# Label Filling via Mixed Supervision for Medical Image Segmentation from Noisy Annotations

arXiv: https://arxiv.org/abs/2410.16057 · HTML: https://mlsanity.com/paper/2410.16057/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 63 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [A Probabilistic U-Net for Segmentation of Ambiguous Images](https://mlsanity.com/paper/1806.05034/) (2018)
- [Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion](https://mlsanity.com/paper/1902.03680/) (2019)
- [MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data](https://mlsanity.com/paper/2002.03366/) (2020)
- [Unpaired Multi-modal Segmentation via Knowledge Distillation](https://mlsanity.com/paper/2001.03111/) (2020)
- [Who Said What: Modeling Individual Labelers Improves Classification](https://mlsanity.com/paper/1703.08774/) (2017)
- [T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos](https://mlsanity.com/paper/1604.02532/) (2016)
- [A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities](https://mlsanity.com/paper/1905.13077/) (2019)
- [Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis](https://mlsanity.com/paper/2007.07222/) (2020)
- [Disentangling Human Error from the Ground Truth in Segmentation of Medical Images](https://mlsanity.com/paper/2007.15963/) (2020)
- [Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning](https://mlsanity.com/paper/1809.10780/) (2018)
- [Deep Distance Transform for Tubular Structure Segmentation in CT Scans](https://mlsanity.com/paper/1912.03383/) (2019)
- [Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies](https://mlsanity.com/paper/2012.04872/) (2020)
- [Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training](https://mlsanity.com/paper/2205.04723/) (2022)
- [3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management](https://mlsanity.com/paper/2012.04701/) (2020)
- [Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling](https://mlsanity.com/paper/2007.14848/) (2020)
- [Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss](https://mlsanity.com/paper/2105.01218/) (2021)
- [Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation](https://mlsanity.com/paper/2002.08694/) (2020)
- [Gaussian Dynamic Convolution for Efficient Single-Image Segmentation](https://mlsanity.com/paper/2104.08783/) (2021)
- [Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition](https://mlsanity.com/paper/2106.03455/) (2021)
- [Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization](https://mlsanity.com/paper/2012.07947/) (2020)
- [Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction](https://mlsanity.com/paper/2105.14773/) (2021)
- [Learning from Multiple Annotators by Incorporating Instance Features](https://mlsanity.com/paper/2106.15146/) (2021)
- [On The Usage Of Average Hausdorff Distance For Segmentation Performance Assessment: Hidden Bias When Used For Ranking](https://mlsanity.com/paper/2009.00215/) (2020)
- [Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks](https://mlsanity.com/paper/2308.14066/) (2023)

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

- [Label Uncertainty for Ultrasound Segmentation](https://mlsanity.com/paper/2508.15635/) (2025)

## Abstract (arXiv)

The success of medical image segmentation usually requires a large number of high-quality labels. But since the labeling process is usually affected by the raters' varying skill levels and characteristics, the estimated masks provided by different raters usually suffer from high inter-rater variability. In this paper, we propose a simple yet effective Label Filling framework, termed as LF-Net, predicting the groundtruth segmentation label given only noisy annotations during training. The fundamental idea of label filling is to supervise the segmentation model by a subset of pixels with trustworthy labels, meanwhile filling labels of other pixels by mixed supervision. More concretely, we propose a qualified majority voting strategy, i.e., a threshold voting scheme is designed to model agreement among raters and the majority-voted labels of the selected subset of pixels are regarded as supervision. To fill labels of other pixels, two types of mixed auxiliary supervision are proposed: a soft label learned from intrinsic structures of noisy annotations, and raters' characteristics labels which propagate individual rater's characteristics information. LF-Net has two main advantages. 1) Training with trustworthy pixels incorporates training with confident supervision, guiding the direction of groundtruth label learning. 2) Two types of mixed supervision prevent over-fitting issues when the network is supervised by a subset of pixels, and guarantee high fidelity with the true label. Results on five datasets of diverse imaging modalities show that our LF-Net boosts segmentation accuracy in all datasets compared with state-of-the-art methods, with even a 7% improvement in DSC for MS lesion segmentation.