QuantumBoostNet: A Hybrid Classical-Quantum Architecture for Enhanced Accuracy in Cardiac Ultrasound View Identification

Noosaga Placements

• Deep Learning and End-to-End Representation Learningframework90%
  The paper uses deep learning models (e.g., ResNet) for image classification and proposes a hybrid architecture with a classical backbone and quantum head.
• Computer Visionsubfield90%
  The paper focuses on image classification tasks, including cardiac ultrasound view identification and standard image benchmarks (FashionMNIST, MNIST), which are core computer vision problems.
• Supervised Deep Learningframework80%
  The models are trained in a supervised manner on labeled image datasets (echocardiography, FashionMNIST, MNIST).
• Machine Learningsubfield80%
  The paper proposes a hybrid classical-quantum model with a two-phase training protocol, which is a machine learning methodology applied to classification tasks.
• Convolutional Neural Networksframework70%
  The classical backbones are based on CNNs (e.g., ViewCNN, ViewResNet), which are convolutional neural networks.
• Deep Learningsubfield70%
  The model uses deep learning components such as convolutional neural networks (e.g., ResNet) and variational quantum circuits, which are part of deep learning architectures.

Abstract

Accurate identification of the correct view or angle in cardiac ultrasound (echocardiogram) is a critical component of cardiologic imaging. This step is essential for precise anatomical interpretation, reliable measurement, and the reduction of clinical errors. Although computer vision has advanced significantly, most state-of-the-art models perform well on standard benchmarks but often yield suboptimal results in specialized medical imaging tasks due to the high level of noise present in the data. QuantumBoostNet, a hybrid classical-quantum architecture, is introduced to address these challenges. This model integrates a classical backbone with two heads: one classical and one quantum, with the quantum head implemented as a parametrized 10-qubit quantum circuit. Training occurs in two stages, with an adaptive transition between heads governed by a mixing parameter that monitors loss dynamics. Extensive experiments indicate that, despite the limited number of qubits that can be simulated, QuantumBoostNet consistently outperforms state-of-the-art classical and hybrid classical-quantum models in cardiac ultrasound view identification, achieving a relative improvement over the best competitor. QuantumBoostNet also demonstrates superior performance on established image classification benchmarks and exhibits robustness to noise. These findings support the continued development of hybrid classical-quantum models for specialized medical imaging applications.

Paper Context