Search Results for author:
Found 14 papers, 11 papers with code
A Perspective on Deep Vision Performance with Standard Image and Video Codecs
The use of standardized codecs, such as JPEG or H. 264, is prevalent and required to ensure interoperability.
Differentiable JPEG: The Devil is in the Details
the input image, the JPEG quality, the quantization tables, and the color conversion parameters.
Deep Video Codec Control for Vision Models
To overcome the deterioration of vision performance, this paper presents the first end-to-end learnable deep video codec control that considers both bandwidth constraints and downstream deep vision performance, while adhering to existing standardization.
The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures
In this paper, we introduce the trapped yeast cell (TYC) dataset, a novel dataset for understanding instance-level semantics and motions of cells in microstructures.
An Instance Segmentation Dataset of Yeast Cells in Microstructures
The aim of the dataset and evaluation strategy is to facilitate the development of new cell segmentation approaches.
Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labels
Here, we propose Self-ViT-MIL, a novel approach for classifying and localizing cancerous areas based on slide-level annotations, eliminating the need for pixel-wise annotated training data.
Histopathological Image Classification
Image Classification
+1
Exploring novel algorithms for atrial fibrillation detection by driving graduate level education in medical machine learning
During the lockdown of universities and the COVID-Pandemic most students were restricted to their homes.
Yeast cell segmentation in microstructured environments with deep learning
We give an overview of the datasets recorded for training, validating and testing the networks, as well as a typical use-case.
Scalable 3D Semantic Segmentation for Gun Detection in CT Scans
With the increased availability of 3D data, the need for solutions processing those also increased rapidly.
OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data
Occupancy networks (O-Nets) are an alternative for which the data is represented continuously in a function space and 3D shapes are learned as a continuous decision boundary.
Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy
Time-lapse fluorescent microscopy (TLFM) combined with predictive mathematical modelling is a powerful tool to study the inherently dynamic processes of life on the single-cell level.
Ranked #1 on
TFLM sequence generation
on TLFM dataset
Generation and Simulation of Yeast Microscopy Imagery with Deep Learning
This thesis is a study towards deep learning-based modeling of TLFM experiments on the image level.
Attention-Based Transformers for Instance Segmentation of Cells in Microstructures
For the specific use case, the proposed method surpasses the state-of-the-art tools for semantic segmentation and additionally predicts the individual object instances.
Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning
Cell segmentation is a major bottleneck in extracting quantitative single-cell information from microscopy data.
