# Metadata Improves Segmentation Through Multitasking Elicitation

arXiv: https://arxiv.org/abs/2308.09411 · HTML: https://mlsanity.com/paper/2308.09411/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 18 Aug 2023.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge](https://mlsanity.com/paper/1912.01054/) (2019)
- [Modulating early visual processing by language](https://mlsanity.com/paper/1707.00683/) (2017)
- [Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data](https://mlsanity.com/paper/1910.03910/) (2019)
- [Conditioned-U-Net: Introducing a Control Mechanism in the U-Net for Multiple Source Separations](https://mlsanity.com/paper/1907.01277/) (2019)
- [ivadomed: A Medical Imaging Deep Learning Toolbox](https://mlsanity.com/paper/2010.09984/) (2020)
- [Benefits of Linear Conditioning with Metadata for Image Segmentation](https://mlsanity.com/paper/2102.09582/) (2021)
- [Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks](https://mlsanity.com/paper/1812.11302/) (2018)

## Built on by (most cited first, 1 of 1)

- [Sim2Real SAR Image Restoration: Metadata-Driven Models for Joint Despeckling and Sidelobes Reduction](https://mlsanity.com/paper/2601.01541/) (2026)

## Abstract (arXiv)

Metainformation is a common companion to biomedical images. However, this potentially powerful additional source of signal from image acquisition has had limited use in deep learning methods, for semantic segmentation in particular. Here, we incorporate metadata by employing a channel modulation mechanism in convolutional networks and study its effect on semantic segmentation tasks. We demonstrate that metadata as additional input to a convolutional network can improve segmentation results while being inexpensive in implementation as a nimble add-on to popular models. We hypothesize that this benefit of metadata can be attributed to facilitating multitask switching. This aspect of metadata-driven systems is explored and discussed in detail.