ct-imaging

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)

Agentic radiology report generator for CT abdomen-pelvis · two vision encoders + MLP head · 220 RATE findings · auditable, deterministic, research-only

Computes CT contrast phase and GI tract contrast using TotalSegmentator and ML

Awesome list of tomographic reconstruction (computed tomography, CT / CBCT / micro-CT): papers, toolkits, datasets, benchmarks. Sparse-view, limited-angle, low-dose, metal-artifact, spectral, photon-counting, cone-beam, 4D CT. FBP, iterative solvers, deep unrolling, Radon transform, sinogram. English / Chinese.

SimSiam self-supervised pretraining for three-class liver-tumour classification from CT images. Research snapshot · MICCAI Workshops 2023.

Polychromatic CT simulation and reconstruction in Julia. Runs on Metal, CUDA, ROCm, CPU.

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

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

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.

How much can organ shape alone reveal? Using 1,228 whole-body CTs (16 organs, intensity discarded), we predict sex (AUC 0.87), age (MAE 9.3 yr), and show shape is near-unique per person. Hip bones carry the strongest signal. Shape outperforms intensity for sex — the signal is geometric. Code + reproducible pipeline.

Publication companion for Vision Transformer patch-size research in liver-tumour segmentation. MMMI 2022.

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

Dirichlet VAE trained unsupervised on NIH DeepLesion (32k multi-organ CT patches) to test spontaneous dual-axis disentanglement of organ identity and malignancy. Probe-based evaluation with UMAP, MIG score, and latent traversals. Extension of Keel et al. BMVC 2023.

2.5D U-Net liver-tumour segmentation with patient-level evaluation, ensembling, and compute-performance analysis.

Audited traced surface geometry on PHerc. MAN5, with the gate that decided what counted and an independent CT check

Deep learning pipeline for organ/tumor segmentation in CT/MRI with respiratory motion modeling.

MSc záróvizsga kidolgozás orvosi mérnökinformatika szakirányra

Browser-native CT and segmentation viewer. NIfTI, multiplanar reformatting, volume raycasting and organ surface reconstruction in raw WebGL2, with no plugins and no server.