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The onset of cancer disease is a traumatic experience for both patients and their families that suddenly change the patient's life and is accompanied by important physical, emotional, and psycho-social problems. The complexity of this scenario has been exacerbated by the COVID-19 pandemic which dramatically affected the continuity of the provision of optimal care to chronic patients. Telemedicine can support the management of oncology care paths by furnishing a suite of effective and efficient tools to monitor the therapies of cancer patients. In particular, this is a suitable setting for therapies that are administered at home. In this paper, we present an AI-based system, called Arianna , designed and implemented to support and monitor patients treated by the professionals belonging to the Breast Cancer Unit Network (BCU-Net) along the entire clinical path of breast cancer treatment. We describe in this work the three modules composing the Arianna system (the tools for patients and clinicians, and the symbolic AI-based module). The system has been validated in a qualitative way and we demonstrated how the Arianna solution reached a high level of acceptability by all types of end-users by making it suitable for a concrete integration into the daily practice of the BCU-Net. • We present an AI-based system called Arianna. • Arianna aims to support patients and clinicians within breast cancer care path. • A symbolic reasoner is used to perform real-time monitoring of patient's data. • Arianna demonstrated its suitability for being used in real-world contexts. [ABSTRACT FROM AUTHOR]

Nowadays, breast and cervical cancers are respectively the first and fourth most common causes of cancer death in females. It is believed that, automated systems based on artificial intelligence would allow the early diagnostic which increases significantly the chances of proper treatment and survival. Although Convolutional Neural Networks (CNNs) have achieved human-level performance in object classification tasks, the regular growing of the amount of medical data and the continuous increase of the number of classes make them difficult to learn new tasks without being re-trained from scratch. Nevertheless, fine tuning and transfer learning in deep models are techniques that lead to the well-known catastrophic forgetting problem. In this paper, an Incremental Deep Tree (IDT) framework for biological image classification is proposed to address the catastrophic forgetting of CNNs allowing them to learn new classes while maintaining acceptable accuracies on the previously learnt ones. To evaluate the performance of our approach, the IDT framework is compared against with three popular incremental methods, namely iCaRL, LwF and SupportNet. The experimental results on MNIST dataset achieved 87 % of accuracy and the obtained values on the BreakHis, the LBC and the SIPaKMeD datasets are promising with 92 %, 98 % and 93 % respectively. [ABSTRACT FROM AUTHOR]

In this paper, we developed BreastScreening-AI within two scenarios for the classification of multimodal beast images: (1) Clinician-Only; and (2) Clinician-AI. The novelty relies on the introduction of a deep learning method into a real clinical workflow for medical imaging diagnosis. We attempt to address three high-level goals in the two above scenarios. Concretely, how clinicians: i) accept and interact with these systems, revealing whether are explanations and functionalities required; ii) are receptive to the introduction of AI-assisted systems, by providing benefits from mitigating the clinical error; and iii) are affected by the AI assistance. We conduct an extensive evaluation embracing the following experimental stages: (a) patient selection with different severities, (b) qualitative and quantitative analysis for the chosen patients under the two different scenarios. We address the high-level goals through a real-world case study of 45 clinicians from nine institutions. We compare the diagnostic and observe the superiority of the Clinician-AI scenario, as we obtained a decrease of 27% for False-Positives and 4% for False-Negatives. Through an extensive experimental study, we conclude that the proposed design techniques positively impact the expectations and perceptive satisfaction of 91% clinicians, while decreasing the time-to-diagnose by 3 min per patient. [ABSTRACT FROM AUTHOR]