Search Results for author:
Found 16 papers, 2 papers with code
A dynamic interactive learning framework for automated 3D medical image segmentation
Many deep learning based automated medical image segmentation systems, in reality, face difficulties in deployment due to the cost of massive data annotation and high latency in model iteration.
AttenScribble: Attentive Similarity Learning for Scribble-Supervised Medical Image Segmentation
This attentive similarity loss optimizes the alignment of FCN encoders, attention mapping and model prediction.
Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation
The advent of Industry 4. 0 has precipitated the incorporation of Artificial Intelligence (AI) methods within industrial contexts, aiming to realize intelligent manufacturing, operation as well as maintenance, also known as industrial intelligence.
The Solution for the CVPR2023 NICE Image Captioning Challenge
In this paper, we present our solution to the New frontiers for Zero-shot Image Captioning Challenge.
NICE: CVPR 2023 Challenge on Zero-shot Image Captioning
In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge.
Complementary to Multiple Labels: A Correlation-Aware Correction Approach
Moreover, theoretical findings reveal that calculating a transition matrix from label correlations in \textit{multi-labeled CLL} (ML-CLL) needs multi-labeled data, while this is unavailable for ML-CLL.
Happy or grumpy? A Machine Learning Approach to Analyze the Sentiment of Airline Passengers' Tweets
This study aims to measure customer satisfaction by analyzing sentiments of Tweets that mention airlines using a machine learning approach.
Cluster-based Contrastive Disentangling for Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) aims to recognize both seen and unseen classes by training only the seen classes, in which the instances of unseen classes tend to be biased towards the seen class.
MCMC Guided CNN Training and Segmentation for Pancreas Extraction
At the same time, the pancreas distribution is also learned for the subsequent segmentation.
FEA-Net: A Physics-guided Data-driven Model for Efficient Mechanical Response Prediction
Based on this, a special type of deep convolutional neural network (DCNN) is proposed that takes advantage of our prior knowledge in physics to build data-driven models whose architectures are of physics meaning.
Large scale digital prostate pathology image analysis combining feature extraction and deep neural network
In this work, we present an analysis pipeline that includes localization of the cancer region, grading, area ratio of different Gleason grades, and cytological/architectural feature extraction.
Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images
In this work, we propose a sparse Convolutional Autoencoder (CAE) for fully unsupervised, simultaneous nucleus detection and feature extraction in histopathology tissue images.
Neural Networks with Smooth Adaptive Activation Functions for Regression
In this paper, we propose and apply AAFs on feedforward NNs for regression tasks.
Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification
However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible.
