B-Cos Aligned Transformers Learn Human-Interpretable Features

no code implementations • 16 Jan 2024 • Manuel Tran, Amal Lahiani, Yashin Dicente Cid, Melanie Boxberg, Peter Lienemann, Christian Matek, Sophia J. Wagner, Fabian J. Theis, Eldad Klaiman, Tingying Peng

Vision Transformers (ViTs) and Swin Transformers (Swin) are currently state-of-the-art in computational pathology.

Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images

1 code implementation • 15 Mar 2023 • Ario Sadafi, Oleksandra Adonkina, Ashkan Khakzar, Peter Lienemann, Rudolf Matthias Hehr, Daniel Rueckert, Nassir Navab, Carsten Marr

Explainability is a key requirement for computer-aided diagnosis systems in clinical decision-making.

Decision Making Multiple Instance Learning

Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification

1 code implementation • 1 Jul 2022 • Raheleh Salehi, Ario Sadafi, Armin Gruber, Peter Lienemann, Nassir Navab, Shadi Albarqouni, Carsten Marr

Here, we propose a cross-domain adapted autoencoder to extract features in an unsupervised manner on three different datasets of single white blood cells scanned from peripheral blood smears.

Image Classification