Перейти к основному содержанию
AkademIndex

Продукты

Для разработчиков

AkademBaseОткрытый API экосистемы
← Назад к работе

Работы, цитирующие эту работу

Работ: 6

Работа: UNETR: Transformers for 3D Medical Image Segmentation

  1. TabNet and TabTransformer: Novel Deep Learning Models for Chemical Toxicity Prediction in Comparison With Machine Learning

    Firas Mahmood Mustafa, Ali Fawzi Al‐Hussainy, Hardik Doshi +10

    СтатьяComputational Drug Discovery MethodsJournal of Applied Toxicology2025Цитирований: 0
    ABI

  2. Radiomics meets transformers: A novel approach to tumor segmentation and classification in mammography for breast cancer

    Mohamed J. Saadh, Qusay Mohammed Hussain, Rafid Jihad Albadr +10

    СтатьяRadiomics and Machine Learning in Medical ImagingJournal of X-Ray Science and Technology2025Цитирований: 0
    ABI

  3. Brain tumor segmentation capabilities of 3D deep learning architectures (U-Net, V-Net, Attention U-Net, ResNet-based U-Net, Transformer-based model)

    Otabek Puldajoov, Pooja Pooja, Ambuj Aggarwal

    СтатьяBrain Tumor Detection and ClassificationNeural Computing and Applications2025Цитирований: 0
    ABI

  4. Deep Learning-Based Framework for Automated Segmentation of Tumors in Medical Images

    Pooja Kapila, Khuriyat Karimberdiyevna Khudayberdiyeva, Ravinder Singh Kuntal +3

    СтатьяAdvanced Radiotherapy Techniques2025Цитирований: 0
    ABI

  5. Global-local feature fusion in MRI brain tumor segmentation via enhanced U-Net-ViT architecture and adaptive contrast preprocessing

    Sun Xinxin, Uzair Aslam Bhatti, Junfeng Zhang +5

    СтатьяBrain Tumor Detection and ClassificationThe Visual Computer2026Цитирований: 0
    ABI

  6. GAN-Based Cross-Modality Brain MRI Synthesis: Paired Versus Unpaired Training and Comparison with Diffusion and Transformer Models

    Behnam Kiani Kalejahi, Sebelan Danishvar, Mohammad Javad Rajabi

    СтатьяGenerative Adversarial Networks and Image SynthesisBiomimetics2026Цитирований: 0
    ABI