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QuantumBoostNet is a hybrid classical-quantum architecture designed to enhance the accuracy of cardiac ultrasound view identification, addressing the limitations of traditional computer vision models in noisy medical imaging contexts. By integrating a classical backbone with a quantum head implemented as a parametrized 10-qubit circuit, the model adapts its training through a two-stage process that optimizes performance based on loss dynamics. Experimental results show that QuantumBoostNet outperforms existing state-of-the-art models, achieving significant improvements in accuracy and robustness against noise in cardiac ultrasound tasks.
QuantumBoostNet outperforms traditional models in cardiac ultrasound view identification, showcasing the potential of hybrid classical-quantum approaches in medical imaging.
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.
