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Found 12 papers, 3 papers with code
The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting
The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada.
BenchMD: A Benchmark for Unified Learning on Medical Images and Sensors
Finally, we evaluate performance on out-of-distribution data collected at different hospitals than the training data, representing naturally-occurring distribution shifts that frequently degrade the performance of medical AI models.
AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT
While it is well known that population differences from genetics, sex, race, and environmental factors contribute to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources.
NanoBatch Privacy: Enabling fast Differentially Private learning on the IPU
We also provide two extensions: 1) DPSGD for pipelined models and 2) per-layer clipping that is 15x faster than the Opacus implementation on 8x A100s.
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society.
A Real-World Demonstration of Machine Learning Generalizability: Intracranial Hemorrhage Detection on Head CT
An ML model was trained using 21, 784 scans from the RSNA Intracranial Hemorrhage CT dataset while generalizability was evaluated using an external validation dataset obtained from our busy trauma and neurosurgical center.
Deep Sequential Learning for Cervical Spine Fracture Detection on Computed Tomography Imaging
Fractures of the cervical spine are a medical emergency and may lead to permanent paralysis and even death.
Cylindrical Transform: 3D Semantic Segmentation of Kidneys With Limited Annotated Images
The images generated from a cylindrical transform augment a limited annotated set of images in three dimensions.
Recent Advances in Recurrent Neural Networks
Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time-series data.
Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks
Medical datasets are often highly imbalanced with over-representation of common medical problems and a paucity of data from rare conditions.
Interpretation of Mammogram and Chest X-Ray Reports Using Deep Neural Networks - Preliminary Results
Radiology reports are an important means of communication between radiologists and other physicians.
A Convolutional Neural Network for Search Term Detection
Pathfinding in hospitals is challenging for patients, visitors, and even employees.
