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Found 12 papers, 6 papers with code
Mediastinal Lymph Node Detection and Segmentation Using Deep Learning
Automatic lymph node (LN) segmentation and detection for cancer staging are critical.
Coronavirus disease situation analysis and prediction using machine learning: a study on Bangladeshi population
According to the prediction produced by the model, Bangladesh may suffer another COVID-19 attack, where the number of infected cases can be between 929 to 2443 and death cases between 19 to 57.
Rational LAMOL: A Rationale-based Lifelong Learning Framework
Lifelong learning (LL) aims to train a neural network on a stream of tasks while retaining knowledge from previous tasks.
High resolution weakly supervised localization architectures for medical images
In medical imaging, Class-Activation Map (CAM) serves as the main explainability tool by pointing to the region of interest.
Encoder-Decoder Based Convolutional Neural Network with Multi-Scale-Aware Modules for Crowd Counting
In this paper, we proposed two modified neural network architectures based on SFANet and SegNet respectively for accurate and efficient crowd counting.
Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting
Inspired by SFANet, the first model, which is named M-SFANet, is attached with atrous spatial pyramid pooling (ASPP) and context-aware module (CAN).
Ranked #1 on
Crowd Counting
on UCF CC 50
CProp: Adaptive Learning Rate Scaling from Past Gradient Conformity
When the past gradients agree on direction, CProp keeps the original learning rate.
A Comparative Study of Pretrained Language Models on Thai Social Text Categorization
The ever-growing volume of data of user-generated content on social media provides a nearly unlimited corpus of unlabeled data even in languages where resources are scarce.
Spatiotemporal Emotion Recognition using Deep CNN Based on EEG during Music Listening
In this work, a study of CNN and its spatiotemporal feature extraction has been conducted in order to explore capabilities of the model in varied window sizes and electrode orders.
Efficient Decision Trees for Multi-class Support Vector Machines Using Entropy and Generalization Error Estimation
We propose new methods for Support Vector Machines (SVMs) using tree architecture for multi-class classi- fication.
Sub-Classifier Construction for Error Correcting Output Code Using Minimum Weight Perfect Matching
We propose a method for constructing the Error Correcting Output Code to obtain the suitable combination of positive and negative classes encoded to represent binary classifiers.
Enhancements of Multi-class Support Vector Machine Construction from Binary Learners using Generalization Performance
We propose several novel methods for enhancing the multi-class SVMs by applying the generalization performance of binary classifiers as the core idea.
