Weakly supervised 3D classification of multi-disease chest CT scans using multi-resolution deep segmentation features via dual-stage CNN architecture (DenseVNet, 3D Residual U-Net).

This Repo contains the updated implementation of our paper "Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131408 (16 March 2020)

Single image super resolution algorithm RED+ADMM+De-QuIP

A Novel Multiresolution-Statistical Texture Analysis Architecture: Radiomics-Aided Diagnosis of PDAC Based on Plain CT Images

Képalkotás szenzor adatokból

Implementation of IEC 60601-2-44 Ed.4 Annex GG for objective evaluation of CT Metal Artifact Reduction (MAR) using DDOG CHO and AUC analysis.

A Simple Tutorial for 2D and 3D U-Net Training and Test Pipeline in Pytorch

Command line tool to extract Siemens' AI-Rad Companion data from a DicomConquest Server into a relational SQLite database.

MSc záróvizsga kidolgozás orvosi mérnökinformatika szakirányra

Preprocessing images with focus on segmentation of CT slices

A comprehensive benchmark study evaluating the robustness of Segment Anything Model (SAM) and its medical domain adaptation (MedSAM) under realistic noisy medical imaging conditions.

The 3D footplate is a non-commercially viable tool to assist medical imaging of complex hindfoot pathologies. This 'Class I Medical Device' helps to position and fixate the foot to the lower leg, allowing clinical stress tests to be simulated during CT scans. The medical images acquired can provide quantitative displacements of bones.

A computer aided low-dose CT image denoising using a weighted sparse coding framework via transform filtering for clinical analysis