4 papers
Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference
Kevin Wilkinghoff, Neelu Madan, Juan Miguel Valverde +6
Anomaly detection aims to identify observations that deviate from expected behavior. Because anomalous events are inherently sparse, most frameworks are trained exclusively on norm…
Towards High-Quality Image Segmentation: Improving Topology Accuracy by Penalizing Neighbor Pixels
Juan Miguel Valverde, Dim P. Papadopoulos, Rasmus Larsen +1
Standard deep learning models for image segmentation cannot guarantee topology accuracy, failing to preserve the correct number of connected components or structures. This, in turn…
TopoMortar: A dataset to evaluate image segmentation methods focused on topology accuracy
Juan Miguel Valverde, Motoya Koga, Nijihiko Otsuka +1
We present TopoMortar, a brick wall dataset that is the first dataset specifically designed to evaluate topology-focused image segmentation methods, such as topology loss functions…
Disconnect to Connect: A Data Augmentation Method for Improving Topology Accuracy in Image Segmentation
Juan Miguel Valverde, Maja Ãstergaard, Adrian Rodriguez-Palomo +4
Accurate segmentation of thin, tubular structures (e.g., blood vessels) is challenging for deep neural networks. These networks classify individual pixels, and even minor misclassi…