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Found 17 papers, 10 papers with code
Regression of Instance Boundary by Aggregated CNN and GCN
In particular, thanks to the proposed aggregation GCN, our network benefits from direct feature learning of the instances’ boundary locations and the spatial information propagation across the image.
Incomplete Modality Disentangled Representation for Ophthalmic Disease Grading and Diagnosis
To address these, we propose an Incomplete Modality Disentangled Representation (IMDR) strategy, which disentangles features into explicit independent modal-common and modal-specific features by guidance of mutual information, distilling informative knowledge and enabling it to reconstruct valuable missing semantics and produce robust multimodal representations.
Self-adaptive vision-language model for 3D segmentation of pulmonary artery and vein
Exploiting the generalization ability of these pre-trained foundation models on downstream tasks, such as segmentation, leads to unexpected performance with a relatively small amount of labeled data.
CathAction: A Benchmark for Endovascular Intervention Understanding
Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions.
Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models
We demonstrate that an attacker can embed a backdoor in LLMs, which, when activated by a specific trigger in the input, manipulates the model's uncertainty without affecting the final output.
CLIP-DR: Textual Knowledge-Guided Diabetic Retinopathy Grading with Ranking-aware Prompting
Diabetic retinopathy (DR) is a complication of diabetes and usually takes decades to reach sight-threatening levels.
Common and Rare Fundus Diseases Identification Using Vision-Language Foundation Model with Knowledge of Over 400 Diseases
Here we introduce RetiZero, a vision-language foundation model that leverages knowledge from over 400 fundus diseases.
Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers?
In this paper, we explore the hypothesis that LLMs process concepts of varying complexities in different layers, introducing the idea of ``Concept Depth'' to suggest that more complex concepts are typically acquired in deeper layers.
Health-LLM: Personalized Retrieval-Augmented Disease Prediction System
Recent advancements in artificial intelligence (AI), especially large language models (LLMs), have significantly advanced healthcare applications and demonstrated potentials in intelligent medical treatment.
The Impact of Reasoning Step Length on Large Language Models
Alternatively, shortening the reasoning steps, even while preserving the key information, significantly diminishes the reasoning abilities of models.
Automatically Segment the Left Atrium and Scars from LGE-MRIs Using a Boundary-focused nnU-Net
Accurate segmentation of the left atrial (LA) and LA scars can provide valuable information to predict treatment outcomes in AF.
Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model
Image segmentation is a fundamental task in the field of imaging and vision.
DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification
Multiple instance learning (MIL) has been increasingly used in the classification of histopathology whole slide images (WSIs).
Ranked #1 on
Multiple Instance Learning
on TCGA
3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs
In this paper, we propose a novel multi-level aggregation network to regress the coordinates of the vertices of a 3D face from a single 2D image in an end-to-end manner.
Counting with Adaptive Auxiliary Learning
This paper proposes an adaptive auxiliary task learning based approach for object counting problems.
BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation
Our model is well-suited to obtain global semantic region information while also accommodates local spatial boundary characteristics simultaneously.
Spatial Uncertainty-Aware Semi-Supervised Crowd Counting
Semi-supervised approaches for crowd counting attract attention, as the fully supervised paradigm is expensive and laborious due to its request for a large number of images of dense crowd scenarios and their annotations.
