# Zero Shot Learning for Multi-Modal Real Time Image Registration

arXiv: https://arxiv.org/abs/1908.06213 · HTML: https://mlsanity.com/paper/1908.06213/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [Medical image registration using unsupervised deep neural network: A scoping literature review](https://mlsanity.com/paper/2208.01825/) (2022)
- [Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy](https://mlsanity.com/paper/2202.03978/) (2022)
- [Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification](https://mlsanity.com/paper/2204.01728/) (2022)
- [A Two-Step Deep Learning Method for 3DCT-2DUS Kidney Registration During Breathing](https://mlsanity.com/paper/2305.13855/) (2023)

## Abstract (arXiv)

In this report we present an unsupervised image registration framework, using a pre-trained deep neural network as a feature extractor. We refer this to zero-shot learning, due to nonoverlap between training and testing dataset (none of the network modules in the processing pipeline were trained specifically for the task of medical image registration). Highlights of our technique are: (a) No requirement of a training dataset (b) Keypoints i.e.locations of important features are automatically estimated (c) The number of key points in this model is fixed and can possibly be tuned as a hyperparameter. (d) Uncertaintycalculation of the proposed, transformation estimates (e) Real-time registration of images. Our technique was evaluated on BraTS, ALBERT, and collaborative hospital Brain MRI data. Results suggest that the method proved to be robust for affine transformation models and the results are practically instantaneous, irrespective of the size of the input image