14 accepted papers
Fusing histopathology images and genomics data with deep learning has significantly advanced precision oncology. However, genomics data is often missing due to its high acquisition cost and complexity in real-world clinical scenarios. Existing solutions aim to reconstruct genomics data from histopat…
Accurate 3D reconstruction of the right ventricle from multi-view echocardiograms is crucial for the quantitative diagnosis of cardiac diseases. However, existing methods often fail to deliver satisfactory results due to the structural complexity of the right ventricle and the sparsity of non-parall…
Mobile manipulation typically entails the base for mobility, the arm for accurate manipulation, and the camera for perception. The principle of Distant Mobility, Close Grasping(DMCG) is essential for holistic control. We propose Embod-ied Holistic Control for Mobile Manipulation(EHC-MM) with the emb
Deep learning has made remarkable medical image segmentation advancements, yet its generalization capability across tasks remains challenging. The variety of task objectives, disease-dependent labeling variations, and multi-center data contribute to the poor generalization capacity of task-specific…
Gigapixel image analysis, particularly for whole slide images (WSIs), often relies on multiple instance learning (MIL). Under the paradigm of MIL, patch image representations are extracted and then fixed during the training of the MIL classifiers for efficiency consideration. However, the invariance…
In this paper, we tackle the problem of grasping transparent and specular objects. This issue holds importance, yet it remains unsolved within the field of robotics due to failure of recover their accurate geometry by depth cameras. For the first time, we propose ASGrasp, a 6-DoF grasp detection net…
Active learning is considered a viable solution to alleviate the contradiction between the high dependency of deep learning-based segmentation methods on annotated data and the expensive pixel-level annotation cost of medical images. However, most existing methods suffer from unreliable uncertainty…
We present RobotGPT, an innovative decision framework for robotic manipulation that prioritizes stability and safety. The execution code generated by ChatGPT cannot guarantee the stability and safety of the system. ChatGPT may provide different answers for the same task, leading to unpredictability.
In this paper, we investigate a local average sampling and reconstruction problem using the fractional Fourier transform (FRFT). We present certain necessary and sufficient conditions under which there is an average sampling theorem for signals bandlimited in the FRFT domain.
Despite recent advancements in unified adverse weather removal methods, there remains a significant challenge of achieving realistic fine-grained texture and reliable background reconstruction to mitigate serious distortions. Inspired by recent advancements in codebook and vector quantization (VQ…
Varicolored haze caused by chromatic casts poses haze removal and depth estimation challenges. Recent learning-based depth estimation methods are mainly targeted at dehazing first and estimating depth subsequently from haze-free scenes. This way, the inner connections between colored haze and scene…
Although Convolutional Neural Networks (CNN) have made good progress in image restoration, the intrinsic equivalence and locality of convolutions still constrain further improvements in image quality. Recent vision transformer and selfattention have achieved promising results on various computer vis…
In the real world, image degradations caused by rain often exhibit a combination of rain streaks and raindrops, thereby increasing the challenges of recovering the underlying clean image. Note that the rain streaks and raindrops have diverse shapes, sizes, and locations in the captured image, and th…
Convolutional neural networks (CNNs) and their variants have been successfully applied to the electroencephalogram (EEG) based motor imagery (MI) decoding task. However, these CNN-based algorithms generally have limitations in perceiving global temporal dependencies of EEG signals. Besides, they als…