaoli, D., & Altamura, S. (2025) · Applied Sciences · doi:10.3390/app15179790 · Cited by 4

Deep learning-based vessel and nerve recognition model for lateral lymph node dissection: a retrospective feasibility study

Sasaki, S., Kitaguchi, D., Noda, T., Matsuzaki, H., Hasegawa, H., Takeshita, N., & Ito, M. (2025) · Langenbeck s Archives of Surgery · doi:10.1007/s00423-025-03882-7 · Cited by 4

Development of a prediction model for head and neck volume reduction by clinical factors, dose–volume histogram parameters and radiomics in head and neck cancer

Ishizawa, M., Tanaka, S., Takagi, H., Kadoya, N., Sato, K., Umezawa, R., Jingu, K., & Takeda, K. (2023) · Journal of Radiation Research · doi:10.1093/jrr/rrad052 · Cited by 4

Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans

Leong, L. T., Wong, M. C., Liu, Y. E., Glaser, Y., Quon, B. K., Kelly, N. N., Cataldi, D., Sadowski, P., Heymsfield, S. B., & Shepherd, J. (2024) · Communications Medicine · doi:10.1038/s43856-024-00434-w · Cited by 4

Machine learning in neutron scattering data analysis

Wang, H., Du, R., Liu, Z., & Zhang, J. (2024) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2024.100870 · Cited by 4

Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI

Bottani, S., Thibeau–Sutre, E., Maire, A., Ströer, S., Dormont, D., Colliot, O., Burgos, N., & APPRIMAGE Study Group (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01242-3 · Cited by 4

Transfer learning for medical image classification: a literature review

Kim, H. E., Cosa‐Linan, A., Santhanam, N., Jannesari, M., Maros, M. E., & Ganslandt, T. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00793-7 · Cited by 1,117

Transfer learning techniques for medical image analysis: A review

Kora, P., Ooi, C. P., Faust, O., Raghavendra, U., Gudigar, A., Chan, W. Y., Meenakshi, K., Swaraja, K., Pławiak, P., & Acharya, U. R. (2021) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2021.11.004 · Cited by 391

A review of transfer learning for medical image classificationPreprint

Hee, K. E., Cosa‐Linan, A., Maros, M. E., Santhanam, N., Jannesari, M., & Ganslandt, T. (2021) · Research Square · doi:10.21203/rs.3.rs-844222/v1 · Cited by 6

A Review of Deep Learning on Medical Image Analysis

Wang, J., Zhu, H., Wang‎, S., & Zhang, Y. (2020) · Mobile Networks and Applications · doi:10.1007/s11036-020-01672-7 · Cited by 318

A Review of Transfer Learning for Medical Image Analysis

Hee, K. E., Alejandro, A., Maros, M. E., Santhanam, N., Jannesari, M., & Ganslandt, T. (2021)