This special session is organised by Upgrad Education (https://www.upgrad.com/gb)
- Dr. Rupal Bhargava, upGrad Education Pvt. Ltd, India
- Dr. Rik Das, ACM Distinguished Speaker, India
- Assoc. Prof. Dr. Muhammad Ehsan Rana, Asia Pacific University of Technology and Innovation, India
Recent advancements in Computer Aided Diagnosis (CAD) has leveraged the domain as an active area of research that is pivotal in identifying lethal ailments at its inception. It has achieved remarkable progress with the popularity of recent disruptive trends in machine learning applications. A high level of precision is observed with automated CAD-based systems compared to manual detection of malignancy for terminal diseases challenging the medical science for an extensive period. This has prevented the premature death of many patients due to late detection and several procedural formalities. Therefore, it is pertinent to design efficient algorithms for proposing CAD systems to mitigate the challenges of critical illnesses at an early stage. Researchers are facing multiple challenges in preparing an automated detection system due to lack of training data, sample annotation, region of interest identification, proper segmentation, etc. Fortunately, recent advancements in computer vision and content-based image classification have paved the way for assorted techniques to address the aforementioned challenges and have helped attain novel paradigms for designing CAD systems. Popular deep learning and machine learning application have profusely added in augmenting the detection accuracy. The special session is an attempt to collate novel techniques and methodologies in the domain of content-based image classification and deep learning/machine learning techniques to design efficient computer-aided diagnosis architecture. In this age of seamless connectivity, medical devices are often connected to hospital networks, mobile phones, the internet, etc. Hence it is essential to ensure cybersecurity to prevent patient data as well as the privacy of any individual. The healthcare IoT has an ongoing and augmenting impact in the medical industry by removing the requirement of repeated office visits with telemedicine and related advancements in technology. The ledger technology implemented in blockchain results in the secure transfer of patient medical records, managing the medicine supply chain and helping healthcare researchers unlock genetic code. Big Data Analytics is pivotal in the evolution of healthcare practices and research and is efficiently applied towards aiding the process of care delivery and disease exploration. Cloud computing enables all Big Data operations through the provision of large storage and processing power. Finally, innovations and progress in software development to monitor, analyse and interpret a patient’s medical state is opening a new dimension in healthcare management. Therefore, this special session is aimed to highlight new challenges and probable innovative solutions in the domain of computer-aided diagnosis and healthcare advancements leveraged by IoT, blockchain Big Data and software development. It will also explore the advancements in the domain of cybersecurity to preserve and ensure the privacy of medical records and individual identity as a matter of fundamental rights.
|This special session seeks to include original, high-quality contributions to AI systems in healthcare. The key areas of concern include but are not limited to: