Predicting Discontinuation of Docetaxel Treatment for Metastatic Castration-Resistant Prostate Cancer (mCRPC)

This repository includes the code for the paper "Detection of Prostate Cancer with Multi-Parametric MRI Utilizing the Anatomic Structure of the Prostate".

Code for the Cancers paper (Functional Linkage of RKIP to the Epithelial to Mesenchymal Transition and Autophagy during the Development of Prostate Cancer)

PCTA web application by Django

Scripts for reproducing the poster: Co-regulation of RKIP and autophagy genes by VEZF1 and ERCC6 in prostate cancer

NGS projects summary

Soft Computing Project by Shoffiyah (140810160057) and Patricia (140810160065).

PAM50 classifier for Prostate Cancer in Python

Prostate lesion classification using Deep Convolutional Neural Networks

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.

My 120th place solution to the PANDA Challenge hosted on Kaggle 🔬

I proved the probabilities of freedom from biochemical recurrence (BCR) among prostate cancer patients are significantly different using stratified Logrank test. I also built a Cox's PH model to identify which genes and demographic factors have effect on survival.

Here I tried various Machine Learning algorithms on different cancer's dataset present in CSV format.

A wrapper containing search algorithm of Forward Selection + Pattern Classifier of KNN to use optimal features in prostate cancer

A package providing MATLAB programming tools for IVIM-DKI analysis with total variation (TV) penalty function.

Fitting of three diffusion models to data acquired using combined T2-DWI.

His study addresses these concerns by predicting prostate cancer using six (6) machine learningtechniques: Random Forest, SVM, KNN, Logistic Regression, Neutral Network, and the Ensemble model. We gathered data from 100 patients who were placed in ten different circumstances. The data was categorised as malignant or non-cancerous. Among the six …

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

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

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