Search Results for author:
Found 12 papers, 4 papers with code
Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review
For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan.
AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
Accurate lung nodule detection and segmentation in computed tomography (CT) images is the most important part of diagnosing lung cancer in the early stage.
Adversarial Learning with Multiscale Features and Kernel Factorization for Retinal Blood Vessel Segmentation
In this paper, we propose an efficient blood vessel segmentation method for the eye fundus images using adversarial learning with multiscale features and kernel factorization.
An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning
We propose to add an atrous convolution layer to the conditional generative adversarial network (cGAN) segmentation model to learn tumor features at different resolutions of BUS images.
SLSNet: Skin lesion segmentation using a lightweight generative adversarial network
Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model.
Hierarchical approach to classify food scenes in egocentric photo-streams
Recent studies have shown that the environment where people eat can affect their nutritional behaviour.
Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network
In this paper, we proposed a conditional Generative Adversarial Network (cGAN) devised to segment a breast mass within a region of interest (ROI) in a mammogram.
MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams
This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams.
REFUGE CHALLENGE 2018-Task 2:Deep Optic Disc and Cup Segmentation in Fundus Images Using U-Net and Multi-scale Feature Matching Networks
In this paper, an optic disc and cup segmentation method is proposed using U-Net followed by a multi-scale feature matching network.
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network
Diversity of food and its attributes represents the culinary habits of peoples from different countries.
Cultural Vocal Bursts Intensity Prediction
General Classification
+1
SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks
The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge.
Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification
This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography.
