Contrastive Representation Learning for Rapid Intraoperative Diagnosis of Skull Base Tumors Imaged Using Stimulated Raman Histology

• URL: http://arxiv.org/abs/2108.03555v1
• Date: Sun, 8 Aug 2021 02:49:29 GMT
• Title: Contrastive Representation Learning for Rapid Intraoperative Diagnosis of Skull Base Tumors Imaged Using Stimulated Raman Histology
• Authors: Cheng Jiang, Abhishek Bhattacharya, Joseph Linzey, Rushikesh Joshi, Sung Jik Cha, Sudharsan Srinivasan, Daniel Alber, Akhil Kondepudi, Esteban Urias, Balaji Pandian, Wajd Al-Holou, Steve Sullivan, B. Gregory Thompson, Jason Heth, Chris Freudiger, Siri Khalsa, Donato Pacione, John G. Golfinos, Sandra Camelo-Piragua, Daniel A. Orringer, Honglak Lee, Todd Hollon
• Abstract summary: Intraoperative diagnosis of skull base tumors can be challenging due to tumor diversity and lack of intraoperative pathology resources. We developed an independent and parallel intraoperative pathology workflow that can provide rapid and accurate skull base tumor diagnoses.
• Score: 26.194247664756553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Accurate diagnosis of skull base tumors is essential for providing personalized surgical treatment strategies. Intraoperative diagnosis can be challenging due to tumor diversity and lack of intraoperative pathology resources. Objective: To develop an independent and parallel intraoperative pathology workflow that can provide rapid and accurate skull base tumor diagnoses using label-free optical imaging and artificial intelligence (AI). Method: We used a fiber laser-based, label-free, non-consumptive, high-resolution microscopy method ($<$ 60 sec per 1 $\times$ 1 mm$^\text{2}$), called stimulated Raman histology (SRH), to image a consecutive, multicenter cohort of skull base tumor patients. SRH images were then used to train a convolutional neural network (CNN) model using three representation learning strategies: cross-entropy, self-supervised contrastive learning, and supervised contrastive learning. Our trained CNN models were tested on a held-out, multicenter SRH dataset. Results: SRH was able to image the diagnostic features of both benign and malignant skull base tumors. Of the three representation learning strategies, supervised contrastive learning most effectively learned the distinctive and diagnostic SRH image features for each of the skull base tumor types. In our multicenter testing set, cross-entropy achieved an overall diagnostic accuracy of 91.5%, self-supervised contrastive learning 83.9%, and supervised contrastive learning 96.6%. Our trained model was able to identify tumor-normal margins and detect regions of microscopic tumor infiltration in whole-slide SRH images. Conclusion: SRH with AI models trained using contrastive representation learning can provide rapid and accurate intraoperative diagnosis of skull base tumors.

Related papers

• Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings [49.84018914962972]
  Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising. We tested a method for generating microscopic histological images from MRI scans of the corpus callosum using conditional generative adversarial network (cGAN) architecture.
  arXiv  Detail & Related papers  (2023-10-16T13:58:53Z)
• Automated ensemble method for pediatric brain tumor segmentation [0.0]
  This study introduces a novel ensemble approach using ONet and modified versions of UNet. Data augmentation ensures robustness and accuracy across different scanning protocols. Results indicate that this advanced ensemble approach offers promising prospects for enhanced diagnostic accuracy.
  arXiv  Detail & Related papers  (2023-08-14T15:29:32Z)
• Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging [59.79875531898648]
  DeepGlioma is an artificial-intelligence-based diagnostic screening system. DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy.
  arXiv  Detail & Related papers  (2023-03-23T18:50:18Z)
• OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology [30.845626784372186]
  We present OpenSRH, the first public dataset of stimulated Raman histology (SRH) images from brain tumors patients. OpenSRH contains data from the most common brain tumors diagnoses, full pathologic annotations, whole slide tumor segmentations, raw and processed optical imaging data. We benchmark two computer vision tasks: multiclass histologic brain tumor classification and patch-based contrastive representation learning.
  arXiv  Detail & Related papers  (2022-06-16T20:43:26Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• Deep Learning models for benign and malign Ocular Tumor Growth Estimation [3.1558405181807574]
  Clinicians often face issues in selecting suitable image processing algorithm for medical imaging data. A strategy for the selection of a proper model is presented here.
  arXiv  Detail & Related papers  (2021-07-09T05:40:25Z)
• Brain Tumors Classification for MR images based on Attention Guided Deep Learning Model [3.6328238032703806]
  We analyze the existing technology and propose an attention guided deep convolution neural network (CNN) model. Our method can achieve the average accuracy of 99.18% under ten-fold cross-validation for identifying the presence or absence of tumor. It can assist doctors in achieving efficient clinical diagnosis of brain tumors.
  arXiv  Detail & Related papers  (2021-04-06T07:25:52Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)
• Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection [49.32653090178743]
  Early detection of head and neck tumors is crucial for patient survival. Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors. We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
  arXiv  Detail & Related papers  (2020-04-21T17:07:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.