Explainable deep learning models in medical image analysis

Deep learning methods have been very effective for a variety of medical diagnostic tasks and has even beaten human experts on some of those. However, the black-box nature of the algorithms has restricted clinical use. Recent explainability studies aim to show the features that influence the decision of a model the most. The majority of literature reviews of this area have focused on taxonomy, ethics, and the need for explanations. A review of the current applications of explainable deep learning for different medical imaging tasks is presented here. The various approaches, challenges for clinical deployment, and the areas requiring further research are discussed here from a practical standpoint of a deep learning researcher designing a system for the clinical end-users.Comment: Preprint submitted to J.Imaging, MDP

Explainable AI in medical imaging:An overview for clinical practitioners - Saliency-based XAI approaches

Since recent achievements of Artificial Intelligence (AI) have proven significant success and promising results throughout many fields of application during the last decade, AI has also become an essential part of medical research. The improving data availability, coupled with advances in high-performance computing and innovative algorithms, has increased AI's potential in various aspects. Because AI rapidly reshapes research and promotes the development of personalized clinical care, alongside its implementation arises an urgent need for a deep understanding of its inner workings, especially in high-stake domains. However, such systems can be highly complex and opaque, limiting the possibility of an immediate understanding of the system's decisions. Regarding the medical field, a high impact is attributed to these decisions as physicians and patients can only fully trust AI systems when reasonably communicating the origin of their results, simultaneously enabling the identification of errors and biases. Explainable AI (XAI), becoming an increasingly important field of research in recent years, promotes the formulation of explainability methods and provides a rationale allowing users to comprehend the results generated by AI systems. In this paper, we investigate the application of XAI in medical imaging, addressing a broad audience, especially healthcare professionals. The content focuses on definitions and taxonomies, standard methods and approaches, advantages, limitations, and examples representing the current state of research regarding XAI in medical imaging. This paper focuses on saliency-based XAI methods, where the explanation can be provided directly on the input data (image) and which naturally are of special importance in medical imaging.</p

Explainable artificial intelligence:an analytical review

This paper provides a brief analytical review of the current state-of-the-art in relation to the explainability of artificial intelligence in the context of recent advances in machine learning and deep learning. The paper starts with a brief historical introduction and a taxonomy, and formulates the main challenges in terms of explainability building on the recently formulated National Institute of Standards four principles of explainability. Recently published methods related to the topic are then critically reviewed and analyzed. Finally, future directions for research are suggested

Програмний додаток моніторингу рівня стресу

Магістерська дисертація за темою «Програмний додаток моніторингу рівня стресу» виконана студентом кафедри біомедичної кібернетики ФБМІ Шевагою Діаною Олександрівною зі спеціальності 122 «Комп’ютерні науки» за освітньо-професійною програмою «Комп’ютерні технології в біології та медицині», та складається зі: вступу; 3 розділів («Аналіз літературних джерел», «Матеріали та методи дослідження», «Моніторинг рівня стресу»), розділу з розрахунком стартап-проєкту, висновків до кожного з цих розділів; загальних висновків; списку використаних джерел, який налічує 65 найменування. Загальний обсяг роботи 113 сторінки. Актуальність теми. У нинішньому сценарії після COVID, оскільки більшість із нас перебуває вдома, рівень стресу є найвищим за весь час через зростання тривоги, що призводить до вищого пульсу. Тому великий інтерес викликав дослідження основних механізмів стресу та моніторинг різних біофізіологічних і біохімічних реакцій організму на стрес. Надійний біомаркер або індикатор стресу міг би забезпечити точний моніторинг стресу, потенційно дозволяючи запобігти патологічним станам на ранніх стадіях. Тривалий стрес може мати серйозні наслідки для здоров’я. Тому здатність визначати, коли людина перебуває в стані стресу, може бути дуже корисною для запобігання проблемам зі здоров’ям, особливо у пацієнтів із суїцидальними думками. Мета і завдання дослідження. Метою роботи реалізація програмного продукту для моніторингу рівня стресу. Для цього необхідно виконати наступні завдання: 1. Аналіз джерел на аналогів. 2. Підбір потрібних матеріалів та методів дослідження. 3. Побудова алгоритмів класифікації. 4. Реалізація програмного продукту. Об’єкт дослідження. Дані електрокардіограми. Предмет дослідження. Застосування алгоритмів класифікації для визначення наявності стресу. Методи дослідження. Машинне навчання, метод k-найближчих сусідів, штучна нейронна мережа, дерево рішень, випадковий ліс.Master's thesis on the topic "Software application for stress level monitoring" is executed by the student of the department of biomedical cybernetics (Faculty of Biomedical Engineering) Shevaga Diana Oleksandrivna in the specialty 122 "Computer science" on the educational and professional program "Computer technologies in biology and medicine", and consists of: introduction ; 3 sections ("Analysis of literary sources", "Research materials and methods", "Monitoring the level of stress"), section with a startup calculation, conclusions to each of these sections; general conclusions; references, which includes 65 titles. The total volume of work is 113 pages. Relevance of the topic. In the current post-Covid scenario, with most of us at home, stress levels are at an all-time high due to rising anxiety, leading to higher heart rates. Therefore, the study of the main mechanisms of stress and the monitoring of various biophysiological and biochemical reactions of the body to stress caused great interest. A reliable biomarker or indicator of stress could provide accurate monitoring of stress, potentially allowing the prevention of pathological conditions at an early stage. Prolonged stress can have serious health consequences. Therefore, the ability to identify when a person is under stress can be very helpful in preventing health problems, especially in patients with suicidal thoughts. Objective and task sof the study. The objectibe of the work is the implementation of a software product for stress level monitoring. Its achievement involves solving the following tasks: 1. Analysis of sources on analogues. 2. Selection of necessary materials and research methods. 3. Construction of classification algorithms. 4. Implementation of the software product. Object of study. Electrocardiogram data. Subject of study. Application of classification algorithms to determine the presence of stress. Research methods. Machine learning, k-nearest neighbors method, artificial neural network, decision tree, random forest

Towards Interpretability of Segmentation Networks by analyzing DeepDreams

International audience<p>Interpretability of a neural network can be expressed as the identification of patterns or features to which the network can be either sensitive or indifferent. To this aim, a method inspired by DeepDream is proposed, where the activation of a neuron is maximized by performing gradient ascent on an input image. The method outputs curves that show the evolution of features during the maximization.A controlled experiment show how it enables assess the robustness to a given feature, or by contrast its sensitivity. The method is illustrated on the task of segmenting tumors in liver CT images.</p