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20152022
most citedUnmasking Clever Hans Predictors and Assessing What Machines Really Learn

1k citations · 1.1k across the 6 of their papers we have counts for

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cs.CV20211 cited

On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy

Vignesh Srinivasan, Nils Strodthoff, Jackie Ma +3

There is an increasing number of medical use-cases where classification algorithms based on deep neural networks reach performance levels that are competitive with human medical ex…

cs.CV2020

SideInfNet: A Deep Neural Network for Semi-Automatic Semantic Segmentation with Side Information

Jing Yu Koh, Duc Thanh Nguyen, Quang-Trung Truong +2

Fully-automatic execution is the ultimate goal for many Computer Vision applications. However, this objective is not always realistic in tasks associated with high failure costs, s…

cs.CV2018

Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

Alexander Binder, Michael Bockmayr, Miriam Hägele +15

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or…

cs.CV2016

Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers

Alexander Binder, Grégoire Montavon, Sebastian Bach +2

Layer-wise relevance propagation is a framework which allows to decompose the prediction of a deep neural network computed over a sample, e.g. an image, down to relevance scores fo…

cs.CV2016

Controlling Explanatory Heatmap Resolution and Semantics via Decomposition Depth

Sebastian Bach, Alexander Binder, Klaus-Robert Müller +1

We present an application of the Layer-wise Relevance Propagation (LRP) algorithm to state of the art deep convolutional neural networks and Fisher Vector classifiers to compare th…