GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-efficient Medical Image Recognition

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

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404

Learning Transferable Visual Models From Natural Language Supervision

Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248

MIMIC-III, a freely accessible critical care database

Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141

Deep visual-semantic alignments for generating image descriptions

Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902

ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616

VQA: Visual Question Answering

Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487

AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., & He, X. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00143 · Cited by 1,923

Learning to detect unseen object classes by between-class attribute transfer

Lampert, C. H., Nickisch, H., & Harmeling, S. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206594 · Cited by 1,849

Bias and Generalizability of Foundation Models Across Datasets in Breast Mammography

Germani, É., Selin-Türk, I., Zeineddine, F., Mourad, C. J., & Albarqouni, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05185-1_3 · Cited by 7

Hybrid framework for lesion-aware, clinically coherent chest X-ray report generation using contrastive learning and large language models

Noh, W.-J., Pi, S.-W., & Lee, B.-D. (2026) · Scientific Reports · doi:10.1038/s41598-025-34799-2 · Cited by 5

TA-MedSAM: Text-augmented improved MedSAM for pulmonary lesion segmentation

Tang, S., Wang, S., Xiang, G., Zhao, J., & Wang, Y. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102698 · Cited by 5

Tackling the complexity of cancer with generative models

Conard, A. M., Hughes, M., Hall, J., Tenenholtz, N., Zimmermann, E., Crawford, L. A., Amini, A. P., & Severson, K. (2026) · Cell · doi:10.1016/j.cell.2026.03.027 · Cited by 5

Attention-driven visual emphasis for medical volumetric image visualization

Li, M., Jung, Y., Song, S., & Kim, J. (2024) · The Visual Computer · doi:10.1007/s00371-024-03596-9 · Cited by 5

Standard plane localization using denoising diffusion model with multi-scale guidance

Dou, H., Huang, Y., Huang, Y., Yang, X., Zhen, C., Zhang, Y., Xiong, Y., Huang, W., & Ni, D. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108619 · Cited by 6

Matryoshka Learning With Metric Transfer for Image-Text Matching

Wang, P., Zhang, L., Mao, Z., Lyu, N., & Zhang, Y. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3558996 · Cited by 5

Grounded knowledge-enhanced medical vision–language pre-training for chest X-ray

Deng, Q., Huang, Z., Wang, Y., Wang, Z., Wang, Z., Zhang, X., Dou, Q., Hui, Y. Y., & Hui, E. S. K. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108416 · Cited by 7

FLeak-Seg: Automated Fundus Fluorescein Leakage Segmentation via Cross-Modal Attention Learning

Wen, Y., Chen, L., Qiao, L., Deng, Y., Chen, H., Zhang, T., & Zhou, C. (2022) · IEEE Multimedia · doi:10.1109/mmul.2022.3142986 · Cited by 5

AHIVE: Anatomy-Aware Hierarchical Vision Encoding for Interactive Radiology Report Retrieval

Yan, S., Cheung, W. K., Tsang, I. W.-H., Chin, K., Tong, T. M., Cheung, K. C., & See, S. C. W. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01358 · Cited by 5

Unified Medical Image Pre-training in Language-Guided Common Semantic Space

He, X., Yang, Y., Jiang, X., Luo, X., Hu, H. R., Zhao, S., Li, D., Yang, Y., & Qiu, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73004-7_8 · Cited by 5

Open-world multi-modal machine learning decision model based on uncertain data analysis for fetal heart diagnosis

Zhu, G., Qin, Z., Xiong, H., Kumari, S., Alenazi, M. J. F., Guo, Y., & Chen, C. (2025) · Information Sciences · doi:10.1016/j.ins.2024.121868 · Cited by 6

Decoupled contrastive learning for multilingual multimodal medical pre-trained model

Li, Q. Y., Qiu, C., Liu, H., Gu, J., & Luo, D. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129809 · Cited by 5

Multimodal multitask similarity learning for vision language model on radiological images and reports

Yu, Y., Wang, J., Liu, W., Mien, I. H., Krishnaswamy, P., Yang, X., & Cheng, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130018 · Cited by 5

Novel Class Discovery in Chest X-rays via Paired Images and Text

Zhou, J., Liu, Y., & Chen, Q. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i7.28598 · Cited by 5

MVCM: Enhancing Multi-View and Cross-Modality Alignment for Medical Visual Question Answering and Medical Image-Text Retrieval

Zou, Y., & Yin, Z. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00023 · Cited by 4

TempA-VLP: Temporal-Aware Vision-Language Pretraining for Longitudinal Exploration in Chest X-Ray Image

Yang, Z., & Shen, L. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00454 · Cited by 4

Open source ML framework algorithms for biophysical models

Andrikov, D. A. (2025) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-025-01621-z · Cited by 4

Unveiling the Capabilities of Latent Diffusion Models for Classification of Lung Diseases in Chest X-Rays

Ning, J., Xing, X., Zhang, S., Ma, X., & Yang, G. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10981008 · Cited by 4

When evidence modeling meets knowledge distillation: Towards reliable contrast-enhanced knowledge distillation for non-contrast medical image segmentation

Zhao, J., & Li, S. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103677 · Cited by 4

Scaling Chest X-Ray Foundation Models From Mixed Supervisions for Dense Prediction

Wang, F., & Yu, L. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3589928 · Cited by 5

All-in-one medical image-to-image translation

Han, L., Tan, T., Huang, Y., Dou, H., Zhang, T., Gao, Y., Wang, X., Lu, C., Liang, X., Sun, Y., et al. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101138 · Cited by 6

Multi-modal graph-aware pre-training for disease classification and cross-modal retrieval in chest radiology

Chen, R., Min, W., Han, Q., Li, J., Li, L., & Wu, H. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112263 · Cited by 4

CoCa-CXR: Contrastive Captioners Learn Strong Temporal Structures for Chest X-Ray Vision-Language Understanding

Chen, Y., Xu, S., Sellergren, A., Matias, Y., Hassidim, A., Shetty, S., Golden, D. I., Yuille, A., & Yang, L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04978-0_8 · Cited by 5

RetFiner: A Vision-Language Refinement Scheme for Retinal Foundation Models

Fecso, R., Morano, J., Schmidt-Erfurth, U. M., & Bogunović, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04971-1_51 · Cited by 4

TRRG: Towards Truthful Radiology Report Generation With Cross-Modal Disease Clue Enhanced Large Language Models

Wang, Y., Sun, Y., Tan, T., Hao, C., Cui, Y., Su, X., Xie, W., Shen, L., & Yu, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04981-0_61 · Cited by 5

Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG

Selivanov, A., Müller, P., Turgut, Ö., Stolt-Ansó, N., & Rueckert, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04927-8_21 · Cited by 4

A General Framework for Efficient Medical Image Analysis via Shared Attention Vision Transformer

Liu, Y., Wen, Y., Yang, L., He, L., & Zhou, M. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3644949 · Cited by 4

MV-MLM: Bridging Multi-View Mammography and Language for Breast Cancer Diagnosis and Risk Prediction

Zheng, S.-F., Lee, H., Kooi, T., & Diba, A. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00131 · Cited by 4

HyPCA-Net: Advancing Multimodal Fusion in Medical Image Analysis

Dhar, J., Pandey, M. K., Chakladar, D. D., Haghighat, M., Alavi, A., Mistry, S., & Zaidi, N. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00183 · Cited by 4

CLARITY: Medical World Model for Guiding Treatment Decisions by Simulating Context-Aware Disease Trajectories

Ding, T., Zou, Y., Chen, C., Shah, M., & Tian, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37038-9_12 · Cited by 4

Automatic visual recognition for leaf disease based on enhanced attention mechanism

Yao, Y., Deng, X., Zhang, X., Li, J., Sun, W., & Zhang, G. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2365 · Cited by 4

Seeing Through Expert’s Eyes: Leveraging Radiologist Eye Gaze and Speech Report with Graph Neural Networks for Chest X-Ray Image Classification

Sultana, J., Qin, R., & Yin, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0901-7_9 · Cited by 4

Describing the APIs comprehensively: Obtaining the holistic representations from multiple modalities data for different tasks

Li, X., Liu, L., Chen, Z., Liu, Y., & Liu, H. (2023) · Information and Software Technology · doi:10.1016/j.infsof.2023.107188 · Cited by 4

Using Multiple Instance Learning to Build Multimodal Representations

Wang, P., Wells, W. M., Berkowitz, S., Horng, S., & Golland, P. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34048-2_35 · Cited by 4

Do We Need a Specific Corpus and Multiple High-Performance GPUs for Training the BERT Model? An Experiment on COVID-19 Dataset

Nuntachit, N., & Sugunnasil, P. (2022) · Machine Learning and Knowledge Extraction · doi:10.3390/make4030030 · Cited by 4

Multi-modal Contrastive-Generative Pre-training for Fine-grained Skin Disease Diagnosis

Ma, L., Zhao, J., Wang, G., Guo, Y., & Xu, F. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385898 · Cited by 4

Enhancing Representation in Medical Vision-Language Foundation Models Via Multi-Scale Information Extraction Techniques

Huang, W., Li, C., Zhou, H.-Y., Liu, J., Yang, H., Liang, Y., Shi, G., Zheng, H., & Wang, S. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635303 · Cited by 5

PLZero: placeholder based approach to generalized zero-shot learning for multi-label recognition in chest radiographs

Yang, C., Jin, Q., Du, F., Guo, J., & Zhou, Y. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01717-4 · Cited by 3

Multi-modal multi-scale representation learning via cross-attention between chest radiology images and free-text reports

Zhong, D., Li, X., Zhiyong, H., Wang, S., Yu, Z. W., Hou, M., Yan, Y., & Liu, Y.-S. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108318 · Cited by 3

Multi-Grained Vision-and-Language Model for Medical Image and Text Alignment

Yan, H., Yang, X., Bai, L., Li, J., & Liang, J. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3590930 · Cited by 4

MedLangViT: A Language–Vision Network for Medical Image Segmentation

Wang, Y., Su, J., Li, X., & Nakahara, E. (2025) · Electronics · doi:10.3390/electronics14153020 · Cited by 3

Integrating text and medical images for segmentation using interpretable graph neural network

Chai, S., Jain, R. K., Mo, S., Liu, J., Teng, S., Tateyama, T., Lin, L., & Chen, Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109404 · Cited by 3

Retrieval-augmented multimodal deep learning for interpretable lung nodule malignancy prediction

Hou, R., Chai, S., Jain, R. K., Li, Y., Liu, J., Teng, S., Shi, X., Lin, L., & Chen, Y. (2026) · Displays · doi:10.1016/j.displa.2026.103525 · Cited by 3

Cross-modal retrieval of chest X-ray images and diagnostic reports based on report entity graph and dual attention

Ou, W., Chen, Y., Liang, L., Gou, J., Xiong, J., Zhang, J., Lai, L., & Zhang, L. (2025) · Multimedia Systems · doi:10.1007/s00530-024-01649-6 · Cited by 4

Study-level cross-modal retrieval of chest x-ray images and reports with adapter-based fine-tuning

Chen, Y., Ou, W., Gao, Z., Lai, L., Yang, W., & Chen, Q. (2025) · Physics in Medicine and Biology · doi:10.1088/1361-6560/adaf05 · Cited by 3

Improving a segment anything model for segmenting low-quality medical images via an adapter

Bai, C., Wang, J., Han, X., & Wu, Z. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104425 · Cited by 3

A generative model uses healthy and diseased image pairs for pixel-level chest X-ray pathology localization

Suo, J., Dong, K., He, K., He, K., & Qionghai, D. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01456-y · Cited by 5

GTGM: Generative Text-Guided 3D Vision-Language Pretraining for Medical Image Segmentation

Chen, Y., Liu, C., Huang, W., Liu, X., Shi, H., Cheng, S. L., Arcucci, R., & Xiong, Z. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00699 · Cited by 3

MIFTP: A Multimodal Multi-Level Independent Fusion Framework with Improved Twin Pyramid for Multilabel Chest X-Ray Image Classification

Zeng, J., Niu, K., Lu, Y. L., Pei, S., Zhou, Y., & Guo, Z. (2022) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai56018.2022.00170 · Cited by 3

TCSA: A Text-Guided Cross-View Medical Semantic Alignment Framework for Adaptive Multi-view Visual Representation Learning

Lei, H., Huang, H., Yang, B., Cui, G., Wang, R., Wu, D., & Li, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7074-2_11 · Cited by 3

Cap2Seg: leveraging caption generation for enhanced segmentation of COVID-19 medical images

Zhao, W., Li, F., Diao, Y., Fan, P., & Chen, Z. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1439122 · Cited by 3

CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning

Du, Y., Chang, B., & Dvornek, N. C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72390-2_44 · Cited by 4

Multimodal Medical Image Binding via Shared Text Embeddings

Liu, Y., Xi, S., Liu, S., Ding, H., Jin, C., Zhong, C., He, J., Liu, C. C., & Shen, Y. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00162 · Cited by 7

Report-Concept Textual-Prompt Learning for Enhancing X-ray Diagnosis

Zhao, X., Liu, Z., Лю, Ф., Li, G., Dou, Y., & Peng, S. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681568 · Cited by 3

Self-supervised learning for early detection of neurodegenerative diseases with small data

Jiang, H. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/166402 · Cited by 3

A Study on DL for Pulmonary Embolism Prediction Harnessing Multimodal Data

Amudha, T. K., Sunitha, R., & Syam, M. E. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-2082-8_16 · Cited by 2

Learning Multi-Scale Knowledge-Guided Features for Text-Guided Face Recognition

Hasan, M. M., Sami, S. M., Nasrabadi, N. M., & Dawson, J. M. (2024) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2024.3466216 · Cited by 2

Masks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring

Gowda, S. N., & Clifton, D. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72120-5_40 · Cited by 2

Robust image representation learning via low-rank consistency regularization for subspace clustering

Abhadiomhen, S. E., Okereke, G. E., Nzeh, R., Ezeora, N. J., Abhadionmhen, O. A., & Asogwa, C. N. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-03869-3 · Cited by 2

Multimodal Retrieval Method for Images and Diagnostic Reports Using Cross-Attention

Sata, I., Amagasaki, M., & Kiyama, M. (2025) · AI · doi:10.3390/ai6020038 · Cited by 3

Texlivernet: Leveraging Medical Knowledge and Spatial-Frequency Perception for Enhanced Liver Tumor Segmentation

Jiang, X., Zhou, Z. J., Wang, H., Wang, G., & Fang, Z. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10980866 · Cited by 2

Text-guided cross-position attention for image analysis: Case of medical image

Lee, G., Choi, S. T., Kim, S. H., Chung, J., Cho, J., & Cho, J. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110297 · Cited by 2

A medical information extraction model with contrastive tuning and tagging layer training

Wang, X. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110465 · Cited by 2

Automated Pneumothorax Segmentation with a Spatial Prior Contrast Adapter

Jia, Y., & Rashed, E. A. (2025) · Applied Sciences · doi:10.3390/app15126598 · Cited by 2

Sci2Txt: Automated report generation of low-resolution SPECT bone scintigrams using spatial-position-aware and hierarchical features

Song, T., Lin, Q., Zeng, X., Cao, Y., Man, Z., Liu, C., Cai, Z., Gui, C., & Huang, X. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108359 · Cited by 2

Structure Observation Driven Image-Text Contrastive Learning for Computed Tomography Report Generation

Liu, H., Wei, D., Peng, Q., Huang, Y., Wu, X., Zheng, Y., & Wang, L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-96625-5_15 · Cited by 3

A Reality Check of Vision-Language Pre-training in Radiology: Have We Progressed Using Text?

Silva-Rodríguez, J., Dolz, J., & Ayed, I. B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-96625-5_20 · Cited by 2

SeLIP: Similarity enhanced contrastive language image pretraining for multi-modal head MRI

Liu, Z., Yang, D. S., Zhang, M., Sun, H., Wu, H., Wang, H., Shen, W., Chai, C., & Xia, S. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108519 · Cited by 4

MEMO-Stab2: Multi-View Sequence-Based Deep Learning Framework for Predicting Mutation-Induced Stability Changes in Transmembrane Proteins

Bao, Y., Liu, Z., Jin, H. Z., Wang, H., Wang, W., & Lin, G. N. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01774 · Cited by 2

Multimodal vision-language models in chest x-ray analysis: a study of generalization, supervision, and robustness

Aljaddouh, B., Malathi, D., & Alaswad, F. (2025) · Biomedical Engineering Letters · doi:10.1007/s13534-025-00535-y · Cited by 3

Revolutionizing Precise Low Back Pain Diagnosis via Contrastive Learning

Le, T. B., Vo, H. N. K., Mai, T.-H., & Phan, T. N. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-4960-3_32 · Cited by 2

Exploring the Impact of Self-Supervised Learning and Swin-B Transformer on Saliency Maps and Performance in Multi-Label Chest X-Ray Classification

Ahmed, T., Ahmed, A., Imran, A. S., Ullah, M., Manaf, A., & Soylu, A. (2026) · IEEE Access · doi:10.1109/access.2026.3655022 · Cited by 3

Meta-Entity Driven Triplet Mining for Aligning Medical Vision-Language Models

Yilmaz, M., Öztürk, Ş., Kara, M., Yavuz, M. T., Gümeler, E., Koç, A., & Çukur, T. (2026) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2026.3678306 · Cited by 2

A Cross-Modal Retrieval Framework for Radiology Reports and Chest X-Ray Images

Mekali, V., Sowbhagya, M. P., Aparna, H. D., Deepa Mathew, K., Singh, V., Nikam, N. N., Dongre, G. B., & Bhosale, Y. H. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.17163 · Cited by 2

MiT-DAUnet: MiT Enhanced Dual Attention Unet for Medical Segmentation

Chandrudu, K., Srikanth, P., Sambanna, K., Ganji, S. J., & Shivakeshav, S. (2026) · National Conference on Communications · doi:10.1109/ncc68160.2026.11478935 · Cited by 2

HiMix : Hierarchical Visual-Textual Mixing Network for Lesion Segmentation

Hwang, S., Sim, J., & Kim, W. H. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00517 · Cited by 2

Medical Referring Image Segmentation via Next-Token Mask Prediction

Chen, X., Wang, Y., Pang, G., Hao, J., Yue, C., Zhou, L., & Li, Y. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3705770 · Cited by 2

Representation Learning with Semantic-Aware Instance and Sparse Token Alignments

Bui, P.-N., Nguyen, T. D., Bum, J., Le, D.-T., & Choo, H. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31583-0_30 · Cited by 2

DinoDental : Benchmarking DINOv3 as a Unified Vision Encoder for Dental Image Analysis

Tang, K., Yang, X., Zheng, M., Liu, X., Li, X., Guo, X., Chen, R., Shen, L., & Meng, H. (2026) · Expert Systems · doi:10.1111/exsy.70451 · Cited by 2

Enhancing Biomedical Multi-modal Representation Learning with Multi-scale Pre-training and Perturbed Report Discrimination

Zhong, X., Batmanghelich, K. N., & Sun, L. (2024) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai59869.2024.00097 · Cited by 2

Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-training

Su, T., Li, J., Zhang, X., Jin, H., Chen, H., Wang, Q., Lv, F., Zhao, B., & Hu, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72083-3_56 · Cited by 2

Knowledge-Grounded Adaptation Strategy for Vision-Language Models: Building a Unique Case-Set for Screening Mammograms for Residents Training

Khan, A. U., Garrett, J., Bradshaw, T., Salkowski, L. R., Jeong, J., Tariq, A., & Banerjee, I. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72390-2_55 · Cited by 2

ASIMSA: Advanced Semantic Information Guided Multi-Scale Alignment Framework for Medical Vision-Language Pretraining

Xiao, S., Zhang, Y., Jiang, L., & Wang, Z. (2024) · International Conference on Computational Intelligence and Applications (ICCIA) · doi:10.1109/iccia62557.2024.10719240 · Cited by 2

Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-label Medical Image Classification

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