mlsanityData to 7 Oct 2026

MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy

arXiv 2212.03465 ↗First posted 7 Dec 2022cs.CV cs.AI cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

Cell segmentation is a fundamental task for computational biology analysis. Identifying the cell instances is often the first step in various downstream biomedical studies. However, many cell segmentation algorithms, including the recently emerging deep learning-based methods, still show limited generality under the multi-modality environment. Weakly Supervised Cell Segmentation in Multi-modality High-Resolution Microscopy Images was hosted at NeurIPS 2022 to tackle this problem. We propose MEDIAR, a holistic pipeline for cell instance segmentation under multi-modality in this challenge. MEDIAR harmonizes data-centric and model-centric approaches as the learning and inference strategies, achieving a 0.9067 F1-score at the validation phase while satisfying the time budget. To facilitate subsequent research, we provide the source code and trained model as open-source: https://github.com/Lee-Gihun/MEDIAR

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 35 of its 68 references that are in the map. First posted on arXiv 7 Dec 2022.

Builds on

35 of 68 references.

References · 35

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  6. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  7. 2017Mask R-CNNcited by 7,259
  8. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  9. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  10. 2022Generative Adversarial Networkscited by 5,603
  11. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  12. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
23 more
  1. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
  2. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,327
  3. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
  4. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  5. 2017Mixed Precision Trainingcited by 1,135
  6. 2020In Search of Lost Domain Generalizationcited by 1,042
  7. 2017Deeper, Broader and Artier Domain Generalizationcited by 983
  8. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  9. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  10. 2018Multi-Task Learning as Multi-Objective Optimizationcited by 809
  11. 2020Semi-Supervised Semantic Segmentation with Cross-Consistency Trainingcited by 375
  12. 2013OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networkscited by 224
  13. 2018Cell Detection with Star-convex Polygonscited by 182
  14. 2020PseudoSeg: Designing Pseudo Labels for Semantic Segmentationcited by 180
  15. 2020Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Modelscited by 168
  16. 2020Using Hindsight to Anchor Past Knowledge in Continual Learningcited by 160
  17. 2019Mish: A Self Regularized Non-Monotonic Activation Functioncited by 141
  18. 2021MST: Masked Self-Supervised Transformer for Visual Representationcited by 114
  19. 2021Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigationcited by 78
  20. 2020MixCo: Mix-up Contrastive Learning for Visual Representationcited by 62
  21. 2019Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentationcited by 55
  22. 2018A Two-Stream Mutual Attention Network for Semi-supervised Biomedical Segmentation with Noisy Labelscited by 26
  23. 2020Deep Learning in Mining Biological Datacited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 0; 2025: 5; 2026: 1.

10 papers.

  1. 2023Segment Anything Model for Medical Images?cited by 39
  2. 2023The Multi-modality Cell Segmentation Challenge: Towards Universal Solutionscited by 39
  3. 2025COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentationcited by 3
  4. 2023Defining the boundaries: challenges and advances in identifying cells in microscopy imagescited by 2
  5. 2025CellStyle: Improved Zero-Shot Cell Segmentation via Style Transfercited by 1
  6. 2025How To Make Your Cell Tracker Say "I dunno!"cited by 1
  7. 2025Segment Anything for Cell Trackingcited by 1
  8. 2025Glioma C6: A Novel Dataset for Training and Benchmarking Cell Segmentationcited by 1
  9. 2023Advanced Multi-Microscopic Views Cell Semi-supervised Segmentationcited by 0
  10. 2026Cell Instance Segmentation via Multi-Task Image-to-Image Schr"odinger Bridgecited by 0