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Found 23 papers, 15 papers with code
Cost-effective Instruction Learning for Pathology Vision and Language Analysis
Here we propose a cost-effective instruction learning framework for conversational pathology named as CLOVER.
Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification
Although explainability is essential in the clinical diagnosis, most deep learning models still function as black boxes without elucidating their decision-making process.
Data-Centric Foundation Models in Computational Healthcare: A Survey
The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare.
Acceleration Estimation of Signal Propagation Path Length Changes for Wireless Sensing
Furthermore, in multi-target scenarios, the fall detection achieves an average true positive rate of 89. 56% and a false positive rate of 11. 78%, demonstrating its importance in enhancing indoor wireless sensing capabilities.
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.
Text-guided Foundation Model Adaptation for Pathological Image Classification
In this paper, we propose to Connect Image and Text Embeddings (CITE) to enhance pathological image classification.
Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction
Survival outcome assessment is challenging and inherently associated with multiple clinical factors (e. g., imaging and genomics biomarkers) in cancer.
AmadeusGPT: a natural language interface for interactive animal behavioral analysis
To overcome the context window limitation, we implement a novel dual-memory mechanism to allow communication between short-term and long-term memory using symbols as context pointers for retrieval and saving.
Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity
Frequent interactions between individuals are a fundamental challenge for pose estimation algorithms.
Ranked #1 on
Animal Pose Estimation
on TriMouse-161
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation
Inspired by the training program of medical radiology residents, we propose a shift towards universal medical image segmentation, a paradigm aiming to build medical image understanding foundation models by leveraging the diversity and commonality across clinical targets, body regions, and imaging modalities.
Rethinking Pose Estimation in Crowds: Overcoming the Detection Information Bottleneck and Ambiguity
Frequent interactions between individuals are a fundamental challenge for pose estimation algorithms.
DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method
Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns.
Multi-animal pose estimation, identification and tracking with DeepLabCut
Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios.
Ranked #4 on
Animal Pose Estimation
on TriMouse-161
TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers
Combining information from multi-view images is crucial to improve the performance and robustness of automated methods for disease diagnosis.
Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images
Top-down instance segmentation framework has shown its superiority in object detection compared to the bottom-up framework.
A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark
Transformers have demonstrated remarkable performance in natural language processing and computer vision.
Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications
Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales.
UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation
In this study, we present UTNet, a simple yet powerful hybrid Transformer architecture that integrates self-attention into a convolutional neural network for enhancing medical image segmentation.
Low-Dose CT Denoising Using a Structure-Preserving Kernel Prediction Network
Low-dose CT has been a key diagnostic imaging modality to reduce the potential risk of radiation overdose to patient health.
Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training
Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance.
Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing
Unlike conventional graph convolution networks always assuming the same node attributes in a global graph, our approach models interdomain information fusion with bipartite graph convolution operation.
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation
Existing domain adaptation methods aim at learning features that can be generalized among domains.
Ranked #3 on
Domain Adaptation
on USPS-to-MNIST
3-D Convolutional Neural Networks for Glioblastoma Segmentation
In this paper, we propose a framework of 3-D fully CNN models for Glioblastoma segmentation from multi-modality MRI data.
