RadarSLAM: Radar based Large-Scale SLAM in All Weathers

Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been presented in last decade using different sensor modalities. However, robust SLAM in extreme weather conditions is still an open research problem. In this paper, RadarSLAM, a full radar based graph SLAM system, is proposed for reliable localization and mapping in large-scale environments. It is composed of pose tracking, local mapping, loop closure detection and pose graph optimization, enhanced by novel feature matching and probabilistic point cloud generation on radar images. Extensive experiments are conducted on a public radar dataset and several self-collected radar sequences, demonstrating the state-of-the-art reliability and localization accuracy in various adverse weather conditions, such as dark night, dense fog and heavy snowfall

How good is good enough? Strategies for dealing with unreliable segmentation annotations of medical data

Medical image segmentation is an essential topic in computer vision and medical image analysis, because it enables the precise and accurate segmentation of organs and lesions for healthcare applications. Deep learning has dominated in medical image segmentation due to increasingly powerful computational resources, successful neural network architecture engineering, and access to large amounts of medical imaging data with high-quality annotations. However, annotating medical imaging data is time-consuming and expensive, and sometimes the annotations are unreliable. This DPhil thesis presents a comprehensive study that explores deep learning techniques in medical image segmentation under various challenging situations of unreliable medical imaging data. These situations include: (1) conventional supervised learning to tackle comprehensive data annotation with full dense masks, (2) semi-supervised learning to tackle partial data annotation with full dense masks, (3) noise-robust learning to tackle comprehensive data annotation with noisy dense masks, and (4) weakly-supervised learning to tackle comprehensive data annotation with sketchy contours for network training. The proposed medical image segmentation strategies improve deep learning techniques to effectively address a series of challenges in medical image analysis, including limited annotated data, noisy annotations, and sparse annotations. These advancements aim to bring deep learning techniques of medical image analysis into practical clinical scenarios. By overcoming these challenges, the strategies establish a more robust and reliable application of deep learning methods which is valuable for improving diagnostic precision and patient care outcomes in real-world clinical environments

Weakly supervised medical image segmentation through dense combinations of dense pseudo-l-abels

Annotating a large amount of medical imaging data thoroughly for training purposes can be expensive, particularly for medical image segmentation tasks; whereas obtaining scribbles, a less precise form of annotation, is more feasible for clinicians. Nevertheless, training semantic segmentation networks with limited-signal supervision remains a technical challenge. In this paper, we present an innovative scribble-supervised image segmentation via densely ensembling dense pseudos called Collaborative Hybrid Networks(CHNets), which consists of groups of CNN- and ViT-based segmentation networks. A simple yet efficient densely collaboration scheme is introduced to ensemble dense pseudo label to expand dataset allowing full-signal supervision. Additionally, internal consistency and external consistency training among networks are proposed to ensure that each network is beneficial to the other, resulting in a significant improvement. Our experiments on a public MRI benchmark dataset demonstrate that our proposed approach outperforms other weakly-supervised methods on various metrics