Browsing by Subject "chest x-rays"

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    Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited triplets
    (arXiv, 2022-04) Bhimireddy, Ananth Reddy; Burns, John Lee; Purkayastha, Saptarshi; Gichoya, Judy Wawira; BioHealth Informatics, School of Informatics and Computing
    Deep learning approaches applied to medical imaging have reached near-human or better-than-human performance on many diagnostic tasks. For instance, the CheXpert competition on detecting pathologies in chest x-rays has shown excellent multi-class classification performance. However, training and validating deep learning models require extensive collections of images and still produce false inferences, as identified by a human-in-the-loop. In this paper, we introduce a practical approach to improve the predictions of a pre-trained model through Few-Shot Learning (FSL). After training and validating a model, a small number of false inference images are collected to retrain the model using \textbf{\textit{Image Triplets}} - a false positive or false negative, a true positive, and a true negative. The retrained FSL model produces considerable gains in performance with only a few epochs and few images. In addition, FSL opens rapid retraining opportunities for human-in-the-loop systems, where a radiologist can relabel false inferences, and the model can be quickly retrained. We compare our retrained model performance with existing FSL approaches in medical imaging that train and evaluate models at once.
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    Optimizing Medical Image Classification Models for Edge Devices
    (Springer, 2021-09) Abid, Areeba; Sinha, Priyanshu; Harpale, Aishwarya; Gichoya, Judy; Purkayastha, Saptarshi; BioHealth Informatics, School of Informatics and Computing
    Machine learning algorithms for medical diagnostics often require resource-intensive environments to run, such as expensive cloud servers or high-end GPUs, making these models impractical for use in the field. We investigate the use of model quantization and GPU-acceleration for chest X-ray classification on edge devices. We employ 3 types of quantization (dynamic range, float-16, and full int8) which we tested on models trained on the Chest-XRay14 Dataset. We achieved a 2–4x reduction in model size, offset by small decreases in the mean AUC-ROC score of 0.0%–0.9%. On ARM architectures, integer quantization was shown to improve inference latency by up to 57%. However, we also observe significant increases in latency on x86 processors. GPU acceleration also improved inference latency, but this was outweighed by kernel launch overhead. We show that optimization of diagnostic models has the potential to expand their utility to day-to-day devices used by patients and healthcare workers; however, these improvements are context- and architecture-dependent and should be tested on the relevant devices before deployment in low-resource environments.