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

Deep Residual Learning for Image Recognition

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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ImageNet classification with deep convolutional neural networks

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ImageNet: A large-scale hierarchical image database

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Densely Connected Convolutional Networks

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Going deeper with convolutions

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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

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Scoping studies: towards a methodological framework

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Untitled work

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Quantum Machine Learning in Prediction of Breast Cancer

Prajapati, J. B., Paliwal, H., Prajapati, B. G., Saikia, S., & Pandey, R. (2023) · Studies in computational intelligence · doi:10.1007/978-981-19-9530-9_19 · Cited by 32

Generalized Morse Wavelets parameter selection and transfer learning for pavement transverse cracking detection

Martinez-Ríos, E. A., Bustamante-Bello, R., & Navarro-Tuch, S. A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106355 · Cited by 35

RANet: a custom CNN model and quanvolutional neural network for the automated detection of rheumatoid arthritis in hand thermal images

Ahalya, R. K., Almutairi, F. M., Umapathy, S., Dhanraj, V., & Aslam, S. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-42111-3 · Cited by 29

Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models

Khan, N. C., Perera, C. M., Dow, E. R., Chen, K., Mahajan, V. B., Mruthyunjaya, P., Diana, V., Leng, T., & Myung, D. J. (2022) · Diagnostics · doi:10.3390/diagnostics12071714 · Cited by 33

COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers

Costa, Y. M. G., Silva, S. A., Teixeira, L. O., Pereira, R. M., Bertolini, D. S., Britto, A. S., Oliveira, L. S., & Cavalcanti, G. D. C. (2022) · Sensors · doi:10.3390/s22197303 · Cited by 29

Artificial Intelligence for Radiation Oncology Applications Using Public Datasets

Wahid, K. A., Glerean, E., Sahlsten, J., Jaskari, J., Kaski, K. K., Naser, M. A., He, R., Mohamed, A., & Fuller, C. D. (2022) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2022.06.009 · Cited by 28

Deep learning-based high-accuracy detection for lumbar and cervical degenerative disease on T2-weighted MR images

Yi, W., Zhao, J., Tang, W., Yin, H., Yu, L., Wang, Y., & Tian, W. Q. (2023) · European Spine Journal · doi:10.1007/s00586-023-07641-4 · Cited by 27

Suitability of GPT-4o as an evaluator of cardiopulmonary resuscitation skills examinations

Wang, L., Mao, Y., Wang, L., Sun, Y., Song, J., & Zhang, Y. (2024) · Resuscitation · doi:10.1016/j.resuscitation.2024.110404 · Cited by 29

Transferability and interpretability of the sepsis prediction models in the intensive care unit

Chen, Q., Li, R., Lin, C., Lai, C., Chen, D., Qu, H., Huang, Y., Lu, W., Tang, Y., & Li, L. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-02090-3 · Cited by 28

Dynamic time warping approach for optimized locomotor impairment detection using biomedical signal processing

Adhikary, S., & Ghosh, A. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103321 · Cited by 26

Unleashing deep neural network full potential for solar radiation forecasting in a new geographic location with historical data scarcity: a transfer learning approach

Abubakr, M., Akoush, B. M., Khalil, A., & Hassan, M. A. (2022) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-022-02666-y · Cited by 26

Classification of Freshwater Fish Diseases in Bangladesh Using a Novel Ensemble Deep Learning Model: Enhancing Accuracy and Interpretability

Maruf, A. A., Fahim, S. H., Bashar, R., Rumy, R. A., Chowdhury, S. I., & Aung, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3426041 · Cited by 25

Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification

Huang, Y., Zou, J., Meng, L., Yue, X., Zhao, Q. Y., Li, J., Song, C., Jiménez, G., Li, S., & Fu, G. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00049 · Cited by 35

Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future

Hameed, B. M. Z., Gayathri, P., Patil, V., Shekhar, P., Raza, S. Z., Karimi, H., Paul, R., Naik, N., Modi, S., Vigneswaran, G., et al. (2021) · Therapeutic Advances in Urology · doi:10.1177/17562872211044880 · Cited by 29

High-Frequency Ultrasound Dataset for Deep Learning-Based Image Quality Assessment

Czajkowska, J., Juszczyk, J., Piejko, L., & Glenc-Ambroży, M. (2022) · Sensors · doi:10.3390/s22041478 · Cited by 25

Automatic semantic segmentation and detection of vertebras and intervertebral discs by neural networks

Guinebert, S., Petit, E., Bousson, V., Bodard, S., Amoretti, N., & Kastler, B. A. (2022) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2022.100055 · Cited by 25

Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning

Iwata, H., Hayashi, Y., Hasegawa, A., Terayama, K., & Okuno, Y. (2022) · International Journal of Pharmaceutics X · doi:10.1016/j.ijpx.2022.100135 · Cited by 25

A GAN-based method for 3D lung tumor reconstruction boosted by a knowledge transfer approach

Rezaei, S. R., & Ahmadi, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15232-0 · Cited by 25

Ensemble Learning Approaches for Alzheimer’s Disease Classification in Brain Imaging Data

Mahmud, T., Aziz, M. T., Uddin, M. K., Barua, K., Rahman, T., Sharmen, N., Kaiser, M. S., Hossain, M. S., Hossain, M. S., & Andersson, K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3937-0_10 · Cited by 24

WeakMedSAM: Weakly-Supervised Medical Image Segmentation via SAM With Sub-Class Exploration and Prompt Affinity Mining

Wang, H., Huai, L., Li, W., Qi, L., Jiang, X., & Shi, Y. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3549433 · Cited by 25

Deep Learning-Based High-Frequency Ultrasound Skin Image Classification with Multicriteria Model Evaluation

Czajkowska, J., Badura, P., Korzekwa, S., Płatkowska-Szczerek, A., & Słowińska, M. (2021) · Sensors · doi:10.3390/s21175846 · Cited by 24

PlexusNet: A neural network architectural concept for medical image classification

Eminağa, O., Abbas, M., Shen, J., Laurie, M. A., Brooks, J. D., Liao, J. C., & Rubin, D. L. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106594 · Cited by 27

Deep learning approaches for classification tasks in medical X-ray, MRI, and ultrasound images: a scoping review

Laçi, H., Sevrani, K., & Iqbal, S. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01701-5 · Cited by 24

ProtoMed: Prototypical networks with auxiliary regularization for few-shot medical image classification

Ouahab, A., & Ahmed, O. B. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105337 · Cited by 24

Analysis of high-resolution reconstruction of medical images based on deep convolutional neural networks in lung cancer diagnostics

Bai, Y., Li, D., Duan, Q., & Chen, X. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106592 · Cited by 22

An Intelligent Attention-Based Transfer Learning Model for Accurate Differentiation of Bone Marrow Stains to Diagnose Hematological Disorder

Alshahrani, H. M., Sharma, G., Anand, V., Gupta, S., Sulaiman, A. A., Elmagzoub, M. A., Reshan, M. S. A., Shaikh, A., & Azar, A. T. (2023) · Life · doi:10.3390/life13102091 · Cited by 22

Large language model for medical images: a survey of taxonomy, systematic review, and future trends

Wang, P., Lu, W., Lu, C., Zhou, R., Li, M., & Qin, L. (2025) · Big Data Mining and Analytics · doi:10.26599/bdma.2024.9020090 · Cited by 21

Dementia classification from magnetic resonance images by machine learning

Waldo-Benítez, G., Padierna, L. C., Cerón, P. V., & Sosa, M. A. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09163-y · Cited by 20

SAMA: A Self-and-Mutual Attention Network for Accurate Recurrence Prediction of Non-Small Cell Lung Cancer Using Genetic and CT Data

Ai, Y., Liu, J., Li, Y., Wang, F., Du, X., Jain, R. K., Lin, L., & Chen, Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3471194 · Cited by 20

Updating methods for artificial intelligence–based clinical prediction models: a scoping review

Meijerink, L., Dunias, Z. S., Leeuwenberg, A. M., de Hond, A., Jenkins, D. A., Martin, G. P., Sperrin, M., Peek, N. B., Spijker, R., Hooft, L., et al. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111636 · Cited by 19

Detecting and classifying breast masses via YOLO-based deep learning

Karaca, B. K., Telatar, Z., Güney, S., & Dengiz, B. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11153-1 · Cited by 22

Cross-Subject EEG Channel Selection Method for Lower Limb Brain-Computer Interface

Wei, M., Huang, M., & Ni, J. (2023) · International Journal of Network Dynamics and Intelligence · doi:10.53941/ijndi.2023.100008 · Cited by 20

Self-supervised endoscopic image key-points matching

Farhat, M., Chaabouni-Chouayakh, H., & Hamadou, A. B. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118696 · Cited by 20

Large Language Models in Medical Image Analysis: A Systematic Survey and Future Directions

Urooj, B., Fayaz, M., Ali, S., Dang, L. M., & Kim, K. W. (2025) · Bioengineering · doi:10.3390/bioengineering12080818 · Cited by 26

Hybrid Deep Feature Generation for Appropriate Face Mask Use Detection

Aydemir, E., Yalçınkaya, M. A., Barua, P. D., Baygin, M., Faust, O., Doğan, Ş., Chakraborty, S., Tuncer, T., & Acharya, U. R. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19041939 · Cited by 20

AccNet24: A deep learning framework for classifying 24-hour activity behaviours from wrist-worn accelerometer data under free-living environments

Farrahi, V., Muhammad, U., Rostami, M., & Oussalah, M. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105004 · Cited by 22

Deep Learning-Based Feature Extraction from Whole-Body PET/CT Employing Maximum Intensity Projection Images: Preliminary Results of Lung Cancer Data

Gil, J., Choi, H., Paeng, J. C., Cheon, G. J., & Kang, K. W. (2023) · Nuclear Medicine and Molecular Imaging · doi:10.1007/s13139-023-00802-9 · Cited by 19

OPG ‐based dental age estimation using a data‐technical exploration of deep learning techniques

Büyükçakır, B., Bertels, J., Claes, P. D., Vandermeulen, D., Tobel, J. D., & Thevissen, P. W. (2024) · Journal of Forensic Sciences · doi:10.1111/1556-4029.15473 · Cited by 20

A Multimodal Transfer Learning Approach Using PubMedCLIP for Medical Image Classification

Dao, H. N., Nguyen, T. D., Mugisha, C., & Paik, I. (2024) · IEEE Access · doi:10.1109/access.2024.3401777 · Cited by 19

Neural network classifiers for images of genetic conditions with cutaneous manifestations

Duong, D., Waikel, R. L., Hu, P., Tekendo‐Ngongang, C., & Solomon, B. D. (2021) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2021.100053 · Cited by 19

Expert-level pediatric brain tumor segmentation in a limited data scenario with stepwise transfer learningPreprint

Boyd, A., Ye, Z., Prabhu, S. P., Tjong, M. C., Zha, Y., Zapaishchykova, A., Vajapeyam, S., Hayat, H., Chopra, R., Liu, K. X., et al. (2023) · medRxiv · doi:10.1101/2023.06.29.23292048 · Cited by 19

Predicting inflammatory response of biomimetic nanofibre scaffolds for tissue regeneration using machine learning and graph theory

Sujeeun, L. Y., Phul, I. C., Goonoo, N., Kotov, N. A., & Bhaw‐Luximon, A. (2025) · Journal of Materials Chemistry B · doi:10.1039/d4tb02494j · Cited by 19

PGNet: Projection generative network for sparse‐view reconstruction of projection‐based magnetic particle imaging

Wu, X., He, B., Gao, P., Zhang, P., Shang, Y., Zhang, L., Zhong, J., Jiang, J., Hui, H., & Tian, J. (2022) · Medical Physics · doi:10.1002/mp.16048 · Cited by 19

Capsule robot pose and mechanism state detection in ultrasound using attention-based hierarchical deep learning

Liu, X., Esser, D. S., Wagstaff, B., Zavodni, A. E. H., Matsuura, N., Kelly, J. W., & Diller, E. (2022) · Scientific Reports · doi:10.1038/s41598-022-25572-w · Cited by 19

Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer’s disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI

Chattopadhyay, T., Ozarkar, S. S., Buwa, K., Joshy, N. A., Komandur, D., Naik, J., Thomopoulos, S. I., Steeg, G. V., Ambite, J. L., & Thompson, P. M. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1387196 · Cited by 21

Big Data and AI‐Powered Modeling: A Pathway to Sustainable Precision Animal Nutrition

Zhang, S., Lai, C., Zhao, J.-B., & Wang, J.-J. (2025) · Advancement of science · doi:10.1002/advs.202507564 · Cited by 20

An artificial intelligent framework for prediction of wildlife vehicle collision hotspots based on geographic information systems and multispectral imagery

Gonzalez-Velez, J. C., Torres-Madronero, M. C., Murillo-Escobar, J. P., & Jaramillo-Fayad, J. C. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101291 · Cited by 18

Probabilistic Integration of Object Level Annotations in Chest X-ray Classification

van Sonsbeek, T., Zhen, X., Mahapatra, D., & Worring, M. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00362 · Cited by 18

A systematic study of transfer learning for colorectal cancer detection

Luo, R., & Bocklitz, T. W. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101292 · Cited by 17

DCCAFN: deep convolution cascade attention fusion network based on imaging genomics for prediction survival analysis of lung cancer

Jia, L., Ren, X., Wu, W. D., Zhao, J., Qiang, Y., & Yang, Q. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01204-2 · Cited by 17

Enhancing glomeruli segmentation through cross-species pre-training

Andreini, P., Bonechi, S., & Dimitri, G. M. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126947 · Cited by 17

Deep learning-based medical image analysis with explainable transfer learning

Hao, J. (2023) · International Conference on Computer Engineering and Distance Learning (CEDL) · doi:10.1109/cedl60560.2023.00029 · Cited by 20

Evaluating Brain Tumor Detection with Deep Learning Convolutional Neural Networks Across Multiple MRI Modalities

Stathopoulos, I., Serio, L., Karavasilis, E., Kouri, M. A., Velonakis, G., Kelekis, N., & EFSTATHOPOULOS, E. P. (2024) · Journal of Imaging · doi:10.3390/jimaging10120296 · Cited by 17

RadImageNet and ImageNet as Datasets for Transfer Learning in the Assessment of Dental Radiographs: A Comparative Study

Okazaki, S., Mine, Y., Yoshimi, Y., Iwamoto, Y., Ito, S., Peng, T., Nishimura, T., Suehiro, T., Koizumi, Y., Nomura, R., et al. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01204-9 · Cited by 17

Data integrity of food and machine learning: Strategies, advances and prospective

Li, C., Li, J., & Wang, Y. (2025) · Food Chemistry · doi:10.1016/j.foodchem.2025.143831 · Cited by 18

Phase Attention Model for Prediction of Early Recurrence of Hepatocellular Carcinoma With Multi-Phase CT Images and Clinical Data

Wang, W., Wang, F., Chen, Q., Ouyang, S., Iwamoto, Y., Han, X., Lin, L., Hu, H., Tong, R., & Chen, Y. (2022) · Frontiers in Radiology · doi:10.3389/fradi.2022.856460 · Cited by 19

The use of deep learning enables high diagnostic accuracy in detecting syndesmotic instability on weight‐bearing CT scanning

Borjali, A., Ashkani‐Esfahani, S., Bhimani, R., Guss, D., Muratoglu, O. K., DiGiovanni, C. W., Varadarajan, K. M., & Lubberts, B. (2023) · Knee Surgery Sports Traumatology Arthroscopy · doi:10.1007/s00167-023-07565-y · Cited by 15

Application of deep learning for automated diagnosis and classification of hip dysplasia on plain radiographs

Magnéli, M., Borjali, A., Takahashi, E., Axenhus, M., Malchau, H., Kk, O. M., & Varadarajan, K. M. (2024) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-024-07244-0 · Cited by 16

I-AI: A Controllable & Interpretable AI System for Decoding Radiologists’ Intense Focus for Accurate CXR Diagnoses

Pham, T.-T., Brecheisen, J., Nguyen, A., Nguyen, H., & Le, N. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00767 · Cited by 16

Recent progress in wearable tactile sensors combined with algorithms based on machine learning and signal processing

Jiang, X., Chen, R., & Zhu, H. (2021) · APL Materials · doi:10.1063/5.0043842 · Cited by 17

Automated selection of mid-height intervertebral disc slice in traverse lumbar spine MRI using a combination of deep learning feature and machine learning classifier

Natalia, F., Young, J. C., Afriliana, N., Meidia, H., Yunus, R. E., & Sudirman, S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0261659 · Cited by 17

An Overview of Deep Learning Based Small Sample Medical Imaging Classification

Wang, K. (2021) · doi:10.1109/conf-spml54095.2021.00060 · Cited by 17

A fully-convolutional residual encoder-decoder neural network to localize breast cancer on histopathology images

Farajzadeh, N., Sadeghzadeh, N., & Hashemzadeh, M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105698 · Cited by 15

Automatic Classification for Sagittal Craniofacial Patterns Based on Different Convolutional Neural Networks

Li, H., Xu, Y., Lei, Y., Wang, Q., & Gao, X. (2022) · Diagnostics · doi:10.3390/diagnostics12061359 · Cited by 16

A scalable pipeline to create synthetic datasets from functional–structural plant models for deep learning

Baker, D. N., Bauer, F., Giraud, M., Schnepf, A., Göbbert, J. H., Scharr, H., Hvannberg, E. Þ., & Riedel, M. (2023) · in silico Plants · doi:10.1093/insilicoplants/diad022 · Cited by 18

Pose pattern mining using transformer for motion classification

Lee, S.-E., Yoo, H., & Chung, K. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05325-0 · Cited by 15

Survival prediction in diffuse large B-cell lymphoma patients: multimodal PET/CT deep features radiomic model utilizing automated machine learning

Chen, J., Lin, F., Dai, Z., Chen, Y., Fan, Y., Li, A., & Zhao, C. (2024) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-024-05905-0 · Cited by 16

Preclinical Validation of a Semi-Autonomous Robot for Transperineal Prostate Biopsy

Maris, B., Fiazza, M.-C., Piccoli, M., Tenga, C., Palladino, L., Puliatti, S., Iseppi, A., Ferrari, R., Piro, A., Bonetti, L. R., et al. (2022) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2022.3159737 · Cited by 15

Evolutionary computation-based machine learning for Smart City high-dimensional Big Data Analytics

Li, X., Zhang, D., Zheng, Y., Hong, W., Wang, W., Xia, J., & Lv, Z. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109955 · Cited by 16

Intelligent image analysis recognizes important orchid viral diseases

Tsai, C.-F., Huang, C., Wu, F.-H., Lin, C., Lee, C., Yu, S.-S., Chan, Y., & Jan, F. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.1051348 · Cited by 14

Automated T1 and T2 mapping segmentation on cardiovascular magnetic resonance imaging using deep learning

Kalapos, A., Szabó, L. E., Dohy, Z., Kiss, M., Merkely, B. P., Gyires-Tóth, B., & Vágó, H. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1147581 · Cited by 14

Semi-supervised medical image segmentation based on GAN with the pyramid attention mechanism and transfer learning

Li, G., Wang, J., Tan, Y., Shen, L., Jiao, D., & Zhang, Q. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16213-z · Cited by 14

Artificial intelligence unravels interpretable malignancy grades of prostate cancer on histology images

Eminağa, O., Fred Saad, C., Tian, Z., Wolffgang, U., Karakiewicz, P. I., Ouellet, V., Azzi, F., Spieker, T., Helmke, B. M., Graefen, M., et al. (2024) · npj Imaging · doi:10.1038/s44303-023-00005-z · Cited by 17

Selecting reliable instances based on evidence theory for transfer learning

Lv, Y., Zhang, B., Yue, X., Denœux, T., & Yue, S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123739 · Cited by 15

Digital twin modeling for predicting loading resistance of loaders driven by deep transfer learning

Wu, B., Hou, L., Wang, S., Bu, X., & Xiang, C. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103245 · Cited by 14

Emissivity prediction of functionalized surfaces using artificial intelligence

Acosta, G. I., Reicks, A., Moreno, M., Borjali, A., Zuhlke, C., & Ghashami, M. (2022) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2022.108325 · Cited by 14

Using 3D deep features from CT scans for cancer prognosis based on a video classification model: A multi‐dataset feasibility study

Chen, J., Wee, L., Dekker, A. L. A. J., & Bermejo, I. (2023) · Medical Physics · doi:10.1002/mp.16430 · Cited by 15

Can artificial intelligence help decision-making in arthroscopy? Part 2: The IA-RTRHO model – a decision-making aid for long head of the biceps diagnoses in small rotator cuff tears

Benhenneda, R., Brouard, T., Charousset, C., & Berhouet, J. (2023) · Orthopaedics & Traumatology Surgery & Research · doi:10.1016/j.otsr.2023.103652 · Cited by 16

Clustering-based spatial analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images

Lee, J., Warner, E., Shaikhouni, S., Bitzer, M., Kretzler, M., Gipson, D. S., Pennathur, S., Bellovich, K. A., Bhat, Z. Y., Gadegbeku, C. A., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-39591-8 · Cited by 14

Flatfeet Severity-Level Detection Based on Alignment Measuring

Alsaidi, F. A., & Moria, K. M. (2023) · Sensors · doi:10.3390/s23198219 · Cited by 15

Automated Grading of Red Ginseng Using DenseNet121 and Image Preprocessing Techniques

Kim, M., Kim, J., Kim, J. S., Lim, J., & Moon, K. (2023) · Agronomy · doi:10.3390/agronomy13122943 · Cited by 13

RA-XTNet: A Novel CNN Model to Predict Rheumatoid Arthritis from Hand Radiographs and Thermal Images: A Comparison with CNN Transformer and Quantum Computing

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