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Found 6 papers, 2 papers with code
Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms
Precision breast cancer (BC) risk assessment is crucial for developing individualized screening and prevention.
Problems in AI, their roots in philosophy, and implications for science and society
In line with Popper and Deutsch, it is proposed that all these theories are based on mistaken philosophies of knowledge.
Nodule detection and generation on chest X-rays: NODE21 Challenge
To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays.
Task-Driven Wavelets using Constrained Empirical Risk Minimization
Deep Neural Networks (DNNs) are widely used for their ability to effectively approximate large classes of functions.
Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms
If one only requires only marginal calibration on the image level, this calibration set consists of all individual pixels in the images available for calibration.
Constrained Empirical Risk Minimization: Theory and Practice
Deep Neural Networks (DNNs) are widely used for their ability to effectively approximate large classes of functions.
