# Phase Recognition in Contrast-Enhanced CT Scans based on Deep Learning and Random Sampling

arXiv: https://arxiv.org/abs/2203.11206 · HTML: https://mlsanity.com/paper/2203.11206/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 20 Mar 2022.

## Builds on (most cited first, 15 of 15)

- [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)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [GhostNet: More Features from Cheap Operations](https://mlsanity.com/paper/1911.11907/) (2019)
- [The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling](https://mlsanity.com/paper/2001.00570/) (2020)
- [On the adequacy of untuned warmup for adaptive optimization](https://mlsanity.com/paper/1910.04209/) (2019)
- [Generalizability issues with deep learning models in medicine and their potential solutions: illustrated with Cone-Beam Computed Tomography (CBCT) to Computed Tomography (CT) image conversion](https://mlsanity.com/paper/2004.07700/) (2020)
- [CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT](https://mlsanity.com/paper/1909.02511/) (2019)
- [Contrast Phase Classification with a Generative Adversarial Network](https://mlsanity.com/paper/1911.06395/) (2019)

## Built on by (most cited first, 10 of 10)

- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph](https://mlsanity.com/paper/2208.03545/) (2022)
- [See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancement](https://mlsanity.com/paper/2512.07251/) (2025)
- [Learning Robust Visual Features in Computed Tomography Enables Efficient Transfer Learning for Clinical Tasks](https://mlsanity.com/paper/2604.04133/) (2026)
- [Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices](https://mlsanity.com/paper/2208.03403/) (2022)
- [MedBLINK: Probing Basic Perception in Multimodal Language Models for Medicine](https://mlsanity.com/paper/2508.02951/) (2025)
- [Leveraging Multiphase CT for Quality Enhancement of Portal Venous CT: Utility for Pancreas Segmentation](https://mlsanity.com/paper/2501.14013/) (2025)
- [Segment-and-Classify: ROI-Guided Generalizable Contrast Phase Classification in CT Using XGBoost](https://mlsanity.com/paper/2501.14066/) (2025)
- [MieDB-100k: A Comprehensive Dataset for Medical Image Editing](https://mlsanity.com/paper/2602.09587/) (2026)
- [PHASOR: Anatomy- and Phase-Consistent Volumetric Diffusion for CT Virtual Contrast Enhancement](https://mlsanity.com/paper/2604.01053/) (2026)

## Abstract (arXiv)

A fully automated system for interpreting abdominal computed tomography (CT) scans with multiple phases of contrast enhancement requires an accurate classification of the phases. This work aims at developing and validating a precise, fast multi-phase classifier to recognize three main types of contrast phases in abdominal CT scans. We propose in this study a novel method that uses a random sampling mechanism on top of deep CNNs for the phase recognition of abdominal CT scans of four different phases: non-contrast, arterial, venous, and others. The CNNs work as a slice-wise phase prediction, while the random sampling selects input slices for the CNN models. Afterward, majority voting synthesizes the slice-wise results of the CNNs, to provide the final prediction at scan level. Our classifier was trained on 271,426 slices from 830 phase-annotated CT scans, and when combined with majority voting on 30% of slices randomly chosen from each scan, achieved a mean F1-score of 92.09% on our internal test set of 358 scans. The proposed method was also evaluated on 2 external test sets: CTPAC-CCRCC (N = 242) and LiTS (N = 131), which were annotated by our experts. Although a drop in performance has been observed, the model performance remained at a high level of accuracy with a mean F1-score of 76.79% and 86.94% on CTPAC-CCRCC and LiTS datasets, respectively. Our experimental results also showed that the proposed method significantly outperformed the state-of-the-art 3D approaches while requiring less computation time for inference.