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Found 12 papers, 8 papers with code
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
MedShapeNet is created as an alternative to these commonly used shape benchmarks to facilitate the translation of data-driven vision algorithms to medical applications, and it extends the opportunities to adapt SOTA vision algorithms to solve critical medical problems.
SyNDock: N Rigid Protein Docking via Learnable Group Synchronization
Firstly, SyNDock formulates multimeric protein docking as a problem of learning global transformations to holistically depict the placement of chain units of a complex, enabling a learning-centric solution.
DDP: Diffusion Model for Dense Visual Prediction
We propose a simple, efficient, yet powerful framework for dense visual predictions based on the conditional diffusion pipeline.
Ranked #2 on
Monocular Depth Estimation
on SUN-RGBD
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods.
DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations
AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient.
Multi-frame Collaboration for Effective Endoscopic Video Polyp Detection via Spatial-Temporal Feature Transformation
For example, STFT improves the still image baseline FCOS by 10. 6% and 20. 6% on the comprehensive F1-score of the polyp localization task in CVC-Clinic and ASUMayo datasets, respectively, and outperforms the state-of-the-art video-based method by 3. 6% and 8. 0%, respectively.
Multi-Compound Transformer for Accurate Biomedical Image Segmentation
The recent vision transformer(i. e. for image classification) learns non-local attentive interaction of different patch tokens.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
RANet: Region Attention Network for Semantic Segmentation
In contrast to the previous methods, RANet configures the information pathways between the pixels in different regions, enabling the region interaction to exchange the regional context for enhancing all of the pixels in the image.
UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation
Unlike the recent neural architecture search (NAS) methods that typically searched the optimal operators in each network layer, but missed a good strategy to search for feature aggregations, this paper proposes a novel NAS method for 3D medical image segmentation, named UXNet, which searches both the scale-wise feature aggregation strategies as well as the block-wise operators in the encoder-decoder network.
PRSNet: Part Relation and Selection Network for Bone Age Assessment
These kinds of information can be effectively captured by the relation of different anatomical parts of hand bone.
Multi-Scale Context Intertwining for Semantic Segmentation
Accurate semantic image segmentation requires the joint consideration of local appearance, semantic information, and global scene context.
