DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image

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

Code for the paper " PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers "

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

simple pytorch unet model for brain tumor detection on MRI tiff images

This repo for the paper titled "SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification"

implementation of Tensorflow Unet brain tumor segmentation and detection enhanced with attention model on nii datasets

An effective deep learning classification framework for whole slide images.

Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter (IJCNN 2020)

🧠 TumorClassifier-RAW-vs-DIP – an advanced 🔬 medical imaging platform 🏥 with 🤖 AI-powered brain tumor classification 🧬 (MRI analysis 📊), 🔄 image preprocessing pipeline (RAW vs DIP comparison 📈), ⚡ Linear SVM classifier 🎯 for tumor detection, 📊 performance metrics visualization 📉, interactive web 🌐 interface for real-time predictions.

This Python script uses TensorFlow to build, train, and evaluate a neural network for breast cancer diagnosis. It processes a dataset (cancer.csv), splits it into training and testing sets, and defines a sequential model with three sigmoid-activated dense layers. Users can train the model or evaluate it via an interactive command-line interface.

🧠 MRI-ViT DL System – an medical analysis platform with 🤖 Vision-Transformer (Tumor/No Tumor detection 🔍), 🖼️ automated image processing (CLAHE/Skull-stripping/Denoising 🛠️), 🎨 modern web interface (React/TypeScript/Vite ⚛️), 📊 real-time confidence metrics 📈 and ⚡ FastAPI backend (PyTorch/timm/OpenCV 🐍) for precise brain tumor diagnosis🧩.

GAN-augmented brain tumor detection using ResNet50 — implementation of IET book chapter

Beyin tümörlerini tespit etmek için derin öğrenme modeli. Python, TensorFlow, Keras ve diğer kütüphaneleri kullanarak geliştirildi.

Procesamiento, análisis y extracción de características de imágenes biomédicas.

Hybrid Quantum–Classical Neural Network (QCNN) for automated brain tumour detection using MRI images. Combines EfficientNet-B0 feature extraction with a 4-qubit PennyLane quantum layer and includes a Gradio-based prediction interface.

Keras, Tensorflow, CNN(convolutional neural network),

Brain Tumor Classification using Deep Learning (CNN, Autoencoder, GAN) — A comparative study of architectures for detecting Glioma, Meningioma, Pituitary, and No Tumor MRI scans.

Brain tumor detection and classification using deep learning. Implements object detection and classification models for brain MRI scans analysis.

A simple program to detect brain tumors from images using image processing