Search Results for author:
Found 17 papers, 6 papers with code
CtrlNeRF: The Generative Neural Radiation Fields for the Controllable Synthesis of High-fidelity 3D-Aware Images
In this paper, we introduce a controllable generative model (i. e. \textbf{CtrlNeRF}) that uses a single MLP network to represent multiple scenes with shared weights.
A Multimodal Vision Foundation Model for Clinical Dermatology
Diagnosing and treating skin diseases require advanced visual skills across domains and the ability to synthesize information from multiple imaging modalities.
Adaptive Transformer Modelling of Density Function for Nonparametric Survival Analysis
Survival analysis holds a crucial role across diverse disciplines, such as economics, engineering and healthcare.
Enhancing Vision-Language Models Generalization via Diversity-Driven Novel Feature Synthesis
To address this, we propose a plug-and-play feature synthesis method called LDFS (Language-Guided Diverse Feature Synthesis) to synthesize new domain features and improve existing CLIP fine-tuning strategies.
Progressive trajectory matching for medical dataset distillation
To solve these barriers, we propose to design a novel progressive trajectory matching strategy to improve the training stability for medical image dataset distillation.
Prompt-driven Latent Domain Generalization for Medical Image Classification
To address these challenges, we propose a novel DG framework for medical image classification without relying on domain labels, called Prompt-driven Latent Domain Generalization (PLDG).
Universal Semi-Supervised Learning for Medical Image Classification
However, in practical scenarios, unlabeled data would be from unseen classes or unseen domains, and it is still challenging to exploit them by existing SSL methods.
EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion Recognition
Concretely, EPVT leverages a set of domain prompts, each of which plays as a domain expert, to capture domain-specific knowledge; and a shared prompt for general knowledge over the entire dataset.
Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision
We introduce a human-in-the-loop framework in the model training process such that users can observe and correct the model's decision logic when confounding behaviors happen.
Skin Lesion Recognition with Class-Hierarchy Regularized Hyperbolic Embeddings
Accordingly, the learned prototypes preserve the semantic class relations in the embedding space and we can predict the label of an image by assigning its feature to the nearest hyperbolic class prototype.
Flexible Sampling for Long-tailed Skin Lesion Classification
To address this, in this paper, we propose a curriculum learning-based framework called Flexible Sampling for the long-tailed skin lesion classification task.
TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack
Existing textual adversarial attacks usually utilize the gradient or prediction confidence to generate adversarial examples, making it hard to be deployed in real-world applications.
Hierarchical Knowledge Guided Learning for Real-world Retinal Diseases Recognition
From a modeling perspective, most deep learning models trained on these datasets may lack the ability to generalize to rare diseases where only a few available samples are presented for training.
Early Melanoma Diagnosis with Sequential Dermoscopic Images
In this study, we propose a framework for automated early melanoma diagnosis using sequential dermoscopic images.
Melanoma Diagnosis with Spatio-Temporal Feature Learning on Sequential Dermoscopic Images
Existing studies for automated melanoma diagnosis are based on single-time point images of lesions.
CNN in CT Image Segmentation: Beyound Loss Function for Expoliting Ground Truth Images
Our insight is that feature maps of two CNNs trained respectively on GT and CT images should be similar on some metric space, because they both are used to describe the same objects for the same purpose.
Communication-Efficient Distributed Estimator for Generalized Linear Models with a Diverging Number of Covariates
Distributed statistical inference has recently attracted immense attention.
