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Improving Pixel Embedding Learning through Intermediate Distance Regression Supervision for Instance Segmentation
Yuli Wu, Long Chen, Dorit Merhof
As a proposal-free approach, instance segmentation through pixel embedding learning and clustering is gaining more emphasis. Compared with bounding box refinement approaches, such…
AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018
Oliver Rippel, Leon Weninger, Dorit Merhof
Fueled by recent advances in machine learning, there has been tremendous progress in the field of semantic segmentation for the medical image computing community. However, develope…
NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image
Boaz Arad, Radu Timofte, Ohad Ben-Shahar +4
This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. As…
Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection
Oliver Rippel, Patrick Mertens, Dorit Merhof
Anomaly Detection (AD) in images is a fundamental computer vision problem and refers to identifying images and image substructures that deviate significantly from the norm. Popular…
MixNet: Multi-modality Mix Network for Brain Segmentation
Long Chen, Dorit Merhof
Automated brain structure segmentation is important to many clinical quantitative analysis and diagnoses. In this work, we introduce MixNet, a 2D semantic-wise deep convolutional n…
Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints
Long Chen, Martin Strauch, Dorit Merhof
Automatic instance segmentation is a problem that occurs in many biomedical applications. State-of-the-art approaches either perform semantic segmentation or refine object bounding…
A CNN Framenwork Based on Line Annotations for Detecting Nematodes in Microscopic Images
Long Chen, Martin Strauch, Matthias Daub +6
Plant parasitic nematodes cause damage to crop plants on a global scale. Robust detection on image data is a prerequisite for monitoring such nematodes, as well as for many biologi…
An Asymmetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans
Michael Gadermayr, Maximilian Tschuchnig, Laxmi Gupta +4
Generative adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a very high potential in manifold medica…