Philipp Seeböck

TL;DR: In this paper, CycleGAN was used to reduce the image variability across different OCT devices (Spectralis and Cirrus) by using CycleGAN, an unsupervised unpaired image transformation algorithm.

TL;DR: An unsupervised deep learning architecture particularly designed for OCT representations for unbiased, purely data-driven biomarker discovery is introduced and known as well as novel medical imaging biomarkers without any prior domain knowledge are identified.

TL;DR: In this paper, an in-depth understanding of the mode of action of supervised and unsupervised learning, the functionality of a convolutional neural net (CNN) and various network architectures is needed.

TL;DR: This approach empirically evaluated a Bayesian deep learning based model for segmenting the photoreceptor layer in pathological OCT scans, improving the performance of the baseline U-Net both in terms of the Dice index and the area under the precision/recall curve.

TL;DR: This work presents a fully automated machine learning approach for segmenting hyperreflective foci in spectral-domain optical coherence tomography (SD-OCT) scans and demonstrates that a residual U-Net allows to segment HRF with high accuracy.