Medical Ethics / Professional Responsibility

Combining Self-attention and Dilation Convolutional for Semantic Segmentation of Coal Maceral Groups

The segmentation of coal maceral groups can be described as a semantic segmentation process of coal maceral group images, which is of great significance for studying the chemical properties of coal. Generally, existing semantic segmentation models of coal maceral groups use the method of stacking parameters to achieve higher accuracy. It leads to increased computational requirements and impacts ...

Semantic Localization Guiding Segment Anything Model For Reference Remote Sensing Image Segmentation

The Reference Remote Sensing Image Segmentation (RRSIS) task generates segmentation masks for specified objects in images based on textual descriptions, which has attracted widespread attention and research interest. Current RRSIS methods rely on multi-modal fusion backbones and semantic segmentation heads but face challenges like dense annotation requirements and complex scene interpretation. T...

SAFEFLOW: A Principled Protocol for Trustworthy and Transactional Autonomous Agent Systems

Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled powerful autonomous agents capable of complex reasoni...

C3S3: Complementary Competition and Contrastive Selection for Semi-Supervised Medical Image Segmentation

For the immanent challenge of insufficiently annotated samples in the medical field, semi-supervised medical image segmentation (SSMIS) offers a pro...

Assessing parameter identifiability of a hemodynamics PDE model using spectral surrogates and dimension reduction

Computational inverse problems for biomedical simulators suffer from limited data and relatively high parameter dimensionality. This often requires ...

Boundary-Enhanced $U^{2}$-Net for Simultaneous Four-Chamber Segmentation in Transthoracic Echocardiography.

The heart, responsible for circulating blood throughout our body, contains four chambers. Existing analysis methods primarily focus on one single vent...

Development and Validation of a Scale for Nurses' Ethical Awareness in The Use of Artificial Intelligence: A Methodological Study.

The integration of artificial intelligence in nursing practice presents significant ethical challenges that require a comprehensive assessment framewo...

Boosting polyp screening with improved point-teacher weakly semi-supervised.

Polyps, like a silent time bomb in the gut, are always lurking and can explode into deadly colorectal cancer at any time. Many methods are attempted t...

TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching

Tumor data synthesis offers a promising solution to the shortage of annotated medical datasets. However, current approaches either limit tumor diver...

Trajectory Generator Matching for Time Series

Accurately modeling time-continuous stochastic processes from irregular observations remains a significant challenge. In this paper, we leverage ide...

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation

Few-Shot Medical Image Segmentation (FSMIS) has been widely used to train a model that can perform segmentation from only a few annotated images. Ho...

Privacy-Preserving Chest X-ray Report Generation via Multimodal Federated Learning with ViT and GPT-2

The automated generation of radiology reports from chest X-ray images holds significant promise in enhancing diagnostic workflows while preserving p...

Occlusion Boundary and Depth: Mutual Enhancement via Multi-Task Learning

Occlusion Boundary Estimation (OBE) identifies boundaries arising from both inter-object occlusions and self-occlusion within individual objects, di...

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization

RGB-D scene parsing methods effectively capture both semantic and geometric features of the environment, demonstrating great potential under challen...

JailBound: Jailbreaking Internal Safety Boundaries of Vision-Language Models

Vision-Language Models (VLMs) exhibit impressive performance, yet the integration of powerful vision encoders has significantly broadened their atta...

CENet: Context Enhancement Network for Medical Image Segmentation

Medical image segmentation, particularly in multi-domain scenarios, requires precise preservation of anatomical structures across diverse representa...

Understanding and Mitigating Overrefusal in LLMs from an Unveiling Perspective of Safety Decision Boundary

Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet they often refuse to answer legitimate quer...

SAMba-UNet: Synergizing SAM2 and Mamba in UNet with Heterogeneous Aggregation for Cardiac MRI Segmentation

To address the challenge of complex pathological feature extraction in automated cardiac MRI segmentation, this study proposes an innovative dual-en...

P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation

Perturbation with diverse unlabeled data has proven beneficial for semi-supervised medical image segmentation (SSMIS). While many works have success...

Beyond Empathy: Integrating Diagnostic and Therapeutic Reasoning with Large Language Models for Mental Health Counseling

Large language models (LLMs) hold significant potential for mental health support, capable of generating empathetic responses and simulating therape...