Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network
D Mahapatra, B Bozorgtabar, JP Thiran… - … Conference on Medical …, 2018 - Springer
Training robust deep learning (DL) systems for medical image classification or segmentation
is challenging due to limited images covering different disease types and severity. We …
is challenging due to limited images covering different disease types and severity. We …
Single nighttime image dehazing based on unified variational decomposition model and multi-scale contrast enhancement
Most of existing dehazing methods are unable to deal with nighttime hazy scenarios well
due to complex degraded factors such as non-uniform illumination, low light, glows and …
due to complex degraded factors such as non-uniform illumination, low light, glows and …
Interpretability-driven sample selection using self supervised learning for disease classification and segmentation
D Mahapatra, A Poellinger, L Shao… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
In supervised learning for medical image analysis, sample selection methodologies are
fundamental to attain optimum system performance promptly and with minimal expert …
fundamental to attain optimum system performance promptly and with minimal expert …
Nighttime image-dehazing: a review and quantitative benchmarking
S Banerjee, S Sinha Chaudhuri - Archives of Computational Methods in …, 2021 - Springer
Visibility enhancement of images captured during hazy weather conditions is highly
essential for various important applications like intelligent vehicles, surveillance, remote …
essential for various important applications like intelligent vehicles, surveillance, remote …
Multi-path dilated convolution network for haze and glow removal in nighttime images
In this paper, we address the single-image haze removal problem in nighttime scenes. The
night haze removal is a severely ill-posed problem due to the presence of various visible …
night haze removal is a severely ill-posed problem due to the presence of various visible …
Weakly-supervised network for detection of COVID-19 in chest CT scans
Deep Learning-based chest Computed Tomography (CT) analysis has been proven to be
effective and efficient for COVID-19 diagnosis. Existing deep learning approaches heavily …
effective and efficient for COVID-19 diagnosis. Existing deep learning approaches heavily …
Structure preserving stain normalization of histopathology images using self supervised semantic guidance
D Mahapatra, B Bozorgtabar, JP Thiran… - Medical Image Computing …, 2020 - Springer
Although generative adversarial network (GAN) based style transfer is state of the art in
histopathology color-stain normalization, they do not explicitly integrate structural …
histopathology color-stain normalization, they do not explicitly integrate structural …
Adaptive CU mode selection in HEVC intra prediction: A deep learning approach
The computational time of HEVC encoder is increased mainly because of the hierarchical
quad-tree-based structure, recursive coding units, and the exhaustive prediction search up …
quad-tree-based structure, recursive coding units, and the exhaustive prediction search up …
Single nighttime image dehazing based on image decomposition
Y Liu, A Wang, H Zhou, P Jia - Signal Processing, 2021 - Elsevier
Dehazing plays an important role in promoting the performance of outdoor computer vision
systems. However, existing dehazing methods are targeted to daytime haze scenes, and are …
systems. However, existing dehazing methods are targeted to daytime haze scenes, and are …
Unsupervised domain adaptation using feature disentanglement and GCNs for medical image classification
D Mahapatra, S Korevaar, B Bozorgtabar… - … on Computer Vision, 2022 - Springer
The success of deep learning has set new benchmarks for many medical image analysis
tasks. However, deep models often fail to generalize in the presence of distribution shifts …
tasks. However, deep models often fail to generalize in the presence of distribution shifts …