PAM50 classifier for Prostate Cancer in Python

ProLesA-Net: a Deep learning model For Prostate Lesion Segmentation from bi-parametric MR-Images

PCTA web application by Django

Keras/Tensorflow implementation for co-generation and segmentation of surgical instruments using unlabelled robot-assisted surgery data.

Pytorch implementation of carcino-net

[MICCAI'24] Incorporating Clinical Guidelines through Adapting Multi-modal Large Language Model for Prostate Cancer PI-RADS Scoring

Using diffusion basis spectrum imaging (DBSI) and multi layer perceptron (MLP) for prostate cancer (PCa) prediction.

Keras/Tensorflow implementation of TP-GAN (end-to-end automatic approach for treatment planning in low-dose-rate prostate brachytherapy)

Fully supervised, healthy/malignant prostate detection in multi-parametric MRI (T2W, DWI, ADC), using a modified 2D RetinaNet model for medical object detection, built upon a shallow SEResNet backbone.

Public release of ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans

🧠 A deep learning algorithm based on convolutional neural networks to detect glandular cells in digitalized biopsies of the prostate. Performed as bachelor thesis for the degree in computer engineering.

Keras/Tensorflow implementation of 3D pix2pix for automating seed planning for prostate brachytherapy

Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center Study

TensorFlow implementation of our paper: "Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging [Medical Physics 2021]".

Hierarchical probabilistic 3D U-Net, with attention mechanisms (—𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘜-𝘕𝘦𝘵, 𝘚𝘌𝘙𝘦𝘴𝘕𝘦𝘵) and a nested decoder structure with deep supervision (—𝘜𝘕𝘦𝘵++). Built in TensorFlow 2.5. Configured for voxel-level clinically significant prostate cancer detection in multi-channel 3D bpMRI scans.