# Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting

arXiv: https://arxiv.org/abs/2107.12978 · HTML: https://mlsanity.com/paper/2107.12978/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation](https://mlsanity.com/paper/1808.01200/) (2018)
- [Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation](https://mlsanity.com/paper/2007.10033/) (2020)

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

- [Label fusion and training methods for reliable representation of inter-rater uncertainty](https://mlsanity.com/paper/2202.07550/) (2022)
- [MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning](https://mlsanity.com/paper/2205.01509/) (2022)
- [BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation](https://mlsanity.com/paper/2609.30223/) (2026)

## Abstract (arXiv)

There are many clinical contexts which require accurate detection and segmentation of all focal pathologies (e.g. lesions, tumours) in patient images. In cases where there are a mix of small and large lesions, standard binary cross entropy loss will result in better segmentation of large lesions at the expense of missing small ones. Adjusting the operating point to accurately detect all lesions generally leads to oversegmentation of large lesions. In this work, we propose a novel reweighing strategy to eliminate this performance gap, increasing small pathology detection performance while maintaining segmentation accuracy. We show that our reweighing strategy vastly outperforms competing strategies based on experiments on a large scale, multi-scanner, multi-center dataset of Multiple Sclerosis patient images.