21 Oct 2024
The MI-VisionShot framework introduces a training-free method for adapting vision-language models to perform slide-level classification of histopathological images in few-shot learning scenarios. This approach reduces the variability of previous zero-shot methods, achieving up to 79.9% balanced accuracy and outperforming baselines by up to 8.2% in low-shot settings.
01 May 2024
Digital-Analog Quantum Convolutional Neural Networks (DAQCNNs) integrate non-trainable digital-analog quantum kernels into hybrid quantum-classical architectures, achieving superior image classification performance on medical imaging datasets compared to classical equivalents with significantly fewer trainable parameters.
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