# Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data

arXiv: https://arxiv.org/abs/1705.00938 · HTML: https://mlsanity.com/paper/1705.00938/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2017.

## Built on by (most cited first, 12 of 12)

- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods -- A Solution for HighRes Brain MRI](https://mlsanity.com/paper/2112.09654/) (2021)
- [A Study of Demographic Bias in CNN-based Brain MR Segmentation](https://mlsanity.com/paper/2208.06613/) (2022)
- [Deep CNNs for Peripheral Blood Cell Classification](https://mlsanity.com/paper/2110.09508/) (2021)
- [Automated Olfactory Bulb Segmentation on High Resolutional T2-Weighted MRI](https://mlsanity.com/paper/2108.04267/) (2021)
- [Label merge-and-split: A graph-colouring approach for memory-efficient brain parcellation](https://mlsanity.com/paper/2404.10572/) (2024)
- [MedSegMamba: 3D CNN-Mamba Hybrid Architecture for Brain Segmentation](https://mlsanity.com/paper/2409.08307/) (2024)
- [UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation](https://mlsanity.com/paper/2209.14378/) (2022)
- [VINNA for Neonates -- Orientation Independence through Latent Augmentations](https://mlsanity.com/paper/2311.17546/) (2023)
- [Factorisation-based Image Labelling](https://mlsanity.com/paper/2111.10326/) (2021)
- [Label tree semantic losses for rich multi-class medical image segmentation](https://mlsanity.com/paper/2507.15777/) (2025)
- [Beyond one-hot encoding? Journey into compact encoding for large multi-class segmentation](https://mlsanity.com/paper/2510.00667/) (2025)

## Abstract (arXiv)

Training deep fully convolutional neural networks (F-CNNs) for semantic image segmentation requires access to abundant labeled data. While large datasets of unlabeled image data are available in medical applications, access to manually labeled data is very limited. We propose to automatically create auxiliary labels on initially unlabeled data with existing tools and to use them for pre-training. For the subsequent fine-tuning of the network with manually labeled data, we introduce error corrective boosting (ECB), which emphasizes parameter updates on classes with lower accuracy. Furthermore, we introduce SkipDeconv-Net (SD-Net), a new F-CNN architecture for brain segmentation that combines skip connections with the unpooling strategy for upsampling. The SD-Net addresses challenges of severe class imbalance and errors along boundaries. With application to whole-brain MRI T1 scan segmentation, we generate auxiliary labels on a large dataset with FreeSurfer and fine-tune on two datasets with manual annotations. Our results show that the inclusion of auxiliary labels and ECB yields significant improvements. SD-Net segments a 3D scan in 7 secs in comparison to 30 hours for the closest multi-atlas segmentation method, while reaching similar performance. It also outperforms the latest state-of-the-art F-CNN models.