A review on multiscale-deep-learning applications
In general, most of the existing convolutional neural network (CNN)-based deep-learning
models suffer from spatial-information loss and inadequate feature-representation issues …
models suffer from spatial-information loss and inadequate feature-representation issues …
Holistically-nested edge detection
We develop a new edge detection algorithm that addresses two critical issues in this long-
standing vision problem:(1) holistic image training; and (2) multi-scale feature learning. Our …
standing vision problem:(1) holistic image training; and (2) multi-scale feature learning. Our …
Multi-scale continuous crfs as sequential deep networks for monocular depth estimation
This paper addresses the problem of depth estimation from a single still image. Inspired by
recent works on multi-scale convolutional neural networks (CNN), we propose a deep model …
recent works on multi-scale convolutional neural networks (CNN), we propose a deep model …
Structured attention guided convolutional neural fields for monocular depth estimation
Recent works have shown the benefit of integrating Conditional Random Fields (CRFs)
models into deep architectures for improving pixel-level prediction tasks. Following this line …
models into deep architectures for improving pixel-level prediction tasks. Following this line …
DeepPap: deep convolutional networks for cervical cell classification
Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a
highly effective cell imaging based cancer detection tool, where cells are partitioned into …
highly effective cell imaging based cancer detection tool, where cells are partitioned into …
Combining convolutional neural network with recursive neural network for blood cell image classification
G Liang, H Hong, W Xie, L Zheng - IEEE access, 2018 - ieeexplore.ieee.org
The diagnosis of blood-related diseases involves the identification and characterization of a
patient's blood sample. As such, automated methods for detecting and classifying the types …
patient's blood sample. As such, automated methods for detecting and classifying the types …
Macular OCT classification using a multi-scale convolutional neural network ensemble
Computer-aided diagnosis (CAD) of retinal pathologies is a current active area in medical
image analysis. Due to the increasing use of retinal optical coherence tomography (OCT) …
image analysis. Due to the increasing use of retinal optical coherence tomography (OCT) …
G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition
Scene recognition plays an important role in the task of visual information retrieval,
segmentation and image/video understanding. Traditional approaches for scene recognition …
segmentation and image/video understanding. Traditional approaches for scene recognition …
Deep learning-based gleason grading of prostate cancer from histopathology images—role of multiscale decision aggregation and data augmentation
Visual inspection of histopathology images of stained biopsy tissue by expert pathologists is
the standard method for grading of prostate cancer (PCa). However, this process is time …
the standard method for grading of prostate cancer (PCa). However, this process is time …
HEp-2 cell image classification with deep convolutional neural networks
Efficient Human Epithelial-2 cell image classification can facilitate the diagnosis of many
autoimmune diseases. This paper proposes an automatic framework for this classification …
autoimmune diseases. This paper proposes an automatic framework for this classification …