# Spatiotemporal Feature Learning Based on Two-Step LSTM and Transformer for CT Scans

arXiv: https://arxiv.org/abs/2207.01579 · HTML: https://mlsanity.com/paper/2207.01579/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 4 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [Deep Transparent Prediction through Latent Representation Analysis](https://mlsanity.com/paper/2009.07044/) (2020)
- [MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis](https://mlsanity.com/paper/2106.07524/) (2021)
- [Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network](https://mlsanity.com/paper/2003.13815/) (2020)
- [COVID-19 Detection in Chest X-ray Images Using Swin-Transformer and Transformer in Transformer](https://mlsanity.com/paper/2110.08427/) (2021)

## Built on by (most cited first, 7 of 7)

- [SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection](https://mlsanity.com/paper/2407.15728/) (2024)
- [A Closer Look at Spatial-Slice Features Learning for COVID-19 Detection](https://mlsanity.com/paper/2404.01643/) (2024)
- [COVID-19 Detection Using Slices Processing Techniques and a Modified Xception Classifier from Computed Tomography Images](https://mlsanity.com/paper/2312.07580/) (2023)
- [Simple 2D Convolutional Neural Network-based Approach for COVID-19 Detection](https://mlsanity.com/paper/2403.11230/) (2024)
- [Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization](https://mlsanity.com/paper/2507.19858/) (2025)
- [Enhanced detection of the presence and severity of COVID-19 from CT scans using lung segmentation](https://mlsanity.com/paper/2303.09440/) (2023)
- [MIA-3DCNN: COVID-19 Detection Based on a 3D CNN](https://mlsanity.com/paper/2303.10738/) (2023)

## Abstract (arXiv)

Computed tomography (CT) imaging could be very practical for diagnosing various diseases. However, the nature of the CT images is even more diverse since the resolution and number of the slices of a CT scan are determined by the machine and its settings. Conventional deep learning models are hard to tickle such diverse data since the essential requirement of the deep neural network is the consistent shape of the input data. In this paper, we propose a novel, effective, two-step-wise approach to tickle this issue for COVID-19 symptom classification thoroughly. First, the semantic feature embedding of each slice for a CT scan is extracted by conventional backbone networks. Then, we proposed a long short-term memory (LSTM) and Transformer-based sub-network to deal with temporal feature learning, leading to spatiotemporal feature representation learning. In this fashion, the proposed two-step LSTM model could prevent overfitting, as well as increase performance. Comprehensive experiments reveal that the proposed two-step method not only shows excellent performance but also could be compensated for each other. More specifically, the two-step LSTM model has a lower false-negative rate, while the 2-step Swin model has a lower false-positive rate. In summary, it is suggested that the model ensemble could be adopted for more stable and promising performance in real-world applications.