output
20202024
most citedDermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

176 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

5 papers

stat.ME2024★ 3 cited

DGP-LVM: Derivative Gaussian process latent variable models

Soham Mukherjee, Manfred Claassen, Paul-Christian Bürkner

We develop a framework for derivative Gaussian process latent variable models (DGP-LVMs) that can handle multi-dimensional output data using modified derivative covariance function…

cs.CY2024★ 14 cited

Temporal and Between-Group Variability in College Dropout Prediction

Dominik Glandorf, Hye Rin Lee, Gabe Avakian Orona +3

Large-scale administrative data is a common input in early warning systems for college dropout in higher education. Still, the terminology and methodology vary significantly across…

q-bio.QM2023★ 176 cited

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Tirtha Chanda, Katja Hauser, Sarah Hobelsberger +32

Although artificial intelligence (AI) systems have been shown to improve the accuracy of initial melanoma diagnosis, the lack of transparency in how these systems identify melanoma…

q-bio.QM2021★ 5 cited

Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder

Quentin Garrido, Sebastian Damrich, Alexander Jäger +4

Motivation: Single cell RNA sequencing (scRNA-seq) data makes studying the development of cells possible at unparalleled resolution. Given that many cellular differentiation proces…

eess.IV2020★ 51 cited

Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation

Karim Armanious, Sherif Abdulatif, Wenbin Shi +6

The concept of biological age (BA), although important in clinical practice, is hard to grasp mainly due to the lack of a clearly defined reference standard. For specific applicati…