# Scheduling Techniques for Liver Segmentation: ReduceLRonPlateau Vs OneCycleLR

arXiv: https://arxiv.org/abs/2202.06373 · HTML: https://mlsanity.com/paper/2202.06373/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 13 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields](https://mlsanity.com/paper/1610.02177/) (2016)
- [Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks](https://mlsanity.com/paper/1702.05970/) (2017)
- [One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation](https://mlsanity.com/paper/1911.01764/) (2019)
- [Liver lesion segmentation informed by joint liver segmentation](https://mlsanity.com/paper/1707.07734/) (2017)
- [Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation](https://mlsanity.com/paper/1710.04540/) (2017)

## Built on by (most cited first, 19 of 19)

- [BRepFormer: Transformer-Based B-rep Geometric Feature Recognition](https://mlsanity.com/paper/2504.07378/) (2025)
- [Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge](https://mlsanity.com/paper/2507.16559/) (2025)
- [Infinite-Fidelity Coregionalization for Physical Simulation](https://mlsanity.com/paper/2207.00678/) (2022)
- [Visual-Attribute Prompt Learning for Progressive Mild Cognitive Impairment Prediction](https://mlsanity.com/paper/2310.14158/) (2023)
- [Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes](https://mlsanity.com/paper/2310.19666/) (2023)
- [Skeleton-based Human Action Recognition via Convolutional Neural Networks (CNN)](https://mlsanity.com/paper/2301.13360/) (2023)
- [Fine-Grained Classification for Poisonous Fungi Identification with Transfer Learning](https://mlsanity.com/paper/2407.07492/) (2024)
- [A Comparison of Deep Learning Architectures for Spacecraft Anomaly Detection](https://mlsanity.com/paper/2403.12864/) (2024)
- [TransUNet-GradCAM: A Hybrid Transformer-U-Net with Self-Attention and Explainable Visualizations for Foot Ulcer Segmentation](https://mlsanity.com/paper/2508.03758/) (2025)
- [The limitation of neural nets for approximation and optimization](https://mlsanity.com/paper/2311.12253/) (2023)
- [TM-PATHVQA:90000+ Textless Multilingual Questions for Medical Visual Question Answering](https://mlsanity.com/paper/2407.11383/) (2024)
- [Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization](https://mlsanity.com/paper/2507.03318/) (2025)
- [A Novel Paradigm in Solving Multiscale Problems](https://mlsanity.com/paper/2402.05067/) (2024)
- [AI Radiologist: Revolutionizing Liver Tissue Segmentation with Convolutional Neural Networks and a Clinician-Friendly GUI](https://mlsanity.com/paper/2406.07688/) (2024)
- [A Transformer-based Autoregressive Decoder Architecture for Hierarchical Text Classification](https://mlsanity.com/paper/2501.13598/) (2025)
- [Beyond Imaging: Vision Transformer Digital Twin Surrogates for 3D+T Biological Tissue Dynamics](https://mlsanity.com/paper/2508.15883/) (2025)
- [WTNN: Weibull-Tailored Neural Networks for survival analysis](https://mlsanity.com/paper/2512.09163/) (2025)
- [Autonomous Fashion Outfit Composition via Unified Aesthetic Foresight Model](https://mlsanity.com/paper/2608.13888/) (2026)
- [Rethinking Radiomap Blind Prediction with Limited Environment and Configuration Representations](https://mlsanity.com/paper/2609.11255/) (2026)

## Abstract (arXiv)

Machine learning and computer vision techniques have influenced many fields including the biomedical one. The aim of this paper is to investigate the important concept of schedulers in manipulating the learning rate (LR), for the liver segmentation task, throughout the training process, focusing on the newly devised OneCycleLR against the ReduceLRonPlateau. A dataset, published in 2018 and produced by the Medical Segmentation Decathlon Challenge organizers, called Task 8 Hepatic Vessel (MSDC-T8) has been used for testing and validation. The reported results that have the same number of maximum epochs (75), and are the average of 5-fold cross-validation, indicate that ReduceLRonPlateau converges faster while maintaining a similar or even better loss score on the validation set when compared to OneCycleLR. The epoch at which the peak LR occurs perhaps should be made early for the OneCycleLR such that the super-convergence feature can be observed. Moreover, the overall results outperform the state-of-the-art results from the researchers who published the liver masks for this dataset. To conclude, both schedulers are suitable for medical segmentation challenges, especially the MSDC-T8 dataset, and can be used confidently in rapidly converging the validation loss with a minimal number of epochs.