Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network

(2020) · doi:10.1016/j.neurobiolaging.2020.12.005 · Cited by 78

A review of deep learning-based three-dimensional medical image registration methods

Xiao, H., Teng, X., Liu, C., Li, T., Ren, G., Yang, R., Shen, D., & Cai, J. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-175 · Cited by 77

Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy

Carneiro, G., Zorron Cheng Tao Pu, L., Singh, R., & Burt, A. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101653 · Cited by 76

Longitudinal self-supervised learning

Zhao, Q., Liu, Z., Adeli, E., & Pohl, K. M. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102051 · Cited by 71

OSPREY 3.0: Open-source protein redesign for you, with powerful new features

Hallen, M. A., Martin, J. W., Ojewole, A., Jou, J. D., Lowegard, A. U., Frenkel, M. S., Gainza, P., Nisonoff, H. M., Mukund, A., Wang, S., et al. (2018) · Journal of Computational Chemistry · doi:10.1002/jcc.25522 · Cited by 72

Automation of 3D reconstruction of neural tissue from large volume of conventional serial section transmission electron micrographs

Mishchenko, Y. (2009) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2008.09.006 · Cited by 73

An efficient deepfake video detection using robust deep learning

Qadir, A., Mahum, R., El-Meligy, M. A., Ragab, A. E., AlSalman, A., & Awais, M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e25757 · Cited by 78

SceneSkim: Searching and Browsing Movies Using Synchronized Captions, Scripts and Plot Summaries

Pavel, A., Goldman, D. B., Hartmann, B., & Agrawala, M. (2015) · Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology · doi:10.1145/2807442.2807502 · Cited by 67

Domain-specific language models pre-trained on construction management systems corpora

Zhong, Y., & Goodfellow, S. D. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105316 · Cited by 65

A co-design framework of neural networks and quantum circuits towards quantum advantage

Jiang, W., Xiong, J., & Shi, Y. (2021) · Nature Communications · doi:10.1038/s41467-020-20729-5 · Cited by 63

Twelve key challenges in medical machine learning and solutions

Ellis, R. J., Sander, R. M., & Limon, A. (2022) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2022.100068 · Cited by 61

A survey of transformer-based multimodal pre-trained modals

Han, X., Wang, Y.-T., Feng, J.-L., Deng, C., Chen, Z.-H., Huang, Y.-A., Su, H., Hu, L., & Hu, P.-W. (2023) · Neurocomputing · doi:10.1016/j.neucom.2022.09.136 · Cited by 59

Deep learning enabled classification of Mayo endoscopic subscore in patients with ulcerative colitis

Bhambhvani, H. P., & Zamora, A. (2021) · European Journal of Gastroenterology & Hepatology · doi:10.1097/meg.0000000000001952 · Cited by 59

RLAfford: End-to-End Affordance Learning for Robotic Manipulation

Geng, Y., An, B., Geng, H., Chen, Y., Yang, Y., & Dong, H. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10161571 · Cited by 57

Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks

(2022) · doi:10.1038/s41467-022-34600-2 · Cited by 57

Artificial intelligence based data processing algorithm for video surveillance to empower industry 3.5

Nguyen, M. T., Truong, L. H., Tran, T. T., & Chien, C.-F. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106671 · Cited by 59

Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach

Wang, Z., Li, Y., Zhai, J., Yang, S., Sun, B., & Liang, P. (2024) · Talanta · doi:10.1016/j.talanta.2024.126138 · Cited by 55

A binocular image fusion approach for minimizing false positives in handgun detection with deep learning

Olmos, R., Tabik, S., Lamas, A., Pérez-Hernández, F., & Herrera, F. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.11.015 · Cited by 57

Taxonomy of Machine Learning Safety: A Survey and Primer

Mohseni, S., Wang, H., Xiao, C., Yu, Z., Wang, Z., & Yadawa, J. (2023) · ACM Computing Surveys · doi:10.1145/3551385 · Cited by 53

Effects of MRI scanner manufacturers in classification tasks with deep learning models

Kushol, R., Parnianpour, P., Wilman, A. H., Kalra, S., & Yang, Y.-H. (2023) · Scientific Reports · doi:10.1038/s41598-023-43715-5 · Cited by 54

Emotion recognition from thermal infrared images using deep Boltzmann machine

Wang, S., He, M., Gao, Z., He, S., & Ji, Q. (2014) · Frontiers of Computer Science · doi:10.1007/s11704-014-3295-3 · Cited by 51

TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology

Wang, F.-A., Zhuang, Z., Gao, F., He, R., Zhang, S., Wang, L., Liu, J., & Li, Y. (2024) · Genome Biology · doi:10.1186/s13059-024-03293-9 · Cited by 51

Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics

Ullah, A., & Dral, P. O. (2022) · Nature Communications · doi:10.1038/s41467-022-29621-w · Cited by 51

Adversarial gesture generation with realistic gesture phasing

Ferstl, Y., Neff, M., & McDonnell, R. (2020) · Computers & Graphics · doi:10.1016/j.cag.2020.04.007 · Cited by 51

CCAFFMNet: Dual-spectral semantic segmentation network with channel-coordinate attention feature fusion module

Yi (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.056 · Cited by 49

Knowledge representation and learning of operator clinical workflow from full-length routine fetal ultrasound scan videos

Sharma, H., Drukker, L., Chatelain, P., Droste, R., Papageorghiou, A. T., & Noble, J. A. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101973 · Cited by 48

Utilization of smartphone and tablet camera photographs to predict healing of diabetes-related foot ulcers

Kim, R. B., Gryak, J., Mishra, A., Cui, C., Soroushmehr, S. M. R., Najarian, K., & Wrobel, J. S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104042 · Cited by 47

Deep learning in ultrasound elastography imaging: A review

Li, H., Bhatt, M., Qu, Z., Zhang, S., Hartel, M. C., Khademhosseini, A., & Cloutier, G. (2022) · Medical Physics · doi:10.1002/mp.15856 · Cited by 47

Learning towards conversational AI: A survey

Fu, T., Gao, S., Zhao, X., Wen, J.-R., & Yan, R. (2022) · AI Open · doi:10.1016/j.aiopen.2022.02.001 · Cited by 46

Considerations for a more ethical approach to data in AI: On data representation and infrastructure

Baird, A., & Schuller, B. (2020) · Frontiers in Big Data · doi:10.3389/fdata.2020.00025 · Cited by 46

A Systematic Review of Recent Deep Learning Approaches for 3D Human Pose Estimation

El Kaid, A., & Baïna, K. (2023) · Journal of Imaging · doi:10.3390/jimaging9120275 · Cited by 44

One step further into the blackbox: A pilot study of how to build more confidence around an AI-based decision system of breast nodule assessment in 2D ultrasound

Dong, F., She, R., Cui, C., Shi, S., Hu, X., Zeng, J., Wu, H., Xu, J., & Zhang, Y. (2021) · European Radiology · doi:10.1007/s00330-020-07561-7 · Cited by 43

TPrune: Efficient Transformer Pruning for Mobile Devices

Mao, J., Yang, H., Li, A., Li, H., & Chen, Y. (2021) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3446640 · Cited by 42

A supervised deep convolutional based bidirectional long short term memory video hashing for large scale video retrieval applications

Anuranji, R., & Srimathi, H. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102729 · Cited by 40

TreEnhance: A tree search method for low-light image enhancement

Cotogni, M., & Cusano, C. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2022.109249 · Cited by 40

High-throughput design of fiber reinforced cement-based composites using deep learning

Tong, Z., Huo, J., & Wang, Z. (2020) · Cement and Concrete Composites · doi:10.1016/j.cemconcomp.2020.103716 · Cited by 39

Design of multi-scale receptive field convolutional neural network for surface inspection of hot rolled steels

He, D., Xu, K., & Wang, D. (2019) · Image and Vision Computing · doi:10.1016/j.imavis.2019.06.008 · Cited by 38

Spinal cord detection in planning CT for radiotherapy through adaptive template matching, IMSLIC and convolutional neural networks

Diniz, J. O. B., Diniz, P. H. B., Valente, T. L. A., Silva, A. C., & Paiva, A. C. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.01.005 · Cited by 34

Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network

Diniz, P. H. B., Valente, T. L. A., Diniz, J. O. B., Silva, A. C., Gattass, M., Ventura, N., Muniz, B. C., & Gasparetto, E. L. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.011 · Cited by 34

A channel-spatial-temporal attention-based network for vibration-based damage detection

Liao, S., Liu, H., Yang, J., & Ge, Y. (2022) · Information Sciences · doi:10.1016/j.ins.2022.05.042 · Cited by 31

Commensal correlation network between segmentation and direct area estimation for bi-ventricle quantification

Luo, G., Dong, S., Wang, W., Wang, K., Cao, S., Tam, C., Zhang, H., Howey, J., Ohorodnyk, P., & Li, S. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101591 · Cited by 30

An unsupervised domain adaptation brain CT segmentation method across image modalities and diseases

Dong, D., Fu, G., Li, J., Pei, Y., & Chen, Y. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118016 · Cited by 30

Semantic labeling for prosthetic vision

Horne, L., Alvarez, J., McCarthy, C., Salzmann, M., & Barnes, N. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.02.015 · Cited by 30

GenAI mirage: The impostor bias and the deepfake detection challenge in the era of artificial illusions

Casu, M., Guarnera, L., Caponnetto, P., & Battiato, S. (2024) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2024.301795 · Cited by 30

An Initial Investigation for Detecting Vocoder Fingerprints of Fake Audio

Yan, X., Yi, J., Tao, J., Wang, C., Ma, H., Wang, T., Wang, S., & Fu, R. (2022) · Proceedings of the 1st International Workshop on Deepfake Detection for Audio Multimedia · doi:10.1145/3552466.3556525 · Cited by 30

A technical survey on statistical modelling and design methods for crowdsourcing quality control

Jin, Y., Carman, M., Zhu, Y., & Xiang, Y. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103351 · Cited by 28

A novel anomaly detection approach based on ensemble semi-supervised active learning (ADESSA)

Niu, Z., Guo, W., Xue, J., Wang, Y., Kong, Z., & Huang, L. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103190 · Cited by 27

Transformer-enabled weakly supervised abnormal event detection in intelligent video surveillance systems

Paulraj, S., & Vairavasundaram, S. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109496 · Cited by 27

Machine learning-based prediction models to guide the selection of Cas9 variants for efficient gene editing

Li, J., Wu, P., Cao, Z., Huang, G., Lu, Z., Yan, J., Zhang, H., Zhou, Y., Liu, R., Chen, H., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113765 · Cited by 26

DeepGA for automatically estimating fetal gestational age through ultrasound imaging

Dan, T., Chen, X., He, M., Guo, H., He, X., Chen, J., Xian, J., Hu, Y., Zhang, B., Wang, N., et al. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102453 · Cited by 25

SmartEye: Assisting Instant Photo Taking via Integrating User Preference with Deep View Proposal Network

Ma, S., Wei, Z., Tian, F., Fan, X., Zhang, J., Shen, X., Lin, Z., Huang, J., Měch, R., Samaras, D., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300701 · Cited by 24

Audio description from image by modal translation network

Ning, H., Zheng, X., Yuan, Y., & Lu, X. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.10.053 · Cited by 23

Iterative Closest Graph Matching for Non-rigid 3d/2d Coronary Arteries Registration

Zhu, J., Li, H., Ai, D., Yang, Q., Fan, J., Huang, Y., Song, H., Han, Y., & Yang, J. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105901 · Cited by 22

Simultaneous Segmentation of Fetal Hearts and Lungs for Medical Ultrasound Images via an Efficient Multi-scale Model Integrated With Attention Mechanism

Xi, J., Chen, J., Wang, Z., Ta, D., Lu, B., Deng, X., Li, X., & Huang, Q. (2021) · Ultrasonic Imaging · doi:10.1177/01617346211042526 · Cited by 21

CoNNa–Hardware accelerator for compressed convolutional neural networks

Struharik, R. J. R., Vukobratović, B. Z., Erdeljan, A. M., & Rakanović, D. M. (2020) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2020.102991 · Cited by 18

GenSMILES: An enhanced validity conscious representation for inverse design of molecules

Bhadwal, A. S., Kumar, K., & Kumar, N. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110429 · Cited by 17

An AI-based approach for detecting cells and microbial byproducts in low volume scanning electron microscope images of biofilms

Abeyrathna, D., Ashaduzzaman, M., Malshe, M., Kalimuthu, J., Gadhamshetty, V., Chundi, P., & Subramaniam, M. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.996400 · Cited by 17

An improved YOLOv8 for fiber bundle segmentation in X-ray computed tomography images of 2.5D composites to build the finite element model

Zhang, S., Wang, K., Zhang, H., Wang, T., Gao, X., Song, Y., & Wang, F. (2024) · Composites Part A: Applied Science and Manufacturing · doi:10.1016/j.compositesa.2024.108337 · Cited by 17

Online perceptual learning and natural language acquisition for autonomous robots

Alomari, M., Li, F., Hogg, D. C., & Cohn, A. G. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2021.103637 · Cited by 17

Are there any ‘object detectors’ in the hidden layers of CNNs trained to identify objects or scenes?

Gale, E. M., Martin, N., Blything, R., Nguyen, A., & Bowers, J. S. (2020) · Vision Research · doi:10.1016/j.visres.2020.06.007 · Cited by 16

FLASH: F ast Neura l A rchitecture S earch with H ardware Optimization

Li, G., Mandal, S. K., Ogras, U. Y., & Marculescu, R. (2021) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3476994 · Cited by 15

Detecting galaxy tidal features using self-supervised representation learning

Desmons, A., Brough, S., & Lanusse, F. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1402 · Cited by 15

Recognising complex activities with histograms of relative tracklets

Stein, S., & McKenna, S. J. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.08.012 · Cited by 11

Deep Learning-Based X-ray Inpainting for Improving Spinal 2d-3d Registration

Esfandiari, H., Weidert, S., Kövesházi, I., Anglin, C., Street, J., & Hodgson, A. J. (2021) · The International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.2228 · Cited by 12

Artificial intelligence-based recognition for variant pathogenicity of BRCA1 using AlphaFold2-predicted structures

Li, C., Zhang, L., Zhuo, Z., Su, F., Li, H., Xu, S., Liu, Y., Zhang, Z., Xie, Y., Yu, X., et al. (2023) · Theranostics · doi:10.7150/thno.79362 · Cited by 10

A multi-modal deep language model for contaminant removal from metagenome-assembled genomes

Zou, B., Wang, J., Ding, Y., Zhang, Z., Huang, Y., Fang, X., Cheung, K. C., See, S., & Zhang, L. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00908-5 · Cited by 9

A Novel Cognitively Inspired Deep Learning Approach to Detect Drivable Areas for Self-driving Cars

Jiang, F., Wang, Z., & Yue, G. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10215-7 · Cited by 9

Real-time on-board pedestrian detection using generic single-stage algorithms and on-road databases

Ortiz Castelló, V., del Tejo Catalá, O., Salvador Igual, I., & Perez-Cortes, J.-C. (2020) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881420929175 · Cited by 7

Residual Grounding Transformer Network for Terrain Recognition on the Lunar Surface

Qiu, L., Li, H., Li, Z., & Wang, C. (2021) · Applied Optics · doi:10.1364/ao.428232 · Cited by 7

Multi-stage Floor Plan Recognition and 3D Reconstruction

Cheng, X., Ding, X., Zhang, J., Liu, M., Chang, K., Wang, J., Zhou, C., & Yuan, J. (2022) · Proceedings of the 8th International Conference on Computing and Artificial Intelligence · doi:10.1145/3532213.3532271 · Cited by 1