Cardiovascular

Human-AI Interaction in the ScreenTrustCAD Trial: Recall Proportion and Positive Predictive Value Related to Screening Mammograms Flagged by AI CAD versus a Human Reader.

Background The ScreenTrustCAD trial was a prospective study that evaluated the cancer detection rates for combinations of artificial intelligence (AI) computer-aided detection (CAD) and two radiologists. The results raised concerns about the tendency of radiologists to agree with AI CAD too much (when AI CAD made an erroneous flagging) or too little (when AI CAD made a correct flagging). Purpose T...

Nonlinear Sparse Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data

Motivation: Biomedical studies increasingly produce multi-view high-dimensional datasets (e.g., multi-omics) that demand integrative analysis. Existing canonical correlation analysis (CCA) and generalized CCA methods address at most two of the following three key aspects simultaneously: (i) nonlinear dependence, (ii) sparsity for variable selection, and (iii) generalization to more than two data...

Rapid Parameter Inference with Uncertainty Quantification for a Radiological Plume Source Identification Problem

In the event of a nuclear accident, or the detonation of a radiological dispersal device, quickly locating the source of the accident or blast is im...

Differentially private fine-tuned NF-Net to predict GI cancer type

Based on global genomic status, the cancer tumor is classified as Microsatellite Instable (MSI) and Microsatellite Stable (MSS). Immunotherapy is us...

ARTInp: CBCT-to-CT Image Inpainting and Image Translation in Radiotherapy

A key step in Adaptive Radiation Therapy (ART) workflows is the evaluation of the patient's anatomy at treatment time to ensure the accuracy of the ...

LUND-PROBE -- LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset

Radiotherapy treatment for prostate cancer relies on computed tomography (CT) and/or magnetic resonance imaging (MRI) for segmentation of target vol...

Transforming Multimodal Models into Action Models for Radiotherapy

Radiotherapy is a crucial cancer treatment that demands precise planning to balance tumor eradication and preservation of healthy tissue. Traditiona...

Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation

Neoadjuvant chemotherapy (NAC) has become a standard clinical practice for tumor downsizing in breast cancer with 18F-FDG Positron Emission Tomograp...

Gamma/hadron separation in the TAIGA experiment with neural network methods

In this work, the ability of rare VHE gamma ray selection with neural network methods is investigated in the case when cosmic radiation flux strongl...

Computational modelling of cancer nanomedicine: Integrating hyperthermia treatment into a multiphase porous-media tumour model

Heat-based cancer treatment, so-called hyperthermia, can be used to destroy tumour cells directly or to make them more susceptible to chemotherapy o...

Artificial intelligence reading digital mammogram: enhancing detection and differentiation of suspicious microcalcifications.

OBJECTIVES: To investigate the impact of artificial intelligence (AI) on enhancing the sensitivity of digital mammograms in the detection and specific...

A Serial MRI-based Deep Learning Model to Predict Survival in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.

Purpose To develop and evaluate a deep learning-based prognostic model for predicting survival in locoregionally advanced nasopharyngeal carcinoma (LA...

Using AI to Select Women with Intermediate Breast Cancer Risk for Breast Screening with MRI.

Background Combined mammography and MRI screening is not universally accessible for women with intermediate breast cancer risk due to limited MRI reso...

An Efficient Lightweight Multi Head Attention Gannet Convolutional Neural Network Based Mammograms Classification.

BACKGROUND: This research aims to use deep learning to create automated systems for better breast cancer detection and categorisation in mammogram ima...

Discovery of New HER2 Inhibitors via Computational Docking, Pharmacophore Modeling, and Machine Learning.

The human epidermal growth factor receptor 2 (HER2) is a critical oncogene implicated in the development of various aggressive cancers, particularly b...

Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence

Full-Field Digital Mammography (FFDM) is the primary imaging modality for routine breast cancer screening; however, its effectiveness is limited in ...

A two-stage dual-task learning strategy for early prediction of pathological complete response to neoadjuvant chemotherapy for breast cancer using dynamic contrast-enhanced magnetic resonance images

Rationale and Objectives: Early prediction of pathological complete response (pCR) can facilitate personalized treatment for breast cancer patients....

Fine-Tuning Open-Source Large Language Models to Improve Their Performance on Radiation Oncology Tasks: A Feasibility Study to Investigate Their Potential Clinical Applications in Radiation Oncology

Background: The radiation oncology clinical practice involves many steps relying on the dynamic interplay of abundant text data. Large language mode...

Multivariate Feature Selection and Autoencoder Embeddings of Ovarian Cancer Clinical and Genetic Data

This study explores a data-driven approach to discovering novel clinical and genetic markers in ovarian cancer (OC). Two main analyses were performe...

Marker Track: Accurate Fiducial Marker Tracking for Evaluation of Residual Motions During Breath-Hold Radiotherapy

Fiducial marker positions in projection image of cone-beam computed tomography (CBCT) scans have been studied to evaluate daily residual motion duri...