# Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: from convolutional neural networks to visual transformers

arXiv: https://arxiv.org/abs/2105.07402 · HTML: https://mlsanity.com/paper/2105.07402/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 58 references that are in the map.
- First posted on arXiv 16 May 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [Bottleneck Transformers for Visual Recognition](https://mlsanity.com/paper/2101.11605/) (2021)
- [DeepPap: Deep Convolutional Networks for Cervical Cell Classification](https://mlsanity.com/paper/1801.08616/) (2018)
- [DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques](https://mlsanity.com/paper/2102.12191/) (2021)
- [Considerations for a PAP Smear Image Analysis System with CNN Features](https://mlsanity.com/paper/1806.09025/) (2018)

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

- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks](https://mlsanity.com/paper/2212.00532/) (2022)
- [IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach](https://mlsanity.com/paper/2206.03368/) (2022)
- [Multimodal Fusion Learning with Dual Attention for Medical Imaging](https://mlsanity.com/paper/2412.01248/) (2024)
- [MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis of Pancreatic Cancer](https://mlsanity.com/paper/2112.13513/) (2021)
- [EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluation](https://mlsanity.com/paper/2110.07723/) (2021)
- [A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformer](https://mlsanity.com/paper/2205.12843/) (2022)
- [2D Self-Organized ONN Model For Handwritten Text Recognition](https://mlsanity.com/paper/2207.08139/) (2022)
- [CerviFormer: A Pap-smear based cervical cancer classification method using cross attention and latent transformer](https://mlsanity.com/paper/2303.10222/) (2023)
- [A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements](https://mlsanity.com/paper/2202.09020/) (2022)
- [An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images](https://mlsanity.com/paper/2204.01341/) (2022)
- [ECPC-IDS:A benchmark endometrail cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions](https://mlsanity.com/paper/2308.08313/) (2023)
- [Adaptive Aspect Ratios with Patch-Mixup-ViT-based Vehicle ReID](https://mlsanity.com/paper/2411.06297/) (2024)
- [Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer](https://mlsanity.com/paper/2205.08467/) (2022)
- [Does image resolution impact chest X-ray based fine-grained Tuberculosis-consistent lesion segmentation?](https://mlsanity.com/paper/2301.04032/) (2023)
- [Study on Aspect Ratio Variability toward Robustness of Vision Transformer-based Vehicle Re-identification](https://mlsanity.com/paper/2407.07842/) (2024)

## Abstract (arXiv)

Cervical cancer is a very common and fatal type of cancer in women. Cytopathology images are often used to screen for this cancer. Given that there is a possibility that many errors can occur during manual screening, a computer-aided diagnosis system based on deep learning has been developed. Deep learning methods require a fixed dimension of input images, but the dimensions of clinical medical images are inconsistent. The aspect ratios of the images suffer while resizing them directly. Clinically, the aspect ratios of cells inside cytopathological images provide important information for doctors to diagnose cancer. Therefore, it is difficult to resize directly. However, many existing studies have resized the images directly and have obtained highly robust classification results. To determine a reasonable interpretation, we have conducted a series of comparative experiments. First, the raw data of the SIPaKMeD dataset are pre-processed to obtain standard and scaled datasets. Then, the datasets are resized to 224 x 224 pixels. Finally, 22 deep learning models are used to classify the standard and scaled datasets. The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images. This conclusion is also validated via the Herlev dataset.