Orcun Goksel — accepted AI-conference papers

2022

Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images

Kevin Thandiackal, Boqi Chen, Pushpak Pati, Guillaume Jaume, Drew F. K. Williamson, Maria Gabrani +1

ECCV 2022poster

"Multiple Instance Learning (MIL) methods have become increasingly popular for classifying gigapixel-sized Whole-Slide Images (WSIs) in digital pathology. Most MIL methods operate at a single WSI magnification, by processing all the tissue patches. Such a formulation induces high computational requi…

Cited by 51 Source ↗PDF ↗code ↗Scholar ↗

2021

Quantifying Explainers of Graph Neural Networks in Computational Pathology

Guillaume Jaume, Pushpak Pati, Behzad Bozorgtabar, Antonio Foncubierta, Anna Maria Anniciello, Florinda Feroce +4

CVPR 2021poster

Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by…

Cited by 107 PDF ↗code ↗Scholar ↗

2020

GramGAN: Deep 3D Texture Synthesis From 2D Exemplars

Tiziano Portenier, Siavash Arjomand Bigdeli, Orcun Goksel

NeurIPS 2020poster

We present a novel texture synthesis framework, enabling the generation of infinite, high-quality 3D textures given a 2D exemplar image. Inspired by recent advances in natural texture synthesis, we train deep neural models to generate textures by non-linearly combining learned noise frequencies. To…

Cited by 28 Source ↗PDF ↗Scholar ↗