{"product_id":"fairness-of-ai-in-medical-imaging-9783032058690","title":"Fairness of AI in Medical Imaging","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e.- LTCXNet: Tackling Long-Tailed Multi-Label Classification and Racial\u003cbr\u003eBias 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\u003cbr\u003eShortcut Mitigation in Chest X-Ray Interpretation.\u003c\/p\u003e\u003cp\u003e.- How Fair Are Foundation Models? Exploring the Role of Covariate\u003cbr\u003eBias 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\u003cbr\u003eText-Conditioned Diffusion Models.\u003c\/p\u003e\u003cp\u003e.- Exploring the interplay of label bias with subgroup size and separability:\u003cbr\u003eA case study in mammographic density classification.\u003c\/p\u003e\u003cp\u003e.- Does a Rising Tide Lift All Boats? Bias Mitigation for AI-based CMR\u003cbr\u003eSegmentation.\u003c\/p\u003e\u003cp\u003e.- MIMM-X: Disentangeling Spurious Correlations for Medical Image\u003cbr\u003eAnalysis.\u003c\/p\u003e\u003cp\u003e.- Predicting Patient Self-reported Race From Skin Histological Images\u003cbr\u003ewith Deep Learning.\u003c\/p\u003e\u003cp\u003e.- Robustness and sex differences in skin cancer detection: logistic\u003cbr\u003eregression vs CNNs.\u003c\/p\u003e\u003cp\u003e.- Sex-based Bias Inherent in the Dice Similarity Coefficient: A Model\u003cbr\u003eIndependent Analysis for Multiple Anatomical Structures.\u003c\/p\u003e\u003cp\u003e.- The Impact of Skin Tone Label Granularity on the Performance and\u003cbr\u003eFairness of AI Based Dermatology Image Classification Models.\u003c\/p\u003e\u003cp\u003e.- Causal Representation Learning with Observational Grouping for CXR\u003cbr\u003eClassification.\u003c\/p\u003e\u003cp\u003e.- Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts\u003cbr\u003ein MRI-based Alzheimer’s Disease Classification.\u003c\/p\u003e\u003cp\u003e.- Fair Dermatological Disease Diagnosis through Auto-weighted\u003cbr\u003eFederated Learning and Performance-aware Personalization.\u003c\/p\u003e\u003cp\u003e.- Assessing Annotator and Clinician Biases in an Open-Source-Based\u003cbr\u003eTool Used to Generate Head CT Segmentations for Deep Learning\u003cbr\u003eTraining.\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\u003cbr\u003eStrategies in Surgical Video Segmentation Using SAM2.\u003c\/p\u003e\u003cp\u003e.- Disentanglement and Assessment of Shortcuts in Ophthalmological\u003cbr\u003eRetinal Imaging Exams.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53195526832471,"sku":"9783032058690","price":49.99,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/fairness-of-ai-in-medical-imaging-9783032058690","provider":"Book Curl","version":"1.0","type":"link"}