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Found 5 papers, 2 papers with code
Evaluating Large Language Models with fmeval
A case study demonstrates a typical use case for the library: picking a suitable model for a question answering task.
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud
We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining predictions.
Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network
We show the potential of this approach to detect enlarged perivascular spaces in white matter on a large brain MRI dataset with an independent test set of 1000 scans.
Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks
For comparison, we modify state-of-the-art methods to compute attention maps for weakly supervised object detection, by using a global regression objective instead of the more conventional classification objective.
