Search Results for author:
Found 16 papers, 6 papers with code
Joint tone mapping and denoising of thermal infrared images via multi-scale Retinex and multi-task learning
In this paper, tone mapping algorithms for thermal infrared images with 16 bpp are investigated that can preserve this information.
Comparison of single image HDR reconstruction methods — the caveats of quality assessment
As the problem of reconstructing high dynamic range (HDR) images from a single exposure has attracted much research effort, it is essential to provide a robust protocol and clear guidelines on how to evaluate and compare new methods.
Standalone Neural ODEs with Sensitivity Analysis
This paper presents the Standalone Neural ODE (sNODE), a continuous-depth neural ODE model capable of describing a full deep neural network.
Learning via nonlinear conjugate gradients and depth-varying neural ODEs
The inverse problem of supervised reconstruction of depth-variable (time-dependent) parameters in a neural ordinary differential equation (NODE) is considered, that means finding the weights of a residual network with time continuous layers.
Generalisation effects of predictive uncertainty estimation in deep learning for digital pathology
In this work we evaluate if adding uncertainty estimates for DL predictions in digital pathology could result in increased value for the clinical applications, by boosting the general predictive performance or by detecting mispredictions.
Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications
We bring forward a number of considerations, such as view generation for the contrastive objective and hyper-parameter tuning.
Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection
The scarcity of labeled data is a major bottleneck for developing accurate and robust deep learning-based models for histopathology applications.
How to cheat with metrics in single-image HDR reconstruction
Here, we reproduce a typical evaluation using existing as well as simulated SI-HDR methods to demonstrate how different aspects of the problem affect objective quality metrics.
Ensembles of GANs for synthetic training data generation
This work investigates the use of synthetic images, created by generative adversarial networks (GANs), as the only source of training data.
Unsupervised anomaly detection in digital pathology using GANs
Machine learning (ML) algorithms are optimized for the distribution represented by the training data.
Survey of XAI in digital pathology
We present a survey on XAI within digital pathology, a medical imaging sub-discipline with particular characteristics and needs.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
A Study of Deep Learning Colon Cancer Detection in Limited Data Access Scenarios
One such scenario relates to detecting tumor metastasis in lymph node tissue, where the low ratio of tumor to non-tumor cells makes the diagnostic task hard and time-consuming.
Classifying the classifier: dissecting the weight space of neural networks
of neural network classifiers, and train a large number of models to represent the weight space.
A Closer Look at Domain Shift for Deep Learning in Histopathology
Most centrally, we present a novel measure for evaluating the distance between domains in the context of the learned representation of a particular model.
Single-frame Regularization for Temporally Stable CNNs
The regularization is formulated to account for different types of motion that can occur between frames, so that temporally stable CNNs can be trained without the need for video material or expensive motion estimation.
HDR image reconstruction from a single exposure using deep CNNs
We demonstrate that our approach can reconstruct high-resolution visually convincing HDR results in a wide range of situations, and that it generalizes well to reconstruction of images captured with arbitrary and low-end cameras that use unknown camera response functions and post-processing.
