# Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2112.11065 · HTML: https://mlsanity.com/paper/2112.11065/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 66 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 22 of 22)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation](https://mlsanity.com/paper/1908.10454/) (2019)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [U-Net and its variants for medical image segmentation: theory and applications](https://mlsanity.com/paper/2011.01118/) (2020)
- [MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.07451/) (2021)
- [C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/1912.09628/) (2019)
- [DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.15954/) (2021)
- [V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1906.02817/) (2019)
- [RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network](https://mlsanity.com/paper/2101.00535/) (2021)
- [Scalable Neural Architecture Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/1906.05956/) (2019)
- [Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation](https://mlsanity.com/paper/2006.05847/) (2020)
- [Measures of Complexity for Large Scale Image Datasets](https://mlsanity.com/paper/2008.04431/) (2020)
- [Reflections on Shannon Information: In search of a natural information-entropy for images](https://mlsanity.com/paper/1609.01117/) (2016)

## Built on by (most cited first, 16 of 16)

- [Minimum Description Length Clustering to Measure Meaningful Image Complexity](https://mlsanity.com/paper/2306.14937/) (2023)
- [ESDMR-Net: A Lightweight Network With Expand-Squeeze and Dual Multiscale Residual Connections for Medical Image Segmentation](https://mlsanity.com/paper/2312.10585/) (2023)
- [Advancing Medical Image Segmentation with Mini-Net: A Lightweight Solution Tailored for Efficient Segmentation of Medical Images](https://mlsanity.com/paper/2405.17520/) (2024)
- [Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction](https://mlsanity.com/paper/2210.07451/) (2022)
- [Region Guided Attention Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2407.18970/) (2024)
- [TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesion Segmentation](https://mlsanity.com/paper/2408.09687/) (2024)
- [EUIS-Net: A Convolutional Neural Network for Efficient Ultrasound Image Segmentation](https://mlsanity.com/paper/2408.12323/) (2024)
- [LSSF-Net: Lightweight Segmentation with Self-Awareness, Spatial Attention, and Focal Modulation](https://mlsanity.com/paper/2409.01572/) (2024)
- [Generalization Capabilities of Neural Cellular Automata for Medical Image Segmentation: A Robust and Lightweight Approach](https://mlsanity.com/paper/2408.15557/) (2024)
- [AD-Net: Attention-based dilated convolutional residual network with guided decoder for robust skin lesion segmentation](https://mlsanity.com/paper/2409.05420/) (2024)
- [Medical Imaging Complexity and its Effects on GAN Performance](https://mlsanity.com/paper/2410.17959/) (2024)
- [Cause-Effect Driven Optimization for Robust Medical Visual Question Answering with Language Biases](https://mlsanity.com/paper/2506.17903/) (2025)
- [A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening](https://mlsanity.com/paper/2201.05963/) (2022)
- [Progressive Class-Wise Attention (PCA) Approach for Diagnosing Skin Lesions](https://mlsanity.com/paper/2306.07300/) (2023)
- [Human Gait Recognition using Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2309.10144/) (2023)
- [MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models](https://mlsanity.com/paper/2606.06103/) (2026)

## Abstract (arXiv)

Recent progress in encoder-decoder neural network architecture design has led to significant performance improvements in a wide range of medical image segmentation tasks. However, state-of-the-art networks for a given task may be too computationally demanding to run on affordable hardware, and thus users often resort to practical workarounds by modifying various macro-level design aspects. Two common examples are downsampling of the input images and reducing the network depth to meet computer memory constraints. In this paper we investigate the effects of these changes on segmentation performance and show that image complexity can be used as a guideline in choosing what is best for a given dataset. We consider four statistical measures to quantify image complexity and evaluate their suitability on ten different public datasets. For the purpose of our experiments we also propose two new encoder-decoder architectures representing shallow and deep networks that are more memory efficient than currently popular networks. Our results suggest that median frequency is the best complexity measure in deciding about an acceptable input downsampling factor and network depth. For high-complexity datasets, a shallow network running on the original images may yield better segmentation results than a deep network running on downsampled images, whereas the opposite may be the case for low-complexity images.