Related papers: Boosting radiotherapy dose calculation accuracy wi…
In this paper, we investigate the effectiveness of deep learning techniques for lung nodule classification in computed tomography scans. Using less than 10,000 training examples, our deep networks perform two times better than a standard…
A 'dual-field' strategy is often used for tumors with highly complex shapes and/or with large volumes exceeding available field-size in both passive and scanning irradiations with ion beams. Range and setup uncertainties can cause hot and…
Cone-beam breast computed tomography (CT) provides true 3D breast images with isotropic resolution and high-contrast information, detecting calcifications as small as a few hundred microns and revealing subtle tissue differences. However,…
We are in a golden age of progress in artificial intelligence (AI). Radiotherapy, due to its technology-intensive nature as well as direct human-machine interactions, is perfectly suited for benefitting from AI to enhance accuracy and…
We propose the BayesDose-Framework, a Bayesian approach for fast and accurate dose prediction in proton therapy. Our framework is based on a previously published deterministic LSTM model and is trained and evaluated on simulated beamlet…
An individualized dose rule recommends a dose level within a continuous safe dose range based on patient level information such as physical conditions, genetic factors and medication histories. Traditionally, personalized dose finding…
In critical decision support systems based on medical imaging, the reliability of AI-assisted decision-making is as relevant as predictive accuracy. Although deep learning models have demonstrated significant accuracy, they frequently…
The paper presents alternative statistical methods for biological dosimetry, such as the Bayesian and Monte Carlo method. The classical Gaussian and robust Bayesian fit algorithms for the linear, linear-quadratic as well as saturated and…
We highlight emerging uses of artificial intelligence (AI) in the field of theranostics, focusing on its significant potential to enable routine and reliable personalization of radiopharmaceutical therapies (RPTs). Personalized RPTs require…
Deep learning has significantly advanced the potential for automated contouring in radiotherapy planning. In this manuscript, guided by contemporary literature, we underscore three key insights: (1) High-quality training data is essential…
A combination of traditional image processing methods with advanced neural networks concretes a predictive and preventive healthcare paradigm. This study offers rapid, accurate, and non-invasive diagnostic solutions that can significantly…
Purpose: Retinoblastoma (RB) is the most common eye tumor in childhood and can be treated external radiotherapy. The purpose of this work is to evaluate the adequacy of Monte Carlo simulations and the accuracy of a commercial treatment…
Deep learning integration into medical imaging systems has transformed disease detection and diagnosis processes with a focus on pneumonia identification. The study introduces an intricate deep learning system using Convolutional Neural…
Motivation: Tumor classification using Imaging Mass Spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are…
In the development of new cancer treatment, an essential step is to determine the maximum tolerated dose (MTD) via phase I clinical trials. Generally speaking, phase I trial designs can be classified as either model-based or algorithm-based…
Near infrared diffuse optical tomography (DOT) provides an imaging modality for the oxygenation of tissue. In this paper, we propose a novel machine learning algorithm based on time-domain radiative transfer equation. We use temporal…
Breast density assessment is a crucial component of mammographic interpretation, with high breast density (BI-RADS categories C and D) representing both a significant risk factor for developing breast cancer and a technical challenge for…
Thanks to advancements in diagnosis and treatment, prostate cancer patients have high long-term survival rates. Currently, an important goal is to preserve quality-of-life during and after treatment. The relationship between the radiation a…
Background: The COVID-19 pandemic has overwhelmed healthcare systems, emphasizing the need for AI-driven tools to assist in rapid and accurate patient prognosis. Chest X-ray imaging is a widely available diagnostic tool, but existing…
Radiation therapy aims to deliver the prescribed amount of dose to a tumour at the same time as sparing the surrounding tissues as much as possible. In charged particle therapy, delivering the prescribed dose is equivalent to delivering the…