# Does Medical Imaging learn different Convolution Filters?

arXiv: https://arxiv.org/abs/2210.13799 · HTML: https://mlsanity.com/paper/2210.13799/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [TorchXRayVision: A library of chest X-ray datasets and models](https://mlsanity.com/paper/2111.00595/) (2021)
- [Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm](https://mlsanity.com/paper/1906.03720/) (2019)
- [Automatic segmentation of MR brain images with a convolutional neural network](https://mlsanity.com/paper/1704.03295/) (2017)
- [CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters](https://mlsanity.com/paper/2203.15331/) (2022)
- [CompNet: Complementary Segmentation Network for Brain MRI Extraction](https://mlsanity.com/paper/1804.00521/) (2018)
- [Adversarial Robustness through the Lens of Convolutional Filters](https://mlsanity.com/paper/2204.02481/) (2022)

## Built on by (most cited first, 2 of 2)

- [Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging](https://mlsanity.com/paper/2403.04484/) (2024)
- [Revisiting Hidden Representations in Transfer Learning for Medical Imaging](https://mlsanity.com/paper/2302.08272/) (2023)

## Abstract (arXiv)

Recent work has investigated the distributions of learned convolution filters through a large-scale study containing hundreds of heterogeneous image models. Surprisingly, on average, the distributions only show minor drifts in comparisons of various studied dimensions including the learned task, image domain, or dataset. However, among the studied image domains, medical imaging models appeared to show significant outliers through "spikey" distributions, and, therefore, learn clusters of highly specific filters different from other domains. Following this observation, we study the collected medical imaging models in more detail. We show that instead of fundamental differences, the outliers are due to specific processing in some architectures. Quite the contrary, for standardized architectures, we find that models trained on medical data do not significantly differ in their filter distributions from similar architectures trained on data from other domains. Our conclusions reinforce previous hypotheses stating that pre-training of imaging models can be done with any kind of diverse image data.