Awesome artificial intelligence in cancer diagnostics and oncology

prostatecancer.ai is an AI-based, zero-footprint medical image viewer that can identify clinically significant prostate cancer.

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.

An interactive graphical illustration of genetic associations and their biological context

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]".

Train and Predict Cancer Subtype with Keras Model based on Mutational Signatures

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

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

🧠 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.

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

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

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

Prostate lesion classification using Deep Convolutional Neural Networks

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.

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 …

Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition

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

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