Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification
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Abstract (arXiv)
We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared with grid sampling and uniform sampling techniques, it achieves higher generalization performance. We validate the strategy on two artificial datasets and two histological datasets for breast cancer and sun exposure classification.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Dec 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 4.
7 papers.
- 2022Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of Intelligencecited by 3
- 2022Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Modulecited by 3
- 2023Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentationcited by 3
- 2023Selecting the Best Optimizers for Deep Learning based Medical Image Segmentationcited by 1
- 2023DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learnercited by 1
- 2023Revealing the Underlying Patterns: Investigating Dataset Similarity, Performance, and Generalizationcited by 1
- 2021Spatial-temporal V-Net for automatic segmentation and quantification of right ventricles in gated myocardial perfusion SPECT imagescited by 0
- 2023Revealing the Underlying Patterns: Investigating Dataset Similarity, Performance, and Generalization7 Aug 2023
- 2023DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learner19 Apr 2023
- 2023Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentation22 Feb 2023
- 2023Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation5 Feb 2023
- 2022Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module17 Nov 2022
- 2022Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of Intelligence11 Nov 2022
- 2021Spatial-temporal V-Net for automatic segmentation and quantification of right ventricles in gated myocardial perfusion SPECT images11 Oct 2021