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.

CNN ensemble for prostate cancer Gleason grading

An interactive graphical illustration of genetic associations and their biological context

LUND-PROBE – LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset

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

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

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

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

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

🧠 A deep learning algorithm based on convolutional neural networks to detect glandular cells in digitalized biopsies of the prostate.

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

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

Prostate lesion classification using Deep Convolutional Neural Networks

A model for fully-automated segmentation of healthy organs in PSMA PET/CT images

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 …

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

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

Prostate cancer segmentation using multiparamteric MRI from Pi-CAI challenge dataset