mlsanityData to 14 Oct 2026

CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset

arXiv 2406.13113 ↗First posted 19 Jun 2024cs.CV cs.AI q-bio.NCcited by 24

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Abstract (arXiv)

Accurately segmenting brain tumors from MRI scans is important for developing effective treatment plans and improving patient outcomes. This study introduces a new implementation of the Columbia-University-Net (CU-Net) architecture for brain tumor segmentation using the BraTS 2019 dataset. The CU-Net model has a symmetrical U-shaped structure and uses convolutional layers, max pooling, and upsampling operations to achieve high-resolution segmentation. Our CU-Net model achieved a Dice score of 82.41%, surpassing two other state-of-the-art models. This improvement in segmentation accuracy highlights the robustness and effectiveness of the model, which helps to accurately delineate tumor boundaries, which is crucial for surgical planning and radiation therapy, and ultimately has the potential to improve patient outcomes.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 13 of its 31 references that are in the map. First posted on arXiv 19 Jun 2024.

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13 of 31 references.

References · 13

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  3. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  4. 2023Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentationcited by 47
  5. 2024BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrievalcited by 30
  6. 2024Credit Card Fraud Detection Using Advanced Transformer Modelcited by 26
  7. 2024Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) datacited by 26
  8. 2024Large Language Model (LLM) AI text generation detection based on transformer deep learning algorithmcited by 23
  9. 2024Enhanced Detection Classification via Clustering SVM for Various Robot Collaboration Taskcited by 19
  10. 2021Recurrent Neural Network from Adder's Perspective: Carry-lookahead RNNcited by 16
  11. 2023Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentationcited by 16
  12. 2024Research on Driver Facial Fatigue Detection Based on Yolov8 Modelcited by 11
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  1. 2024Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertisingcited by 10

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Citing papers per year: 2024: 23; 2025: 0; 2026: 1.

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