pathmnist

Based on our paper "Implementing vision transformer for classifying 2D biomedical images" published in Scientific Reports (Nature)

🔍 Enhance medical imaging with a lightweight CNN model that offers over 91% accuracy and integrated explainability for better clinical trust.

Medical Image Synthesis project (MedSyn). In-depth evaluation of the efffects of different synthesis models (i.e., CFG ccDDPM) for medical image synthesis for class balancing on image datasets (i.e., PathMNIST).

A lightweight Explainable AI CNN for PathMNIST medical imaging, achieving 91%+ accuracy with Integrated Gradients and SQLite-based attribution storage. Built in PyTorch, this scalable model delivers high performance, transparency, and real-world readiness, making it ideal for medical AI, edge deployment, and explainable deep learning research.

This script focuses on explaining a pre-trained CNN’s predictions on the MedMNIST dataset using Deep SHAP. Shapley values are computed for each pixel, summed to create a single Shapley score per image, and saved alongside labels in CSV files for interpretability in medical image classification.

Improving PathMNIST histopathology classification with cancer-focused specialist models and recall-oriented evaluation.

Class-wise Reliability-Aware Distillation for Decentralized Heterogeneous Federated Learning

A comparative study and implementation of five autoencoder architectures (Basic, CNN, Denoising, VAE, Sparse) for medical image reconstruction and feature extraction using the PathMNIST dataset.

Reproducible PathMNIST reliability benchmark: imbalance, calibration, external-centre stress tests, failure slices, and explanations.

IRSANet Case Study: Patch-level classification of histopathological colon tissue images

Étude académique : classification histologique, calibration des probabilités et abstention sur PathMNIST. Scikit-learn, PyTorch et Streamlit.

PathMNIST tissue classification: interpretable colour/texture features versus a small CNN, with fixed splits and visual error analysis.

cnn built from scratch in pytorch, with a robustness eval and label-free MMD shift detection.

CNN, MLP and Random Forest comparison for PathMNIST histopathology image classification.

PyTorch experiments for PathMNIST classification and MNIST knowledge distillation using compact CNNs and ResNet18.