# Survival prediction using ensemble tumor segmentation and transfer learning

arXiv: https://arxiv.org/abs/1810.04274 · HTML: https://mlsanity.com/paper/1810.04274/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2018.

## Built on by (most cited first, 2 of 2)

- [3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation](https://mlsanity.com/paper/2304.14508/) (2023)
- [Medical Image Analysis using Deep Relational Learning](https://mlsanity.com/paper/2303.16099/) (2023)

## Abstract (arXiv)

Segmenting tumors and their subregions is a challenging task as demonstrated by the annual BraTS challenge. Moreover, predicting the survival of the patient using mainly imaging features, while being a desirable outcome to evaluate the treatment of the patient, it is also a difficult task. In this paper, we present a cascaded pipeline to segment the tumor and its subregions and then we use these results and other clinical features together with image features coming from a pretrained VGG-16 network to predict the survival of the patient. Preliminary results with the training and validation dataset show a promising start in terms of segmentation, while the prediction values could be improved with further testing on the feature extraction part of the network.