Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging
Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning models requires...
Less is More: Selective Reduction of CT Data for Self-Supervised Pre-Training of Deep Learning Models with Contrastive Learning Improves Downstream Classification Performance
Self-supervised pre-training of deep learning models with contrastive learning is a widely used technique in image analysis. Current findings indicate a strong potential for contrastive...
Supervisory model predictive control for PV battery and heat pump system with phase change slurry thermal storage
We present the design, implementation and experimental validation of a supervisory predictive control approach for an electrical heating system featuring a phase change slurry as heat storage and...
Find my mug: Efficient object search with a mobile robot using semantic segmentation
In this paper, we propose an efficient semantic segmentation framework for indoor scenes, tailored to the application on a mobile robot. Semantic segmentation can help robots to gain a reasonable...
When Vision-Language Models Look but Don't See: Anatomical Bias in Endoscopic Spatial Reasoning
Multiple Trajectory Prediction with Deep Temporal and Spatial Convolutional Neural Networks
Identification and Reconstruction of Complete Gait Cycles for Person Identification in Crowded Scenes
Abstract: Self-supervised Pre-training for Dealing with Small Datasets in Deep Learning for Medical Imaging - Evaluation of Contrastive and Masked Autoencoder Methods
Abstract: Your other Left! Vision-language Models Fail to Understand Relative Positions in Medical Images
Abstract: Selective Reduction of CT Data for Self-supervised Pre-training Improves Downstream Classification Performance
Learning and Detecting Objects with a Mobile Robot to Assist Older Adults in Their Homes
Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images
Fast semantic segmentation of 3D point clouds using a dense CRF with learned parameters
Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images
Clinical decision-making relies heavily on understanding relative positions of anatomical structures and anomalies. Therefore, for Vision-Language Models (VLMs) to be applicable in clinical...