Search Results for author:
Found 24 papers, 10 papers with code
Knowledge Distillation for Adaptive MRI Prostate Segmentation Based on Limit-Trained Multi-Teacher Models
To deal with this issue, Knowledge Distillation (KD) has been proposed as a compression method and an acceleration technology.
An Enhanced Scheme for Reducing the Complexity of Pointwise Convolutions in CNNs for Image Classification Based on Interleaved Grouped Filters without Divisibility Constraints
The number of groups of filters and filters per group for layers K and L is determined by exact divisions of the original number of input channels and filters by Ch.
Ranked #1 on
Image Classification
on Malaria Dataset
Color-aware two-branch DCNN for efficient plant disease classification
Deep convolutional neural networks (DCNNs) have been successfully applied to plant disease detection.
Ranked #3 on
Image Classification
on PlantVillage
Grouped Pointwise Convolutions Reduce Parameters in Convolutional Neural Networks
In Deep Convolutional Neural Networks (DCNNs), the parameter count in pointwise convolutions quickly grows due to the multiplication of the filters and input channels from the preceding layer.
Ranked #1 on
Image Classification
on PlantDoc
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network
In particular, our method provided comparable and promising results with a high prediction accuracy of 89% on the publicly KITTI and Make3D datasets along with a reduction of 40% in the number of trainable parameters compared to the state of the art solutions.
Ranked #1 on
Monocular Depth Estimation
on Make3D
T-YOLO: Tiny Vehicle Detection Based on YOLO and Multi-Scale Convolutional Neural Networks
In fact, as shown in the experiments, the results show a small reduction from 7. 28 million parameters of the YOLO-v5-S profile to 7. 26 million parameters in our model.
Ranked #1 on
Parking Space Occupancy
on PKLot
(using extra training data)
Absolute distance prediction based on deep learning object detection and monocular depth estimation models
Determining the distance between the objects in a scene and the camera sensor from 2D images is feasible by estimating depth images using stereo cameras or 3D cameras.
Reliable Deep Learning Plant Leaf Disease Classification Based on Light-Chroma Separated Branches
The Food and Agriculture Organization (FAO) estimated that plant diseases cost the world economy $220 billion in 2019.
Ranked #3 on
Image Classification
on PlantVillage
Grouped Pointwise Convolutions Significantly Reduces Parameters in EfficientNet
Our proposal is to improve the pointwise (1x1) convolutions, whose number of parameters rapidly grows due to the multiplication of the number of filters by the number of input channels that come from the previous layer.
Ranked #5 on
Image Classification
on Oxford-IIIT Pet Dataset
(PARAMS metric)
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.
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.
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.
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.
Identification and Visualization of the Underlying Independent Causes of the Diagnostic of Diabetic Retinopathy made by a Deep Learning Classifier
In this paper we go forward into the generation of explanations by identifying the independent causes that use a deep learning model for classifying an image into a certain class.
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.
Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network
Then, the discriminator learns as a loss function to train this mapping by comparing the ground-truth and the predicted output with observing the input image as a condition. Experiments were performed on two publicly available dataset; DRISHTI GS1 and RIM-ONE.
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.
A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading
Deep neural network models have been proven to be very successful in image classification tasks, also for medical diagnosis, but their main concern is its lack of interpretability.
Analyzing Stability of Convolutional Neural Networks in the Frequency Domain
Understanding the internal process of ConvNets is commonly done using visualization techniques.
