mlsanityData to 7 Oct 2026

BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset

arXiv 2308.11298 ↗First posted 22 Aug 2023cs.CVcited by 27

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Abstract (arXiv)

Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors. Identifying, localizing and quantifying ICH has important clinical implications, in a bleed-dependent manner. While deep learning techniques are widely used in medical image segmentation and have been applied to the ICH segmentation task, existing public ICH datasets do not support the multi-class segmentation problem. To address this, we develop the Brain Hemorrhage Segmentation Dataset (BHSD), which provides a 3D multi-class ICH dataset containing 192 volumes with pixel-level annotations and 2200 volumes with slice-level annotations across five categories of ICH. To demonstrate the utility of the dataset, we formulate a series of supervised and semi-supervised ICH segmentation tasks. We provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 22 Aug 2023.

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11 of 30 references.

References · 11

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  3. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  4. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  5. 2018ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentationcited by 623
  6. 2021Semi-Supervised Semantic Segmentation with Cross Pseudo Supervisioncited by 462
  7. 2019Interpolation Consistency Training for Semi-Supervised Learningcited by 248
  8. 2021CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentationcited by 227
  9. 2023The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challengecited by 25
  10. 2018Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scanscited by 24
  11. 2018Practical Window Setting Optimization for Medical Image Deep Learningcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 14; 2026: 4.

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  1. 2024Large-Scale 3D Medical Image Pre-training with Geometric Context Priorscited by 34
  2. 2025MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Predictioncited by 16
  3. 2024ESA: Annotation-Efficient Active Learning for Semantic Segmentationcited by 15
  4. 2024MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodulecited by 14
  5. 2024SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentationcited by 13
  6. 2024MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation Detectioncited by 11
  7. 2025GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentationcited by 10
  8. 2025PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Imagescited by 9
  9. 2024VoxelPrompt: A Vision Agent for End-to-End Medical Image Analysiscited by 6
  10. 2024Medical Artificial Intelligence for Early Detection of Lung Cancer: A Surveycited by 5
  11. 2025DOEI: Dual Optimization of Embedding Information for Attention-Enhanced Class Activation Mapscited by 4
  12. 2025SAM-aware Test-time Adaptation for Universal Medical Image Segmentationcited by 3
15 more
  1. 2025SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentationcited by 3
  2. 2025Benchmarking DINOv3 for Multi-Task Stroke Analysis on Non-Contrast CTcited by 2
  3. 2025MedROV: Towards Real-Time Open-Vocabulary Detection Across Diverse Medical Imaging Modalitiescited by 2
  4. 2025A Novel Convolutional-Free Method for 3D Medical Imaging Segmentationcited by 1
  5. 2025Zero-Shot Multi-modal Large Language Model v.s. Supervised Deep Learning: A Comparative Study on CT-Based Intracranial Hemorrhage Subtypingcited by 1
  6. 2025Examining Deployment and Refinement of the VIOLA-AI Intracranial Hemorrhage Model Using an Interactive NeoMedSys Platformcited by 1
  7. 2025Hemorica: A Comprehensive CT Scan Dataset for Automated Brain Hemorrhage Classification, Segmentation, and Detectioncited by 1
  8. 2024Dual-Task Vision Transformer for Rapid and Accurate Intracerebral Hemorrhage CT Image Classificationcited by 0
  9. 2024Federated Voxel Scene Graph for Intracranial Hemorrhagecited by 0
  10. 20253D Medical Imaging Segmentation on Non-Contrast CTcited by 0
  11. 2025SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentationcited by 0
  12. 2026Free Lunch in Medical Image Foundation Model Pre-training via Randomized Synthesis and Disentanglementcited by 0
  13. 2026SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentationcited by 0
  14. 2026Knowledge Transfer Scaling Laws for 3D Medical Imagingcited by 0
  15. 2026BREIT: A Framework for Brain Stroke Reconstruction using Multi-Frequency 3D EITcited by 0