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Domain adaptive unsupervised instance segmentation for biomedical images
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Classroom Contents
Computer Vision for Segmenting & Analyzing Subcellular Components in Cryo-Electron Tomography
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- 1 Intro
- 2 CryoET data analysis offers many opportunities... but also present significant challenges
- 3 Quantifying mitochondrial granules in HD neurons
- 4 HD Patient iPSC-Derived Neurons on CryoEM Grids
- 5 Objective: Automated Quantification of Mito Granules
- 6 Training a 3D Segmentation Model
- 7 Quantitative Characterization of Mitochondrial Granules in Neurites of HD Neurons
- 8 Ongoing and Future Directions
- 9 Further reducing annotation needs for training computer vision algorithms
- 10 Unsupervised learning methods for segmentation
- 11 Motivations for our work in unsupervised segmentation
- 12 Hyperbolic space
- 13 Poincare ball model
- 14 Gyrovector operations bring linear algebra to the Poincare ball
- 15 Our work: reconstructing visual hierarchy as a pretext task
- 16 Self-supervised hierarchical triplet loss
- 17 Hyperbolic clustering
- 18 Experimental validation
- 19 BraTS Dataset (Menze et al 2015)
- 20 Cryogenic electron tomography
- 21 Summary
- 22 Domain adaptive unsupervised instance segmentation for biomedical images
- 23 DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images
- 24 Background: Mask R-CNN instance segmentation architecture
- 25 DARCNN model: feature-level adaptation
- 26 DARCNN model: pseudo-labelling
- 27 Potential unsupervised discovery applications in Cryo-ET
- 28 Developing Computer Vision Methods for Segmenting and Analyzing Subcellular Components in Cryo-ET
