Code for paper: DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification [ECCV 2024]

R package that automatically classifies the cells in the scRNA data by segregating non-malignant cells of tumor microenviroment from the malignant cells. It also infers the copy number profile of malignant cells, identifies subclonal structures and analyses the specific and shared alterations of each subpopulation.

Classification of brain tumor in MR images using deep spatiospatial models.

Deep Learning Model that classifies brain tumor from images

The MAMA-MIA Dataset: A Multi-Center Breast Cancer DCE-MRI Public Dataset with Expert Segmentations

Tumor type classification with traditional feature extractions and classifiers.

Deep Learning for Classification of Bone Lesions on Routine MRI

Python toolkit for parsing, processing, and analysis of Illumina methylation array IDAT files

Topological data analysis (TDA) in medical image analysis

This study introduces ARMNet, a sophisticated attention-based model designed to enhance the efficacy of early-stage brain tumor detection through advanced feature extraction methodologies.

In this we trained a model to detect if there is a tumor in the brain image given to the model. Meaning a model for binary class with an accuracy of above 90 for same and cross validation.

Tumor Identification Based on Machine Learning Analysis of Microarray Data

An MRI tumor classification with AI chat bot

Use deep learning to identify ocular tumor types

As part of the Thesis project, an Artificial Intelligence model was trained to classify breast tumors from ultrasound images as benign or malignant. Additionally, a user-friendly web application was created with Python for the backend and ReactJS for the frontend.

scMalignantFinder is a Python package specially designed for analyzing cancer single-cell RNA-seq datasets to distinguish malignant cells from their normal counterparts.

Tumor Prediction Web App

Using Naive Bayes for tumor classification in medical images. Great for healthcare & data science. Python & scikit-learn powered

Student project in TIP at INSA Lyon to detect tumors via a neural network