# Interactive Image Selection and Training for Brain Tumor Segmentation Network

arXiv: https://arxiv.org/abs/2406.03225 · HTML: https://mlsanity.com/paper/2406.03225/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 15 of 15)

- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1604.05144/) (2016)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [On Regularized Losses for Weakly-supervised CNN Segmentation](https://mlsanity.com/paper/1803.09569/) (2018)
- [nnU-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2011.00848/) (2020)
- [Learning to Segment Medical Images with Scribble-Supervision Alone](https://mlsanity.com/paper/1807.04668/) (2018)
- [Extending nn-UNet for brain tumor segmentation](https://mlsanity.com/paper/2112.04653/) (2021)
- [Scribble-based Domain Adaptation via Co-segmentation](https://mlsanity.com/paper/2007.03632/) (2020)
- [MedAL: Deep Active Learning Sampling Method for Medical Image Analysis](https://mlsanity.com/paper/1809.09287/) (2018)
- [Human-in-the-loop Extraction of Interpretable Concepts in Deep Learning Models](https://mlsanity.com/paper/2108.03738/) (2021)
- [Learning CNN filters from user-drawn image markers for coconut-tree image classification](https://mlsanity.com/paper/2008.03549/) (2020)
- [Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection](https://mlsanity.com/paper/2403.12748/) (2024)
- [CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images](https://mlsanity.com/paper/2111.08710/) (2021)
- [Convolutional Neural Networks from Image Markers](https://mlsanity.com/paper/2012.12108/) (2020)

## Built on by (most cited first, 3 of 3)

- [Multi-level Cellular Automata for FLIM networks](https://mlsanity.com/paper/2504.11406/) (2025)
- [FLIM-based Salient Object Detection Networks with Adaptive Decoders](https://mlsanity.com/paper/2504.20872/) (2025)
- [Implicit Shape-Prior for Few-Shot Assisted 3D Segmentation](https://mlsanity.com/paper/2509.08580/) (2025)

## Abstract (arXiv)

Medical image segmentation is a relevant problem, with deep learning being an exponent. However, the necessity of a high volume of fully annotated images for training massive models can be a problem, especially for applications whose images present a great diversity, such as brain tumors, which can occur in different sizes and shapes. In contrast, a recent methodology, Feature Learning from Image Markers (FLIM), has involved an expert in the learning loop, producing small networks that require few images to train the convolutional layers. In this work, We employ an interactive method for image selection and training based on FLIM, exploring the user's knowledge. The results demonstrated that with our methodology, we could choose a small set of images to train the encoder of a U-shaped network, obtaining performance equal to manual selection and even surpassing the same U-shaped network trained with backpropagation and all training images.