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.

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]".

🧠 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.

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

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

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

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

PAM50 classifier for Prostate Cancer in Python

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

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

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

Pytorch implementation of carcino-net

PCTA web application by Django