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Found 7 papers, 1 papers with code
Exploiting Richness of Learned Compressed Representation of Images for Semantic Segmentation
Autonomous vehicles and Advanced Driving Assistance Systems (ADAS) have the potential to radically change the way we travel.
Lung Segmentation and Nodule Detection in Computed Tomography Scan using a Convolutional Neural Network Trained Adversarially using Turing Test Loss
Lung cancer is the most common form of cancer found worldwide with a high mortality rate.
A Systematic Search over Deep Convolutional Neural Network Architectures for Screening Chest Radiographs
Chest radiographs are primarily employed for the screening of pulmonary and cardio-/thoracic conditions.
Learning Decision Ensemble using a Graph Neural Network for Comorbidity Aware Chest Radiograph Screening
Chest radiographs are primarily employed for the screening of cardio, thoracic and pulmonary conditions.
A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms
Our method performed well on the task of localization of masses with an average Precision/Recall of 0. 76/0. 80 and acheived an average AUC of 0. 91 on the imagelevel classification task using a five-fold cross-validation on the INbreast dataset.
Jointly Learning Convolutional Representations to Compress Radiological Images and Classify Thoracic Diseases in the Compressed Domain
Deep learning models trained in natural images are commonly used for different classification tasks in the medical domain.
Ranked #5 on
Pneumonia Detection
on ChestX-ray14
Fully Convolutional Model for Variable Bit Length and Lossy High Density Compression of Mammograms
Early works on medical image compression date to the 1980's with the impetus on deployment of teleradiology systems for high-resolution digital X-ray detectors.
