Deep learning is gaining instant popularity in computer aided diagnosis of COVID-19. Due to the high sensitivity of Computed Tomography (CT) to this disease, CT-based COVID-19 detection with visual models is currently at the forefront of medical imaging research. Outcomes published in this direction are frequently claiming highly accurate detection under deep transfer learning. This is leading medical technologists to believe that deep transfer learning is the mainstream solution for the problem. However, our critical analysis of the literature reveals an alarming performance disparity between different published results. Hence, we conduct a systematic thorough investigation to analyze the effectiveness of deep transfer learning for COVID-19 detection with CT images. Exploring 14 state-of-the-art visual models with over 200 model training sessions, we conclusively establish that the published literature is frequently overestimating transfer learning performance for the problem, even in the prestigious scientific sources. The roots of overestimation trace back to inappropriate data curation. We also provide case studies that consider more realistic scenarios, and establish transparent baselines for the problem. We hope that our reproducible investigation will help in curbing hype-driven claims for the critical problem of COVID-19 diagnosis, and pave the way for a more transparent performance evaluation of techniques for CT-based COVID-19 detection.
翻译:在对COVID-19的计算机辅助诊断中,深层次的学习正在得到迅速普及。由于对这一疾病进行精密的计算成像学(CT)的高度敏感性,以CT为基础的COVID-19检测视觉模型目前处于医学成像研究的最前沿。在这方面公布的结果经常声称在深层转移学习中发现非常精确。这导致医学技术人员认为深层转移学习是解决问题的主流解决办法。然而,我们对文献的批判性分析揭示了不同出版结果之间惊人的绩效差距。因此,我们进行了系统的彻底调查,以分析用CT图像探测COVID-19的深层转移学习的有效性。探索了14个最先进的视觉模型,并举办了200多期示范培训。我们最后确定,出版的文献经常高估这一问题的转移学习成绩,即使是在著名的科学来源中也是如此。高估深度转移学习是造成问题的主要原因。我们还提供了案例研究,这些案例研究考虑到更现实的情景,并为这一问题建立透明的基线。我们希望,我们的重新调查将有助于遏制以HVI-19为主的检测方法的CVI-19的透明性化检验方法,为COVI的CVI的诊断问题铺垫路。