research
∙
07/21/2023
Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation
Medical imaging has witnessed remarkable progress but usually requires a...
share
research
∙
05/31/2023
MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Images
Micro-ultrasound (micro-US) is a novel 29-MHz ultrasound technique that ...
research
∙
05/31/2023
Image Registration of In Vivo Micro-Ultrasound and Ex Vivo Pseudo-Whole Mount Histopathology Images of the Prostate: A Proof-of-Concept Study
Early diagnosis of prostate cancer significantly improves a patient's 5-...
research
∙
03/22/2023
Distribution Aligned Diffusion and Prototype-guided network for Unsupervised Domain Adaptive Segmentation
The Diffusion Probabilistic Model (DPM) has emerged as a highly effectiv...
research
∙
12/20/2022
Unleashing the Power of Visual Prompting At the Pixel Level
This paper presents a simple and effective visual prompting method for a...
research
∙
10/12/2022
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning
Learning medical visual representations directly from paired radiology r...
research
∙
09/06/2022
Bag of Tricks for FGSM Adversarial Training
Adversarial training (AT) with samples generated by Fast Gradient Sign M...
research
∙
08/25/2022
Masked Autoencoders Enable Efficient Knowledge Distillers
This paper studies the potential of distilling knowledge from pre-traine...
research
∙
06/15/2022
A Simple Data Mixing Prior for Improving Self-Supervised Learning
Data mixing (e.g., Mixup, Cutmix, ResizeMix) is an essential component f...
research
∙
06/07/2022
Can CNNs Be More Robust Than Transformers?
The recent success of Vision Transformers is shaking the long dominance ...
research
∙
05/03/2022
In Defense of Image Pre-Training for Spatiotemporal Recognition
Image pre-training, the current de-facto paradigm for a wide range of vi...
research
∙
04/08/2022
CD^2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning
Federated learning (FL) is a distributed learning paradigm that enables ...
research
∙
02/09/2022
Learning to Bootstrap for Combating Label Noise
Deep neural networks are powerful tools for representation learning, but...
research
∙
01/04/2022
External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data
The spleen is one of the most commonly injured solid organs in blunt abd...
research
∙
11/30/2021
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021
A collection of the accepted abstracts for the Machine Learning for Heal...
research
∙
11/23/2021
RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR
Despite the routine use of electronic health record (EHR) data by radiol...
research
∙
05/31/2021
Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction
Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause o...
research
∙
03/29/2021
DualNorm-UNet: Incorporating Global and Local Statistics for Robust Medical Image Segmentation
Batch Normalization (BN) is one of the key components for accelerating n...
research
∙
11/25/2020
Can Temporal Information Help with Contrastive Self-Supervised Learning?
Leveraging temporal information has been regarded as essential for devel...
research
∙
10/29/2020
Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples
Although deep neural networks have been a dominant method for many 2D vi...
research
∙
05/18/2020
Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation
In this paper, we present a novel unsupervised domain adaptation (UDA) m...
research
∙
04/04/2020
Neural Architecture Search for Lightweight Non-Local Networks
Non-Local (NL) blocks have been widely studied in various vision tasks. ...
research
∙
03/28/2020
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Network
3D Convolution Neural Networks (CNNs) have been widely applied to 3D sce...
research
∙
03/18/2020
Detecting Pancreatic Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancer...
research
∙
12/06/2019
Deep Distance Transform for Tubular Structure Segmentation in CT Scans
Tubular structure segmentation in medical images, e.g., segmenting vesse...
research
∙
09/10/2019
UPC: Learning Universal Physical Camouflage Attacks on Object Detectors
In this paper, we study physical adversarial attacks on object detectors...
research
∙
09/03/2019
Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancer...
research
∙
08/21/2019
FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans
Automatic abnormality detection in abdominal CT scans can help doctors i...
research
∙
06/23/2019
Multi-Scale Attentional Network for Multi-Focal Segmentation of Active Bleed after Pelvic Fractures
Trauma is the worldwide leading cause of death and disability in those y...
research
∙
04/12/2019
Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation
Accurate multi-organ abdominal CT segmentation is essential to many clin...
research
∙
01/30/2019
Adversarial Metric Attack for Person Re-identification
Person re-identification (re-ID) has attracted much attention recently d...
research
∙
12/09/2018
Learning Transferable Adversarial Examples via Ghost Networks
The recent development of adversarial attack has proven that ensemble-ba...
research
∙
04/23/2018
Abdominal multi-organ segmentation with organ-attention networks and statistical fusion
Accurate and robust segmentation of abdominal organs on CT is essential ...
research
∙
04/07/2018
Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound
Deep convolutional neural networks (CNNs), especially fully convolutiona...
research
∙
04/07/2018
Semi-supervised multi-organ segmentation via multi-planar co-training
Multi-organ segmentation is a critical problem in medical image analysis...
research
∙
03/19/2018
Improving Transferability of Adversarial Examples with Input Diversity
Though convolutional neural networks have achieved state-of-the-art perf...
research
∙
11/13/2017
Visual Concepts and Compositional Voting
It is very attractive to formulate vision in terms of pattern theory Mum...
research
∙
09/13/2017
Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation
We aim at segmenting small organs (e.g., the pancreas) from abdominal CT...
research
∙
06/22/2017
Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans
Automatic segmentation of an organ and its cystic region is a prerequisi...
research
∙
03/24/2017
Adversarial Examples for Semantic Segmentation and Object Detection
It has been well demonstrated that adversarial examples, i.e., natural i...
research
∙
12/25/2016
