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Found 15 papers, 4 papers with code
Cross-modality Attention Adapter: A Glioma Segmentation Fine-tuning Method for SAM Using Multimodal Brain MR Images
However, for medical images with small dataset sizes, deep learning methods struggle to achieve better results on real-world image datasets.
Ladder Fine-tuning approach for SAM integrating complementary network
Currently, ongoing research focuses on exploring the effective utilization of these generalized models for specific domains, such as medical imaging.
BEVStereo++: Accurate Depth Estimation in Multi-view 3D Object Detection via Dynamic Temporal Stereo
Bounded by the inherent ambiguity of depth perception, contemporary multi-view 3D object detection methods fall into the performance bottleneck.
Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance
The framework contains two sub-tasks, of which semantic segmentation is the main task and super resolution is an auxiliary task aiding in rebuilding the high frequency information from the LR input.
BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereo
To this end, we introduce an effective temporal stereo method to dynamically select the scale of matching candidates, enable to significantly reduce computation overhead.
Ranked #11 on
3D Object Detection
on nuScenes Camera Only
STS: Surround-view Temporal Stereo for Multi-view 3D Detection
Instead of using a sole monocular depth method, in this work, we propose a novel Surround-view Temporal Stereo (STS) technique that leverages the geometry correspondence between frames across time to facilitate accurate depth learning.
BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection
In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View (BEV) 3D object detection.
Ranked #4 on
3D Object Detection
on Rope3D
Pathological Image Segmentation with Noisy Labels
Segmentation of pathological images is essential for accurate disease diagnosis.
Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models
Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored.
VolumeNet: A Lightweight Parallel Network for Super-Resolution of Medical Volumetric Data
We construct a parallel connection structure based on the group convolution and feature aggregation to build a 3D CNN that is as wide as possible with few parameters.
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors
Deep learning techniques have led to state-of-the-art image super resolution with natural images.
DeepACC:Automate Chromosome Classification based on Metaphase Images using Deep Learning Framework Fused with Prior Knowledge
Previous methods only classify manually segmented single chromosome, which is far from clinical practice.
DeepACEv2: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks
Chromosome enumeration is an essential but tedious procedure in karyotyping analysis.
Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey
Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock complete potentials of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services.
A Unified Knowledge Representation and Context-aware Recommender System in Internet of Things
Within the rapidly developing Internet of Things (IoT), numerous and diverse physical devices, Edge devices, Cloud infrastructure, and their quality of service requirements (QoS), need to be represented within a unified specification in order to enable rapid IoT application development, monitoring, and dynamic reconfiguration.
