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Found 15 papers, 9 papers with code
Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses
To generate evidence regarding the safety and efficacy of artificial intelligence (AI) enabled medical devices, AI models need to be evaluated on a diverse population of patient cases, some of which may not be readily available.
THMA: Tencent HD Map AI System for Creating HD Map Annotations
THMA has been deployed by the Tencent Map team to provide services to downstream companies and users, serving over 1, 000 labeling workers and producing more than 30, 000 kilometers of HD map data per day at most.
ViTASD: Robust Vision Transformer Baselines for Autism Spectrum Disorder Facial Diagnosis
Research progress in the field of ASD facial analysis in pediatric patients has been hindered due to a lack of well-established baselines.
Automatic Infectious Disease Classification Analysis with Concept Discovery
Automatic infectious disease classification from images can facilitate needed medical diagnoses.
Improving Computed Tomography (CT) Reconstruction via 3D Shape Induction
Chest computed tomography (CT) imaging adds valuable insight in the diagnosis and management of pulmonary infectious diseases, like tuberculosis (TB).
SATBench: Benchmarking the speed-accuracy tradeoff in object recognition by humans and dynamic neural networks
Using FLOPs as an analog for reaction time, we compare networks with humans on curve-fit error, category-wise correlation, and curve steepness, and conclude that cascaded dynamic neural networks are a promising model of human reaction time in object recognition tasks.
CASS: Cross Architectural Self-Supervision for Medical Image Analysis
We also show that CASS is much more robust to changes in batch size and training epochs.
Ranked #1 on
Classification
on Brain Tumor MRI Dataset
Semi-supervised Nonnegative Matrix Factorization for Document Classification
We propose new semi-supervised nonnegative matrix factorization (SSNMF) models for document classification and provide motivation for these models as maximum likelihood estimators.
Improving Tuberculosis (TB) Prediction using Synthetically Generated Computed Tomography (CT) Images
The evaluation of infectious disease processes on radiologic images is an important and challenging task in medical image analysis.
Large-Scale Unsupervised Object Discovery
Extensive experiments on COCO and OpenImages show that, in the single-object discovery setting where a single prominent object is sought in each image, the proposed LOD (Large-scale Object Discovery) approach is on par with, or better than the state of the art for medium-scale datasets (up to 120K images), and over 37% better than the only other algorithms capable of scaling up to 1. 7M images.
Anytime Prediction as a Model of Human Reaction Time
We conclude that Anytime classification (i. e. early exits) is a promising model for human reaction time in recognition tasks.
Learning to Infer Semantic Parameters for 3D Shape Editing
Many applications in 3D shape design and augmentation require the ability to make specific edits to an object's semantic parameters (e. g., the pose of a person's arm or the length of an airplane's wing) while preserving as much existing details as possible.
Semi-supervised NMF Models for Topic Modeling in Learning Tasks
We propose several new models for semi-supervised nonnegative matrix factorization (SSNMF) and provide motivation for SSNMF models as maximum likelihood estimators given specific distributions of uncertainty.
Ranked #13 on
Text Classification
on 20NEWS
Using Human Psychophysics to Evaluate Generalization in Scene Text Recognition Models
Relative to the attention-based (Attn) model, we discover that the connectionist temporal classification (CTC) model is more robust to noise and occlusion, and better at generalizing to different word lengths.
On Large-Scale Dynamic Topic Modeling with Nonnegative CP Tensor Decomposition
There is currently an unprecedented demand for large-scale temporal data analysis due to the explosive growth of data.
