Embedded processing architectures are often integrated into devices to develop novel functions in a cost-effective medical system. In order to integrate neural networks in medical equipment, these models require specialized optimizations for preparing their integration in a high-efficiency and power-constrained environment. In this paper, we research the feasibility of quantized networks with limited memory for the detection of Barrett’s neoplasia. An Efficientnet-lite1+Deeplabv3 architecture is proposed, which is trained using a quantizationaware training scheme, in order to achieve an 8-bit integer-based model. The performance of the quantized model is comparable with float32 precision models. We show that the quantized model with only 5-MB memory is capable of reaching the same performance scores with 95% Area Under the Curve (AUC), compared to a fullprecision U-Net architecture, which is 10× larger. We have also optimized the segmentation head for efficiency and reduced the output to a resolution of 32×32 pixels. The results show that this resolution captures sufficient segmentation detail to reach a DICE score of 66.51%, which is comparable to the full floating-point model. The proposed lightweight approach also makes the model quite energy-efficient, since it can be real-time executed on a 2-Watt Coral Edge TPU. The obtained low power consumption of the lightweight Barrett’s esophagus neoplasia detection and segmentation system enables the direct integration into standard endoscopic equipment.
Computer-Aided Diagnosis (CADx) systems for characterization of Narrow-Band Imaging (NBI) videos of suspected lesions in Barrett’s Esophagus (BE) can assist endoscopists during endoscopic surveillance. The real clinical value and application of such CADx systems lies in real-time analysis of endoscopic videos inside the endoscopy suite, placing demands on robustness in decision making and insightful classification matching with the clinical opinions. In this paper, we propose a lightweight int8-based quantized neural network architecture supplemented with an efficient stability function on the output for real-time classification of NBI videos. The proposed int8-architecture has low-memory footprint (4.8 MB), enabling operation on a range of edge devices and even existing endoscopy equipment. Moreover, the stability function ensures robust inclusion of temporal information from the video to provide a continuously stable video classification. The algorithm is trained, validated and tested with a total of 3,799 images and 284 videos of in total 598 patients, collected from 7 international centers. Several stability functions are experimented with, some of them being clinically inspired by weighing low-confidence predictions. For the detection of early BE neoplasia, the proposed algorithm achieves a performance of 92.8% accuracy, 95.7% sensitivity, and 91.4% specificity, while only 5.6% of the videos are without a final video classification. This work shows a robust, lightweight and effective deep learning-based CADx system for accurate automated real-time endoscopic video analysis, suited for embedding in endoscopy clinical practice.
The majority of the encouraging experimental results published on AI-based endoscopic Computer-Aided Detection (CAD) systems have not yet been reproduced in clinical settings, mainly due to highly curated datasets used throughout the experimental phase of the research. In a realistic clinical environment, these necessary high image-quality standards cannot be guaranteed, and the CAD system performance may degrade. While several studies have previously presented impressive outcomes with Frame Informativeness Assessment (FIA) algorithms, the current-state of the art implies sequential use of FIA and CAD systems, affecting the time performance of both algorithms. Since these algorithms are often trained on similar datasets, we hypothesise that part of the learned feature representations can be leveraged for both systems, enabling a more efficient implementation. This paper explores this case for early Barrett cancer detection by integrating the FIA algorithm within the CAD system. Sharing the weights between two tasks reduces the number of parameters from 16 to 11 million and the number of floating-point operations from 502 to 452 million. Due to the lower complexity of the architecture, the proposed model leads to inference time up to 2 times faster than the state-of-the-art sequential implementation while retaining the classification performance.
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