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Found 9 papers, 0 papers with code
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.
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.
Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging
In this paper, we present a data-driven fully automated method for estimation of core and penumbra in ischaemic lesions using diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) sequence maps of MRI.
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.
Application of S-Transform on Hyper kurtosis based Modified Duo Histogram Equalized DIC images for Pre-cancer Detection
Our proposed hyper kurtosis based histogram equalized DIC images enhances the contrast by preserving the brightness.
A comparative study between proposed Hyper Kurtosis based Modified Duo-Histogram Equalization (HKMDHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) for Contrast Enhancement Purpose of Low Contrast Human Brain CT scan images
In this paper, a comparative study between proposed hyper kurtosis based modified duo-histogram equalization (HKMDHE) algorithm and contrast limited adaptive histogram enhancement (CLAHE) has been presented for the implementation of contrast enhancement and brightness preservation of low contrast human brain CT scan images.
Wavelet based approach for tissue fractal parameter measurement: Pre cancer detection
In this paper, we have carried out the detail studies of pre-cancer by wavelet coherency and multifractal based detrended fluctuation analysis (MFDFA) on differential interference contrast (DIC) images of stromal region among different grades of pre-cancer tissues.
Diagnosing Heterogeneous Dynamics for CT Scan Images of Human Brain in Wavelet and MFDFA domain
CT scan images of human brain of a particular patient in different cross sections are taken, on which wavelet transform and multi-fractal analysis are applied.
