mlsanityData to 7 Oct 2026

OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology

arXiv 2206.08439 ↗First posted 16 Jun 2022eess.IV cs.CV cs.LGcited by 9

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Abstract (arXiv)

Accurate intraoperative diagnosis is essential for providing safe and effective care during brain tumor surgery. Our standard-of-care diagnostic methods are time, resource, and labor intensive, which restricts access to optimal surgical treatments. To address these limitations, we propose an alternative workflow that combines stimulated Raman histology (SRH), a rapid optical imaging method, with deep learning-based automated interpretation of SRH images for intraoperative brain tumor diagnosis and real-time surgical decision support. Here, we present OpenSRH, the first public dataset of clinical SRH images from 300+ brain tumors patients and 1300+ unique whole slide optical images. OpenSRH contains data from the most common brain tumors diagnoses, full pathologic annotations, whole slide tumor segmentations, raw and processed optical imaging data for end-to-end model development and validation. We provide a framework for patch-based whole slide SRH classification and inference using weak (i.e. patient-level) diagnostic labels. Finally, we benchmark two computer vision tasks: multiclass histologic brain tumor classification and patch-based contrastive representation learning. We hope OpenSRH will facilitate the clinical translation of rapid optical imaging and real-time ML-based surgical decision support in order to improve the access, safety, and efficacy of cancer surgery in the era of precision medicine. Dataset access, code, and benchmarks are available at opensrh.mlins.org.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 18 of its 67 references that are in the map. First posted on arXiv 16 Jun 2022.

Builds on

18 of 67 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  4. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  5. 2020Supervised Contrastive Learningcited by 2,803
  6. 2016Understanding deep learning requires rethinking generalizationcited by 1,657
  7. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  8. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 738
  9. 2021How to train your ViT? Data, Augmentation, and Regularization in Vision Transformerscited by 562
  10. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 549
  11. 2019Transfusion: Understanding Transfer Learning for Medical Imagingcited by 366
  12. 2014CNN Features off-the-shelf: an Astounding Baseline for Recognitioncited by 316
6 more
  1. 2019Revisiting Self-Supervised Visual Representation Learningcited by 250
  2. 2015Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classificationcited by 159
  3. 2020Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primarycited by 89
  4. 2018Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancercited by 34
  5. 2021Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligencecited by 4
  6. 2019To What Extent Does Downsampling, Compression, and Data Scarcity Impact Renal Image Analysis?cited by 2

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Cited by year

Citing papers per year: 2023: 1; 2024: 5; 2025: 3.

9 papers.

  1. 2023Hierarchical discriminative learning improves visual representations of biomedical microscopycited by 12
  2. 2024A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Modelscited by 9
  3. 2024A self-supervised framework for learning whole slide representationscited by 6
  4. 2024Step-Calibrated Diffusion for Biomedical Optical Image Restorationcited by 5
  5. 2024Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation Learningcited by 4
  6. 2024Super-resolution of biomedical volumes with 2D supervisioncited by 3
  7. 2025Hierarchical Self-Supervised Adversarial Training for Robust Vision Models in Histopathologycited by 0
  8. 2025Intelligent Histology for Tumor Neurosurgerycited by 0
  9. 2025Hierarchical Generalized Category Discovery for Brain Tumor Classification in Digital Pathologycited by 0