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Medical Ethics / Professional Responsibility

Latest AI and machine learning research in medical ethics / professional responsibility for healthcare professionals.

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Boundary-Aware Instance Segmentation in Microscopy Imaging

Accurate delineation of individual cells in microscopy videos is essential for studying cellular dyn...

HamVision: Hamiltonian Dynamics as Inductive Bias for Medical Image Analysis

We present HamVision, a framework for medical image analysis that uses the damped harmonic oscillato...

Trust the Unreliability: Inward Backward Dynamic Unreliability Driven Coreset Selection for Medical Image Classification

Efficiently managing and utilizing large-scale medical imaging datasets with limited resources prese...

M2P: Improving Visual Foundation Models with Mask-to-Point Weakly-Supervised Learning for Dense Point Tracking

Tracking Any Point (TAP) has emerged as a fundamental tool for video understanding. Current approach...

When Generative Augmentation Hurts: A Benchmark Study of GAN and Diffusion Models for Bias Correction in AI Classification Systems

Generative models are widely used to compensate for class imbalance in AI training pipelines, yet th...

Understanding the geometry of deep learning with decision boundary volume

For classification tasks, the performance of a deep neural network is determined by the structure of...

TopoCL: Topological Contrastive Learning for Medical Imaging

Contrastive learning (CL) has become a powerful approach for learning representations from unlabeled...

Spatio-Semantic Expert Routing Architecture with Mixture-of-Experts for Referring Image Segmentation

Referring image segmentation aims to produce a pixel-level mask for the image region described by a ...

Multiscale Structure-Guided Latent Diffusion for Multimodal MRI Translation

Although diffusion models have achieved remarkable progress in multi-modal magnetic resonance imagin...

Visually-Guided Controllable Medical Image Generation via Fine-Grained Semantic Disentanglement

Medical image synthesis is crucial for alleviating data scarcity and privacy constraints. However, f...

DCAU-Net: Differential Cross Attention and Channel-Spatial Feature Fusion for Medical Image Segmentation

Accurate medical image segmentation requires effective modeling of both long-range dependencies and ...

Structure and Progress Aware Diffusion for Medical Image Segmentation

Medical image segmentation is crucial for computer-aided diagnosis, which necessitates understanding...

A Semi-Supervised Framework for Breast Ultrasound Segmentation with Training-Free Pseudo-Label Generation and Label Refinement

Semi-supervised learning (SSL) has emerged as a promising paradigm for breast ultrasound (BUS) image...

When AI Levels the Playing Field: Skill Homogenization, Asset Concentration, and Two Regimes of Inequality

Generative AI compresses within-task skill differences while shifting economic value toward concentr...

BEGA-UNet: Boundary-Explicit Guided Attention U-Net with Multi-Scale Feature Aggregation for Colonoscopic Polyp Segmentation

Accurate polyp segmentation from colonoscopy images is critical for colorectal cancer prevention, ye...

Polyp Segmentation Using Wavelet-Based Cross-Band Integration for Enhanced Boundary Representation

Accurate polyp segmentation is essential for early colorectal cancer detection, yet achieving reliab...

Balancing Fidelity, Utility, and Privacy in Synthetic Cardiac MRI Generation: A Comparative Study

Deep learning in cardiac MRI (CMR) is fundamentally constrained by both data scarcity and privacy re...

Gated Differential Linear Attention: A Linear-Time Decoder for High-Fidelity Medical Segmentation

Medical image segmentation requires models that preserve fine anatomical boundaries while remaining ...

The Texture-Shape Dilemma: Boundary-Safe Synthetic Generation for 3D Medical Transformers

Vision Transformers (ViTs) have revolutionized medical image analysis, yet their data-hungry nature ...

GuiDINO: Rethinking Vision Foundation Model in Medical Image Segmentation

Foundation vision models are increasingly adopted in medical image analysis. Due to domain shift, th...

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