mlsanityData to 7 Oct 2026

BugNIST -- a Large Volumetric Dataset for Object Detection under Domain Shift

arXiv 2304.01838 ↗First posted 4 Apr 2023cs.CV cs.AIcited by 1

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Abstract (arXiv)

Domain shift significantly influences the performance of deep learning algorithms, particularly for object detection within volumetric 3D images. Annotated training data is essential for deep learning-based object detection. However, annotating densely packed objects is time-consuming and costly. Instead, we suggest training models on individually scanned objects, causing a domain shift between training and detection data. To address this challenge, we introduce the BugNIST dataset, comprising 9154 micro-CT volumes of 12 bug types and 388 volumes of tightly packed bug mixtures. This dataset is characterized by having objects with the same appearance in the source and target domains, which is uncommon for other benchmark datasets for domain shift. During training, individual bug volumes labeled by class are utilized, while testing employs mixtures with center point annotations and bug type labels. Together with the dataset, we provide a baseline detection analysis, with the aim of advancing the field of 3D object detection methods.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 40 of its 82 references that are in the map. First posted on arXiv 4 Apr 2023.

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40 of 82 references.

References · 40

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  6. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  7. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  8. 2016Densely Connected Convolutional Networkscited by 6,691
  9. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  10. 2017Squeeze-and-Excitation Networkscited by 4,763
  11. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  12. 2022A ConvNet for the 2020scited by 3,545
28 more
  1. 2015Fast R-CNNcited by 2,930
  2. 2023A Survey of Large Language Modelscited by 2,537
  3. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  4. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  5. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,327
  6. 2021Swin Transformer V2: Scaling Up Capacity and Resolutioncited by 1,215
  7. 2017Deep Hashing Network for Unsupervised Domain Adaptationcited by 1,181
  8. 2017Deeper, Broader and Artier Domain Generalizationcited by 983
  9. 2016Playing for Data: Ground Truth from Computer Gamescited by 816
  10. 2018Deep Visual Domain Adaptation: A Surveycited by 634
  11. 2013Domain Generalization via Invariant Feature Representationcited by 622
  12. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  13. 2022MONAI: An open-source framework for deep learning in healthcarecited by 438
  14. 2019A large annotated medical image dataset for the development and evaluation of segmentation algorithmscited by 371
  15. 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 351
  16. 2020Deep Domain-Adversarial Image Generation for Domain Generalisationcited by 231
  17. 2020A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promisescited by 230
  18. 2016Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challengecited by 198
  19. 2022SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptationcited by 146
  20. 2021Benchmarking Detection Transfer Learning with Vision Transformerscited by 130
  21. 2018Why rankings of biomedical image analysis competitions should be interpreted with carecited by 90
  22. 2021nnDetection: A Self-configuring Method for Medical Object Detectioncited by 60
  23. 2022CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentationcited by 58
  24. 2018Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detectioncited by 46
  25. 2021Detecting COVID-19 and Community Acquired Pneumonia using Chest CT scan images with Deep Learningcited by 8
  26. 2018Review of Deep Learningcited by 5
  27. 2020Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagerycited by 4
  28. 2020Contraband Materials Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagerycited by 3

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