Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology.

PURPOSE: Deep learning (DL) models have rapidly become a popular and cost-effective tool for image classification within oncology. A major limitation of DL models is their vulnerability to adversarial images, manipulated input images designed to cause misclassifications by DL models. The purpose of the study is to investigate the robustness of DL models trained on diagnostic images using adversarial images and explore the utility of an iterative adversarial training approach to improve the robustness of DL models against adversarial images. METHODS: We examined the impact of adversarial images on the classification accuracies of DL models trained to classify cancerous lesions across three common oncologic imaging modalities. The computed tomography (CT) model was trained to classify malignant lung nodules. The mammogram model was trained to classify malignant breast lesions. The magnetic resonance imaging (MRI) model was trained to classify brain metastases. RESULTS: Oncologic images showed instability to small pixel-level changes. A pixel-level perturbation of 0.004 (for pixels normalized to the range between 0 and 1) resulted in most oncologic images to be misclassified (CT 25.6%, mammogram 23.9%, and MRI 6.4% accuracy). Adversarial training improved the stability and robustness of DL models trained on oncologic images compared with naive models ([CT 67.7% v 26.9%], mammogram [63.4% vs 27.7%], and MRI [87.2% vs 24.3%]). CONCLUSION: DL models naively trained on oncologic images exhibited dramatic instability to small pixel-level changes resulting in substantial decreases in accuracy. Adversarial training techniques improved the stability and robustness of DL models to such pixel-level changes. Before clinical implementation, adversarial training should be considered to proposed DL models to improve overall performance and safety.

Safeguarding COVID-19 and cancer management: drug design and therapeutic approach.

Recent clinical cohort studies have highlighted that there is a three-fold greater SARS-Cov-2 infection risk in cancer patients, and overall mortality in individuals with tumours is increased by 41% with respect to general COVID-19 patients. Thus, access to therapeutics and intensive care is compromised for people with both diseases (comorbidity) and there is risk of delayed access to diagnosis. This comorbidity has resulted in extensive burden on the treatment of patients and health care system across the globe; moreover, mortality of hospitalized patients with comorbidity is reported to be 30% higher than for individuals affected by either disease. In this data-driven review, we aim specifically to address drug discoveries and clinical data of cancer management during the COVID-19 pandemic. The review will extensively address the treatment of COVID-19/cancer comorbidity; treatment protocols and new drug discoveries, including the description of drugs currently available in clinical settings; demographic features; and COVID-19 outcomes in cancer patients worldwide.