# CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learning

arXiv: https://arxiv.org/abs/2503.05604 · HTML: https://mlsanity.com/paper/2503.05604/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography](https://mlsanity.com/paper/1908.06948/) (2019)
- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [The Open Kidney Ultrasound Data Set](https://mlsanity.com/paper/2206.06657/) (2022)
- [COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics](https://mlsanity.com/paper/2103.10003/) (2021)
- [Is Grad-CAM Explainable in Medical Images?](https://mlsanity.com/paper/2307.10506/) (2023)
- [Ultrafast Cardiac Imaging Using Deep Learning For Speckle-Tracking Echocardiography](https://mlsanity.com/paper/2306.14265/) (2023)
- [Boosting Cardiac Color Doppler Frame Rates with Deep Learning](https://mlsanity.com/paper/2404.00067/) (2024)

## Built on by (most cited first, 6 of 6)

- [USF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencoding](https://mlsanity.com/paper/2510.22990/) (2025)
- [A Deep Learning Framework for Real-Time Image Processing in Medical Diagnostics: Enhancing Accuracy and Speed in Clinical Applications](https://mlsanity.com/paper/2510.16611/) (2025)
- [Multi-cancer detection using a computationally efficient CNN with transfer learning](https://mlsanity.com/paper/2606.22400/) (2026)
- [End-to-End Framework Integrating Generative AI and Deep Reinforcement Learning for Autonomous Ultrasound Scanning](https://mlsanity.com/paper/2511.00114/) (2025)
- [Benchmarking Self-Supervised Models for Cardiac Ultrasound View Classification](https://mlsanity.com/paper/2602.15339/) (2026)
- [Spatio-Temporal Fusion Model for Standard View Classification of Echocardiographic Videos](https://mlsanity.com/paper/2606.17437/) (2026)

## Abstract (arXiv)

Cardiac ultrasound (US) scanning is a commonly used techniques in cardiology to diagnose the health of the heart and its proper functioning. Therefore, it is necessary to consider ways to automate these tasks and assist medical professionals in classifying and assessing cardiac US images. Machine learning (ML) techniques are regarded as a prominent solution due to their success in numerous applications aimed at enhancing the medical field, including addressing the shortage of echography technicians. However, the limited availability of medical data presents a significant barrier to applying ML in cardiology, particularly regarding US images of the heart. This paper addresses this challenge by introducing the first open graded dataset for Cardiac Assessment and ClassificaTion of UltraSound (CACTUS), which is available online. This dataset contains images obtained from scanning a CAE Blue Phantom and representing various heart views and different quality levels, exceeding the conventional cardiac views typically found in the literature. Additionally, the paper introduces a Deep Learning (DL) framework consisting of two main components. The first component classifies cardiac US images based on the heart view using a Convolutional Neural Network (CNN). The second component uses Transfer Learning (TL) to fine-tune the knowledge from the first component and create a model for grading and assessing cardiac images. The framework demonstrates high performance in both classification and grading, achieving up to 99.43% accuracy and as low as 0.3067 error, respectively. To showcase its robustness, the framework is further fine-tuned using new images representing additional cardiac views and compared to several other state-of-the-art architectures. The framework's outcomes and performance in handling real-time scans were also assessed using a questionnaire answered by cardiac experts.