Cover Image

Who Does Your Algorithm Fail? Investigating Age and Ethnic Bias in the MAMA-MIA Dataset

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Parikh, Aditya, Das, Sneha, Feragen, Aasa
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Investigating Label Bias and Representational Sources of Age-Related Disparities in Medical Segmentation
by: Parikh, Aditya, et al.
Published: (2025)

Fair Lung Disease Diagnosis from Chest CT via Gender-Adversarial Attention Multiple Instance Learning
by: Parikh, Aditya, et al.
Published: (2026)

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation
by: Parikh, Aditya, et al.
Published: (2026)

Measuring What VLMs Don't Say: Validation Metrics Hide Clinical Terminology Erasure in Radiology Report Generation
by: Parikh, Aditya, et al.
Published: (2026)

Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methods
by: Olesen, Vincent, et al.
Published: (2024)

Patronus: Interpretable Diffusion Models with Prototypes
by: Weng, Nina, et al.
Published: (2025)

Explainable fetal ultrasound quality assessment with progressive concept bottleneck models
by: Lin, Manxi, et al.
Published: (2022)

The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction
by: Garrucho, Lidia, et al.
Published: (2026)

Are generative models fair? A study of racial bias in dermatological image generation
by: López-Pérez, Miguel, et al.
Published: (2025)

Navigating Uncertainty in Medical Image Segmentation
by: Zepf, Kilian, et al.
Published: (2024)

MIA-Mind: A Multidimensional Interactive Attention Mechanism Based on MindSpore
by: Qin, Zhenkai, et al.
Published: (2025)

Informed Deep Abstaining Classifier: Investigating noise-robust training for diagnostic decision support systems
by: Schneider, Helen, et al.
Published: (2024)

DistractMIA: Black-Box Membership Inference on Vision-Language Models via Semantic Distraction
by: Tang, Hongyi, et al.
Published: (2026)

MIA-DPO: Multi-Image Augmented Direct Preference Optimization For Large Vision-Language Models
by: Liu, Ziyu, et al.
Published: (2024)

General Methods Make Great Domain-specific Foundation Models: A Case-study on Fetal Ultrasound
by: Ambsdorf, Jakob, et al.
Published: (2025)

When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generators
by: Adamkiewicz, Krzysztof, et al.
Published: (2026)

Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data
by: Korshunov, Pavel, et al.
Published: (2025)

Your Image Generator Is Your New Private Dataset
by: Resmini, Nicolo, et al.
Published: (2025)

OpenLVLM-MIA: A Controlled Benchmark Revealing the Limits of Membership Inference Attacks on Large Vision-Language Models
by: Miyamoto, Ryoto, et al.
Published: (2025)

Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation
by: Weng, Nina, et al.
Published: (2023)

Auditing Facial Emotion Recognition Datasets for Posed Expressions and Racial Bias
by: Khan, Rina, et al.
Published: (2025)

ConceptScope: Characterizing Dataset Bias via Disentangled Visual Concepts
by: Choi, Jinho, et al.
Published: (2025)

IDD-X: A Multi-View Dataset for Ego-relative Important Object Localization and Explanation in Dense and Unstructured Traffic
by: Parikh, Chirag, et al.
Published: (2024)

Modeling Saliency Dataset Bias
by: Kümmerer, Matthias, et al.
Published: (2025)

Mitigating Bias in Dataset Distillation
by: Cui, Justin, et al.
Published: (2024)

Auditing and Mitigating Bias in Gender Classification Algorithms: A Data-Centric Approach
by: Bahiru, Tadesse K, et al.
Published: (2025)

Revealing the Underlying Patterns: Investigating Dataset Similarity, Performance, and Generalization
by: Achara, Akshit, et al.
Published: (2023)

The Point, the Vision and the Text: Does Point Cloud Boost Spatial Reasoning of Large Language Models? A Bias-Controlled Study
by: Zhang, Weichen, et al.
Published: (2025)

Solar Panel Segmentation :Self-Supervised Learning Solutions for Imperfect Datasets
by: Sagaram, Sankarshanaa, et al.
Published: (2024)

FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation
by: Velioglu, Riza, et al.
Published: (2024)

Content Bias in Deep Learning Image Age Approximation: A new Approach Towards better Explainability
by: Jöchl, Robert, et al.
Published: (2023)

Texture-AD: An Anomaly Detection Dataset and Benchmark for Real Algorithm Development
by: Lei, Tianwu, et al.
Published: (2024)

IndicFairFace: Balanced Indian Face Dataset for Auditing and Mitigating Geographical Bias in Vision-Language Models
by: Mohsin, Aarish Shah, et al.
Published: (2026)

When Alignment Fails: Multimodal Adversarial Attacks on Vision-Language-Action Models
by: Yan, Yuping, et al.
Published: (2025)

Better Eyes, Better Thoughts: Why Vision Chain-of-Thought Fails in Medicine
by: Wu, Yuan, et al.
Published: (2026)

Vision language models are blind: Failing to translate detailed visual features into words
by: Rahmanzadehgervi, Pooyan, et al.
Published: (2024)

VLAgeBench: Benchmarking Large Vision-Language Models for Zero-Shot Human Age Estimation
by: Sajib, Rakib Hossain, et al.
Published: (2026)

Who Evaluates the Evaluations? Objectively Scoring Text-to-Image Prompt Coherence Metrics with T2IScoreScore (TS2)
by: Saxon, Michael, et al.
Published: (2024)

Doodle Your 3D: From Abstract Freehand Sketches to Precise 3D Shapes
by: Bandyopadhyay, Hmrishav, et al.
Published: (2023)

How Do Medical MLLMs Fail? A Study on Visual Grounding in Medical Images
by: Liu, Guimeng, et al.
Published: (2026)