We introduce a generative probabilistic model for segmentation of brain lesions in multi-
dimensional images that generalizes the EM segmenter, a common approach for modelling …

This paper presents a model which is based on machine learning algorithms to detect brain
tumours from magnetic resonance images with high accuracy. A Convolutional Neural …

The computer-assisted study for improved deciphering imageries has been long-lasting
topics in the field of medical imaging. Normally, various imaging techniques like ultrasound …

In silico cancer models have demonstrated great potential as a tool to improve drug design,
optimise the delivery of drugs to target sites in the host tissue and, hence, improve …

Imaging response assessment is a cornerstone of patient care and drug development in
oncology. Clinicians/clinical researchers rely on tumor imaging to estimate the impact of new …

The vast majority of malignant gliomas relapse after surgery and standard radio-
chemotherapy. Novel molecular and cellular therapies are thus being developed, targeting …

Indefinite and uncontrollable growth of cells leads to tumors in the brain. The early diagnosis
and proper treatment of brain tumors are essential to prevent permanent damage to the …

Recently, Internet of Medical Things (IoMT) has gained considerable attention to provide
improved healthcare services to patients. Since earlier diagnosis of brain tumor (BT) using …

This paper develops a three-dimensional in silico hybrid model of cancer, which describes
the multi-variate phenotypic behaviour of tumour and host cells. The model encompasses …

Brain tumors vary in their position, mass, nature, and consistency of these lesions. Due to
the similarities found between brain lesions and normal tissues, many challenges are faced …