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.

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

Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.

To Detect and Classify Brain Tumors using CNN and ANN as an asset of Deep Learning and to examine the position of the tumor.

PyTorch implementation for Camelyon17 (Breast Tumor Classification)

Brain Tomur Classification Using Pre-trained Models

3d convnet for the classification of nodules/tumor in lung CT scans. Trained on Luna16 for Kaggle's 2017 data science bowl competition (result in top 3%)

Brain Tumor Classification Using Deep Learning

This repository is part of the Brain Tumor Classification Project. The repo contains the unaugmented dataset used for the project

sc-MTOP is an analysis framework based on deep learning and computational pathology. This framework aims to characterize the tumor ecosystem diversity at the single-cell level. This code provide 1) Hover-Net-based nuclear segmentation and classification; 2) Nuclear morphological and texture feature extraction; 3) Multi-level pairwise nuclear graph construction and spatial topological feature extraction.

Refining the Accuracy and Efficiency to classify brain tumor images into malignant and benign using Matlab

Multi-attention Guided Multi-task Learning Network for Automatic Gastric Tumor Segmentation and Lymph Node Classification

Classification of brain tumors using MRI images based on pretrained models

Multi-task learning for segmentation and classification of breast tumors from ultrasound images

Deep Learning Based Tumor Type Classification Using Gene Expression Data

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

A Multitask Deep Learning Model for Simultaneous Detection, Segmentation and Classification of Bone Tumors on Radiographs

Brain tumor classification on structural MR images of BraTS dataset based on 3D Multi-Scale Convolutional Neural Network, which is a part of my master thesis project.

Deep Learning Model that classifies brain tumor from images

No description available

This project contains the production ready Machine Learning(Deep Learning) solution for detecting and classifying the brain tumor in medical images

Tumor classification. TF Slim implementation of Camelyon 16 challenge (WIP)

Implementation of SVM Classifier To Perform Classification on the dataset of Breast Cancer Wisconin; to predict if the tumor is cancer or not.

This is a deep learning project for automated brain tumor detection and classification using MRI imaging.Binary Classification: Detecting presence/absence of tumors (88.77% accuracy) Multiclass Classification: Classifying among 4 tumor types (78.73% accuracy)

Brain Tumor Segmentation And Classification using artificial intelligence

MethyLYZR: a Naïve Bayesian framework for rapid brain tumor classification from sparse epigenomic data

brain_tumor_mri_classification_TensorFlow & Eager_cnn

No description available

A novel segmentation-to-classification scheme for breast ultrasound image classification