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Found 11 papers, 4 papers with code
Cross-Validation Is All You Need: A Statistical Approach To Label Noise Estimation
Most existing label noise detection approaches are designed for classification tasks, and data cleaning for outcome prediction analysis is relatively unexplored.
Intra-Modal Constraint Loss For Image-Text Retrieval
Cross-modal retrieval has drawn much attention in both computer vision and natural language processing domains.
Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling
Cancer stage is a large determinant of patient prognosis and management in many cancer types, and is often assessed using medical imaging modalities, such as CT and MRI.
The MICCAI Hackathon on reproducibility, diversity, and selection of papers at the MICCAI conference
With this growth, however, come new challenges for the community.
AMINN: Autoencoder-based Multiple Instance Neural Network Improves Outcome Prediction of Multifocal Liver Metastases
Experimental results empirically validated our hypothesis that incorporating imaging features of all lesions improves outcome prediction for multifocal cancer.
AIBench Training: Balanced Industry-Standard AI Training Benchmarking
We use real-world benchmarks to cover the factors space that impacts the learning dynamics to the most considerable extent.
Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation
Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients.
AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite
An end-to-end benchmark is a distillation of the essential attributes of an industry-scale application.
How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study
The \emph{second contribution} is that we systematically evaluated five benchmark methods on two representative public datasets.
Unsupervised Clustering of Quantitative Imaging Phenotypes using Autoencoder and Gaussian Mixture Model
The performance of the proposed pipeline was evaluated on an institutional MRI cohort of 108 patients with colorectal cancer liver metastases.
Multi-layer Domain Adaptation for Deep Convolutional Networks
Despite their success in many computer vision tasks, convolutional networks tend to require large amounts of labeled data to achieve generalization.
