Abstract
Computer tomography is most commonly used for diagnosing lung cancer, which is one of the deadliest cancers in the world. Online services that allow users to share their single-channel monochrome images, in particular computer tomography scans, in order to receive independent medical advice are becoming wide-spread these days. In this paper, we propose an optimization for the previously known two-staged architecture for detecting cvancerous tumors in computer tomography scans that demonstrates the state-of-the-art results on Open Joint Monochrome Lungs Computer Tomography (OJLMCT - Open Joint Monochrome Lungs Computer Tomography dataset firstly proposed in Samarin et al. [14]) dataset. Modernized architecture allows to reduce the number of weights of the neural network based model (4,920,073 parameters vs. 26,468,315 in the original model) and its inference time (0.38 s vs. 2.15 s in the original model) without loss of neoplasms recognition quality (0.996 \(F_1\) score). The proposed results were obtained using heavyweight encoder elimination, special combined loss function and watershed based method for the automated dataset markup and a Consistency Regularization approach adaptation that are described in the current paper.
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Notes
- 1.
Online medical consultation services: betterhelp (https://www.betterhelp.com/), amwell (https://amwell.com/cm/), Yandex Health (https://health.yandex.ru/), Sber Med AI (https://sbermed.ai/).
- 2.
GPU: NVIDIA GeForce RTX 3060; CPU: Intel(R) Core(TM) i5-10400 CPU @ 2.90 GHz 2.90 GHz; RAM: 16 GB.
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Samarin, A. et al. (2023). Prior Segmentation and Attention Based Approach to Neoplasms Recognition by Single-Channel Monochrome Computer Tomography Snapshots. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13644. Springer, Cham. https://doi.org/10.1007/978-3-031-37742-6_44
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