{"product_id":"9783032058690","title":"Fairness of AI in Medical Imaging : Third International Workshop, FAIMI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings (Lecture Notes in Computer Science)","description":"\u003cp\u003eThis book constitutes the refereed proceedings of the Third International Workshop, FAIMI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, in September 23, 2025.\u003c\/p\u003e\u003cp\u003eThe 21 full papers presented in this book were carefully reviewed and selected from 29 submissions.\u003c\/p\u003e\u003cp\u003eFAIMI aimed to raise awareness about potential fairness issues in machine learning within the context of biomedical image analysis.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e \u003cp\u003e.- LTCXNet: Tackling Long-Tailed Multi-Label Classification and Racial\nBias in Chest X-Ray Analysis.\u003c\/p\u003e\u003cp\u003e.- Fairness and Robustness of CLIP-Based Models for Chest X-rays.\u003c\/p\u003e\u003cp\u003e.- ShortCXR: Benchmarking Self-Supervised Learning Methods for\nShortcut Mitigation in Chest X-Ray Interpretation.\u003c\/p\u003e\u003cp\u003e.- How Fair Are Foundation Models? Exploring the Role of Covariate\nBias in Histopathology.\u003c\/p\u003e\u003cp\u003e.- The Cervix in Context: Bias Assessment in Preterm Birth Prediction.\u003c\/p\u003e\u003cp\u003e.- Identifying Gender-Specific Visual Bias Signals in Skin Lesion Classification.\u003c\/p\u003e\u003cp\u003e.- Fairness-Aware Data Augmentation for Cardiac MRI using\nText-Conditioned Diffusion Models.\u003c\/p\u003e\u003cp\u003e.- Exploring the interplay of label bias with subgroup size and separability:\nA case study in mammographic density classification.\u003c\/p\u003e\u003cp\u003e.- Does a Rising Tide Lift All Boats? Bias Mitigation for AI-based CMR\nSegmentation.\u003c\/p\u003e\u003cp\u003e.- MIMM-X: Disentangeling Spurious Correlations for Medical Image\nAnalysis.\u003c\/p\u003e\u003cp\u003e.- Predicting Patient Self-reported Race From Skin Histological Images\nwith Deep Learning.\u003c\/p\u003e\u003cp\u003e.- Robustness and sex differences in skin cancer detection: logistic\nregression vs CNNs.\u003c\/p\u003e\u003cp\u003e.- Sex-based Bias Inherent in the Dice Similarity Coefficient: A Model\nIndependent Analysis for Multiple Anatomical Structures.\u003c\/p\u003e\u003cp\u003e.- The Impact of Skin Tone Label Granularity on the Performance and\nFairness of AI Based Dermatology Image Classification Models.\u003c\/p\u003e\u003cp\u003e.- Causal Representation Learning with Observational Grouping for CXR\nClassification.\u003c\/p\u003e\u003cp\u003e.- Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts\nin MRI-based Alzheimer s Disease Classification.\u003c\/p\u003e\u003cp\u003e.- Fair Dermatological Disease Diagnosis through Auto-weighted\nFederated Learning and Performance-aware Personalization.\u003c\/p\u003e\u003cp\u003e.- Assessing Annotator and Clinician Biases in an Open-Source-Based\nTool Used to Generate Head CT Segmentations for Deep Learning\nTraining.\u003c\/p\u003e\u003cp\u003e.- meval: A Statistical Toolbox for Fine-Grained Model Performance Analysis.\u003c\/p\u003e\u003cp\u003e.- Revisiting the Evaluation Bias Introduced by Frame Sampling\nStrategies in Surgical Video Segmentation Using SAM2.\u003c\/p\u003e\u003cp\u003e.- Disentanglement and Assessment of Shortcuts in Ophthalmological\nRetinal Imaging Exams.\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":42663558283339,"sku":"00000_00000_00000_00000","price":2317.11,"currency_code":"THB","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0641\/0023\/7387\/files\/9783032058690-1.jpg?v=1783500093","url":"https:\/\/kinokuniya.co.th\/ja\/products\/9783032058690","provider":"Books Kinokuniya Thailand","version":"1.0","type":"link"}