Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping

1 code implementation • 19 Oct 2023 • Joshua Butke, Noriaki Hashimoto, Ichiro Takeuchi, Hiroaki Miyoshi, Koichi Ohshima, Jun Sakuma

Whole-slide image analysis via the means of computational pathology often relies on processing tessellated gigapixel images with only slide-level labels available.

Multiple Instance Learning

End-to-end Multiple Instance Learning for Whole-Slide Cytopathology of Urothelial Carcinoma

1 code implementation • MICCAI Workshop COMPAY 2021 • Joshua Butke, Tatjana Frick, Florian Roghmann, Samir El-Mashtoly, Klaus Gerwert, Axel Mosig

As a non-invasive approach, cytopathology of urine sediment is a highly promising approach to diagnosing urothelial carcinoma.

Multiple Instance Learning whole slide images

Deep Neural Networks for the Correction of Mie Scattering in Fourier-Transformed Infrared Spectra of Biological Samples

no code implementations • 18 Feb 2020 • Arne P. Raulf, Joshua Butke, Lukas Menzen, Claus Küpper, Frederik Großerueschkamp, Klaus Gerwert, Axel Mosig

Infrared spectra obtained from cell or tissue specimen have commonly been observed to involve a significant degree of (resonant) Mie scattering, which often overshadows biochemically relevant spectral information by a non-linear, non-additive spectral component in Fourier transformed infrared (FTIR) spectroscopic measurements.