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

2025-03-07

AI Summary

To address the challenges of automatic cardiac ultrasound image classification and quality assessment under limited medical data, this paper proposes a two-stage deep transfer learning framework: first performing precise cardiac view identification using CNNs, then transferring learned features for image quality grading. Key contributions include: (1) introducing CACTUS—the first open-source, multi-view, multi-level annotated hierarchical cardiac ultrasound dataset; and (2) designing an end-to-end cascaded architecture (“view identification → quality assessment”) enabling rapid adaptation to novel views and facilitating expert-in-the-loop validation. Experiments demonstrate a 99.43% accuracy in view classification and a mean absolute error of only 0.3067 in quality grading. The framework exhibits strong robustness in few-shot and cross-view scenarios and has been validated by clinical experts for practical utility.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Automating cardiac ultrasound image classification and assessment
• Addressing limited medical data availability for machine learning
• Developing a deep learning framework for cardiac image grading

Innovation

• Open dataset for cardiac ultrasound image classification
• Deep learning framework with CNN and transfer learning
• High accuracy in image classification and grading