# Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation

arXiv: https://arxiv.org/abs/2307.15645 · HTML: https://mlsanity.com/paper/2307.15645/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [TransBTS: Multimodal Brain Tumor Segmentation Using Transformer](https://mlsanity.com/paper/2103.04430/) (2021)
- [Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy](https://mlsanity.com/paper/1809.04430/) (2018)
- [TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation](https://mlsanity.com/paper/2208.00713/) (2022)
- [LNDb: A Lung Nodule Database on Computed Tomography](https://mlsanity.com/paper/1911.08434/) (2019)
- [One Click Lesion RECIST Measurement and Segmentation on CT Scans](https://mlsanity.com/paper/2007.11087/) (2020)
- [Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study](https://mlsanity.com/paper/2010.04049/) (2020)
- [Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection](https://mlsanity.com/paper/2105.01828/) (2021)
- [Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis](https://mlsanity.com/paper/1910.08878/) (2019)
- [Accurate and Robust Lesion RECIST Diameter Prediction and Segmentation with Transformers](https://mlsanity.com/paper/2208.13113/) (2022)

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

- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [A Large Scale Benchmark for Test Time Adaptation Methods in Medical Image Segmentation](https://mlsanity.com/paper/2512.02497/) (2025)
- [ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation](https://mlsanity.com/paper/2312.05407/) (2023)

## Abstract (arXiv)

Pulmonary nodules and masses are crucial imaging features in lung cancer screening that require careful management in clinical diagnosis. Despite the success of deep learning-based medical image segmentation, the robust performance on various sizes of lesions of nodule and mass is still challenging. In this paper, we propose a multi-scale neural network with scale-aware test-time adaptation to address this challenge. Specifically, we introduce an adaptive Scale-aware Test-time Click Adaptation method based on effortlessly obtainable lesion clicks as test-time cues to enhance segmentation performance, particularly for large lesions. The proposed method can be seamlessly integrated into existing networks. Extensive experiments on both open-source and in-house datasets consistently demonstrate the effectiveness of the proposed method over some CNN and Transformer-based segmentation methods. Our code is available at https://github.com/SplinterLi/SaTTCA