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Found 55 papers, 12 papers with code
How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images
To evaluate the label-efficiency of our finetuning method, we compare the results of these three prediction heads on a public medical image segmentation dataset with limited labeled data.
Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation
With different amounts of labeled data, our methods consistently outperform the state-of-the-art contrast-based methods and other semi-supervised learning techniques.
C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer
Although existing GAN-based HVMT methods have achieved great success, they either fail to preserve appearance details due to the loss of spatial consistency between synthesized and exemplary images, or generate incoherent video results due to the lack of temporal consistency among video frames.
Positional Contrastive Learning for Volumetric Medical Image Segmentation
The success of deep learning heavily depends on the availability of large labeled training sets.
Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labels
In the segmentation phase, we achieved tissue semantic segmentation by our proposed Multi-Layer Pseudo-Supervision.
ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease
To demonstrate this, we further present a baseline framework for the automatic classification of CHD, based on a state-of-the-art CHD segmentation method.
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
This paper studies the unsupervised domain adaption (UDA) for echocardiogram video segmentation, where the goal is to generalize the model trained on the source domain to other unlabelled target domains.
Less is More: Surgical Phase Recognition from Timestamp Supervision
Our study uncovers unique insights of surgical phase recognition with timestamp supervisions: 1) timestamp annotation can reduce 74% annotation time compared with the full annotation, and surgeons tend to annotate those timestamps near the middle of phases; 2) extensive experiments demonstrate that our method can achieve competitive results compared with full supervision methods, while reducing manual annotation cost; 3) less is more in surgical phase recognition, i. e., less but discriminative pseudo labels outperform full but containing ambiguous frames; 4) the proposed UATD can be used as a plug and play method to clean ambiguous labels near boundaries between phases, and improve the performance of the current surgical phase recognition methods.
ColluEagle: Collusive review spammer detection using Markov random fields
Previous works use review network effects, i. e. the relationships among reviewers, reviews, and products, to detect fake reviews or review spammers, but ignore time effects, which are critical in characterizing group spamming.
Segmentation with Multiple Acceptable Annotations: A Case Study of Myocardial Segmentation in Contrast Echocardiography
Experiment results on our clinical MCE data set demonstrate that the neural network trained with the proposed loss function outperforms those existing ones that try to obtain a unique ground truth from multiple annotations, both quantitatively and qualitatively.
Dynamic Sub-Cluster-Aware Network for Few-Shot Skin Disease Classification
To achieve the objective of the second branch, we present a cluster loss to learn image similarities via unsupervised clustering.
GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentation
Additionally, a Multi-view Local-based Fusion Module (MLFM) is designed to extract correlations of cardiac structures from different views.
PBGen: Partial Binarization of Deconvolution-Based Generators for Edge Intelligence
This work explores the binarization of the deconvolution-based generator in a GAN for memory saving and speedup of image construction.
Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation
Unlike existing litera- ture on quantization which primarily targets memory and computation complexity reduction, we apply quan- tization as a method to reduce over tting in FCNs for better accuracy.
Lifelong Metric Learning
In this paper, we consider lifelong learning problem to mimic "human learning", i. e., endowing a new capability to the learned metric for a new task from new online samples and incorporating previous experiences and knowledge.
DAC-SDC Low Power Object Detection Challenge for UAV Applications
The 55th Design Automation Conference (DAC) held its first System Design Contest (SDC) in 2018.
Text Embeddings for Retrieval From a Large Knowledge Base
Text embedding representing natural language documents in a semantic vector space can be used for document retrieval using nearest neighbor lookup.
MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation
The three multi-scale dense connections improve U-net performance by up to 1. 8% on test A and 3. 5% on test B in the MICCAI Gland dataset.
A Multi-Resolution Word Embedding for Document Retrieval from Large Unstructured Knowledge Bases
Deep language models learning a hierarchical representation proved to be a powerful tool for natural language processing, text mining and information retrieval.
On the Quantization of Cellular Neural Networks for Cyber-Physical Systems
In some CPS applications such as telemedicine and advanced driving assistance system (ADAS), data processing on the embedded devices is preferred due to security/safety and real-time requirement.
SCNN: A General Distribution based Statistical Convolutional Neural Network with Application to Video Object Detection
By introducing a parameterized canonical model to model correlated data and defining corresponding operations as required for CNN training and inference, we show that SCNN can process multiple frames of correlated images effectively, hence achieving significant speedup over existing CNN models.
DLBC: A Deep Learning-Based Consensus in Blockchains for Deep Learning Services
In this paper, we propose DLBC to exploit the computation power of miners for deep learning training as proof of useful work instead of calculating hash values.
Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds
In this paper we will use deep learning based medical image segmentation as a vehicle and demonstrate that interestingly, machine and human view the compression quality differently.
Accurate Congenital Heart Disease Model Generation for 3D Printing
3D printing has been widely adopted for clinical decision making and interventional planning of Congenital heart disease (CHD), while whole heart and great vessel segmentation is the most significant but time-consuming step in the model generation for 3D printing.
MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation
Cardiac magnetic resonance imaging (MRI) is an essential tool for MRI-guided surgery and real-time intervention.
On Neural Architecture Search for Resource-Constrained Hardware Platforms
With 30, 000 LUTs, a light-weight design is found to achieve 82. 98\% accuracy and 1293 images/second throughput, compared to which, under the same constraints, the traditional method even fails to find a valid solution.
MRNN: A Multi-Resolution Neural Network with Duplex Attention for Document Retrieval in the Context of Question Answering
The primary goal of ad-hoc retrieval (document retrieval in the context of question answering) is to find relevant documents satisfied the information need posted in a natural language query.
GAC-GAN: A General Method for Appearance-Controllable Human Video Motion Transfer
Therefore, each trained model can only generate videos with a specific scene appearance, new models are required to be trained to generate new appearances.
Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia
We found that the real time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab has a relatively low positive rate in the early stage to determine COVID-19 (named by the World Health Organization).
What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation
Unsupervised domain adaptation has attracted growing research attention on semantic segmentation.
Evolving Metric Learning for Incremental and Decremental Features
Online metric learning has been widely exploited for large-scale data classification due to the low computational cost.
BUNET: Blind Medical Image Segmentation Based on Secure UNET
The strict security requirements placed on medical records by various privacy regulations become major obstacles in the age of big data.
ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation
Real-time cine magnetic resonance imaging (MRI) plays an increasingly important role in various cardiac interventions.
Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation
Experimental results on ACDC MICCAI 2017 dataset demonstrate that our hardware-aware multi-scale NAS framework can reduce the latency by up to 3. 5 times and satisfy the real-time constraints, while still achieving competitive segmentation accuracy, compared with the state-of-the-art NAS segmentation framework.
CSCL: Critical Semantic-Consistent Learning for Unsupervised Domain Adaptation
Unsupervised domain adaptation without consuming annotation process for unlabeled target data attracts appealing interests in semantic segmentation.
Do Noises Bother Human and Neural Networks In the Same Way? A Medical Image Analysis Perspective
Deep learning had already demonstrated its power in medical images, including denoising, classification, segmentation, etc.
Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation
Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost.
Myocardial Segmentation of Cardiac MRI Sequences with Temporal Consistency for Coronary Artery Disease Diagnosis
Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial segmentation of Magnetic Resonance Imaging (MRI) sequences.
ObjectAug: Object-level Data Augmentation for Semantic Image Segmentation
In addition, ObjectAug can support category-aware augmentation that gives various possibilities to objects in each category, and can be easily combined with existing image-level augmentation methods to further boost performance.
Pyramid U-Net for Retinal Vessel Segmentation
To further improve performance, two optimizations including pyramid inputs enhancement and deep pyramid supervision are applied to PSABs in the encoder and decoder, respectively.
EchoCP: An Echocardiography Dataset in Contrast Transthoracic Echocardiography for Patent Foramen Ovale Diagnosis
For PFO diagnosis, contrast transthoracic echocardiography (cTTE) is preferred as being a more robust method compared with others.
ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection
Results show that the baseline method can achieve comparable results with existing works on aorta and TL segmentation.
Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography
At the data acquisition time, the operator could not know the quality of the segmentation results.
Hardware Aware Neural Architecture Search
Neural Architecture Search
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"One-Shot" Reduction of Additive Artifacts in Medical Images
Medical images may contain various types of artifacts with different patterns and mixtures, which depend on many factors such as scan setting, machine condition, patients' characteristics, surrounding environment, etc.
Uncertainty-Aware Training of Neural Networks for Selective Medical Image Segmentation
Existing selective segmentation methods, however, ignore this unique property of selective segmentation and train their DNN models by optimizing accuracy on the entire dataset.
FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis
By pruning the parameters based on this importance difference, we can reduce the accuracy difference between the privileged group and the unprivileged group to improve fairness without a large accuracy drop.
RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation
Accurately segmenting temporal frames of cine magnetic resonance imaging (MRI) is a crucial step in various real-time MRI guided cardiac interventions.
Unsupervised Knowledge Graph Construction and Event-centric Knowledge Infusion for Scientific NLI
Therefore, we propose an unsupervised knowledge graph construction method to build a scientific knowledge graph (SKG) without any labeled data.
ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images
In this paper, we propose a large-scale dataset for coronary artery segmentation on CTA images.
SATBA: An Invisible Backdoor Attack Based On Spatial Attention
In this paper, we propose a novel backdoor attack named SATBA that overcomes these limitations using spatial attention and an U-net based model.
TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detection
This paper concludes with the direction of improvement for the future TinyML design for health monitoring applications.
Additional Positive Enables Better Representation Learning for Medical Images
This paper presents a new way to identify additional positive pairs for BYOL, a state-of-the-art (SOTA) self-supervised learning framework, to improve its representation learning ability.
BNS-Net: A Dual-channel Sarcasm Detection Method Considering Behavior-level and Sentence-level Conflicts
Sarcasm detection is a binary classification task that aims to determine whether a given utterance is sarcastic.
Towards Trustable Language Models: Investigating Information Quality of Large Language Models
Large language models (LLM) are generating information at a rapid pace, requiring users to increasingly rely and trust the data.
Invisible Backdoor Attack Through Singular Value Decomposition
With the widespread application of deep learning across various domains, concerns about its security have grown significantly.
