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Found 18 papers, 3 papers with code
Beyond H&E: Unlocking Pathological Insights with Polarization via Self-supervised Learning
Our approach employs a feature decomposition strategy to disentangle common and modality specific features, ensuring effective multimodal representation learning.
Diffusion-based Virtual Staining from Polarimetric Mueller Matrix Imaging
However, existing methods for polarization-based virtual staining are still in the early stage, and the diffusion-based model, which has shown great potential in enhancing the fidelity of the generated images, has not been studied yet.
CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation
Unlike previous works that directly repurpose VLM for action prediction by simple action quantization, we propose a omponentized VLA architecture that has a specialized action module conditioned on VLM output.
Adversarial Prompt Distillation for Vision-Language Models
In this work, we propose a novel method called Adversarial Prompt Distillation (APD) that combines APT with knowledge distillation to boost the adversarial robustness of CLIP.
BlueSuffix: Reinforced Blue Teaming for Vision-Language Models Against Jailbreak Attacks
In this work, we focus on black-box defense for VLMs against jailbreak attacks.
PathInsight: Instruction Tuning of Multimodal Datasets and Models for Intelligence Assisted Diagnosis in Histopathology
We conducted a qualitative assessment of the capabilities of the base model and the fine-tuned model in performing image captioning and classification tasks on the specific dataset.
EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders
However, conveying AI fairness metrics to stakeholders without AI expertise, capturing their personal preferences, and seeking a collective consensus remain challenging and underexplored.
MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis
We further scale up the MiM to large pre-training datasets with more than 10k volumes, showing that large-scale pre-training can further enhance the performance of downstream tasks.
Rethinking Generative Large Language Model Evaluation for Semantic Comprehension
Through a comprehensive evaluation of 24 models across 11 benchmarks, we highlight several potential drawbacks of MCQA, for instance, the inconsistency between the MCQA evaluation and the generation of open-ended responses in practical scenarios.
When Visual Grounding Meets Gigapixel-level Large-scale Scenes: Benchmark and Approach
Visual grounding refers to the process of associating natural language expressions with corresponding regions within an image.
Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI
In order to investigate this approach, we conducted two studies of a complex AI model for credit rating used in loan applications.
SCAAT: Improving Neural Network Interpretability via Saliency Constrained Adaptive Adversarial Training
Deep Neural Networks (DNNs) are expected to provide explanation for users to understand their black-box predictions.
Assessing and Enhancing Robustness of Deep Learning Models with Corruption Emulation in Digital Pathology
Deep learning in digital pathology brings intelligence and automation as substantial enhancements to pathological analysis, the gold standard of clinical diagnosis.
What a Whole Slide Image Can Tell? Subtype-guided Masked Transformer for Pathological Image Captioning
Pathological captioning of Whole Slide Images (WSIs), though is essential in computer-aided pathological diagnosis, has rarely been studied due to the limitations in datasets and model training efficacy.
AGMDT: Virtual Staining of Renal Histology Images with Adjacency-Guided Multi-Domain Transfer
Renal pathology, as the gold standard of kidney disease diagnosis, requires doctors to analyze a series of tissue slices stained by H&E staining and special staining like Masson, PASM, and PAS, respectively.
Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation
Since pseudo labels are noisy and unreliable, these methods suffer from catastrophic forgetting and error accumulation when dealing with dynamic data distributions.
AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection
This paper proposed a novel anomaly detection (AD) approach of High-speed Train images based on convolutional neural networks and the Vision Transformer.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
