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interpretAI_DigiPath
Hands-on Sessions 1 and 2 at the Building Interpretable AI for Digital Pathology AMLD workshop 2021concept_discovery_svd
Automatic identification of regions in the latent space of a model that correspond to unique concepts, namely to concepts with a semantically distinct meaning.interpretableWSItoRNAseq
An interpretable approach based on trainable attention that identifies which regions in H&E slides of colorectal cancer are the most informative about RNA transcriptomicsConceptAttribution
With this library you will be able to apply concept attribution to your task. You will find the functions to compute concept measures on your data, to learn the regression concept vectors and to generate concept based explanations.miccaihackathon_shifts
XAI_evaluation
This repository contains the code and results of the paper "Evaluation and Comparison of CNN Visual Explanations for Histopathology", to be presented at the XAI Workshop at AAAI-21.InterpretabilityVISUM22
Hands-on session on Interpretable AI at the VISUM Summer School 2022multitask_adversarial
Repository for our work on multi task adversarial CNNsmedgift-VisBreastHist
Visual interpretability for patch-based classification of breast cancer histopathology images. (in review)intentionally_flawed_models
This repository contains the scripts to replicate the experiments in Interpreting Intentionally Flawed Models with Linear Probescam-4M17-normApproximation
IMVIP2019
This reporitory contains the code for replicating the experiments in "Visualizing and interpreting feature reuse of pretrained CNNs for histopathology", submitted as a short abstract at IMVIP2019.cam-kaggle-ML-major
maragraziani.github.io
Personal Websitecam-4M17
medgift-KerasAugmentation
Improved Data Augmentation for CNN training with keras.tdd-bdd-final-project
Final project IBM introduction to TDD/BDD courseraintro-interpretableAI
Repository of the main source code for the assignments of the "Introduction to interpretable AI" courseLove Open Source and this site? Check out how you can help us