Search Results for author: Adam P. Harrison

Found 44 papers, 7 papers with code

Localized Adversarial Domain Generalization

1 code implementation9 May 2022 Wei Zhu, Le Lu, Jing Xiao, Mei Han, Jiebo Luo, Adam P. Harrison

Adversarial domain generalization is a popular approach to DG, but conventional approaches (1) struggle to sufficiently align features so that local neighborhoods are mixed across domains; and (2) can suffer from feature space over collapse which can threaten generalization performance.

Domain Generalization

A deep learning pipeline for localization, differentiation, and uncertainty estimation of liver lesions using multi-phasic and multi-sequence MRI

no code implementations17 Oct 2021 Peng Wang, YuHsuan Wu, Bolin Lai, Xiao-Yun Zhou, Le Lu, Wendi Liu, Huabang Zhou, Lingyun Huang, Jing Xiao, Adam P. Harrison, Ningyang Jia, Heping Hu

Results: the proposed CAD solution achieves a mean F1 score of 0. 62, outperforming the abdominal radiologist (0. 47), matching the junior hepatology radiologist (0. 61), and underperforming the senior hepatology radiologist (0. 68).

Accurate and Generalizable Quantitative Scoring of Liver Steatosis from Ultrasound Images via Scalable Deep Learning

no code implementations12 Oct 2021 Bowen Li, Dar-In Tai, Ke Yan, Yi-Cheng Chen, Shiu-Feng Huang, Tse-Hwa Hsu, Wan-Ting Yu, Jing Xiao, Le Lu, Adam P. Harrison

High diagnostic performance was observed across all viewpoints: area under the curves of the ROC to classify >=mild, >=moderate, =severe steatosis grades were 0. 85, 0. 90, and 0. 93, respectively.

A Flexible Three-Dimensional Hetero-phase Computed Tomography Hepatocellular Carcinoma (HCC) Detection Algorithm for Generalizable and Practical HCC Screening

no code implementations17 Aug 2021 Chi-Tung Cheng, Jinzheng Cai, Wei Teng, Youjing Zheng, YuTing Huang, Yu-Chao Wang, Chien-Wei Peng, YouBao Tang, Wei-Chen Lee, Ta-Sen Yeh, Jing Xiao, Le Lu, Chien-Hung Liao, Adam P. Harrison

We develop a flexible three-dimensional deep algorithm, called hetero-phase volumetric detection (HPVD), that can accept any combination of contrast-phase inputs and with adjustable sensitivity depending on the clinical purpose.

Computed Tomography (CT)

Learning from Subjective Ratings Using Auto-Decoded Deep Latent Embeddings

no code implementations12 Apr 2021 Bowen Li, Xinping Ren, Ke Yan, Le Lu, Lingyun Huang, Guotong Xie, Jing Xiao, Dar-In Tai, Adam P. Harrison

Importantly, ADDLE does not expect multiple raters per image in training, meaning it can readily learn from data mined from hospital archives.

Deep Implicit Statistical Shape Models for 3D Medical Image Delineation

1 code implementation7 Apr 2021 Ashwin Raju, Shun Miao, Dakai Jin, Le Lu, Junzhou Huang, Adam P. Harrison

DISSMs use a deep implicit surface representation to produce a compact and descriptive shape latent space that permits statistical models of anatomical variance.

Liver Segmentation Pose Estimation

Hetero-Modal Learning and Expansive Consistency Constraints for Semi-Supervised Detection from Multi-Sequence Data

no code implementations24 Mar 2021 Bolin Lai, YuHsuan Wu, Xiao-Yun Zhou, Peng Wang, Le Lu, Lingyun Huang, Mei Han, Jing Xiao, Heping Hu, Adam P. Harrison

Lesion detection serves a critical role in early diagnosis and has been well explored in recent years due to methodological advancesand increased data availability.

Lesion Detection

Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies

1 code implementation CVPR 2021 Jinzheng Cai, YouBao Tang, Ke Yan, Adam P. Harrison, Jing Xiao, Gigin Lin, Le Lu

In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals.

14 3D Object Tracking

Contour Transformer Network for One-shot Segmentation of Anatomical Structures

1 code implementation2 Dec 2020 Yuhang Lu, Kang Zheng, Weijian Li, Yirui Wang, Adam P. Harrison, ChiHung Lin, Song Wang, Jing Xiao, Le Lu, Chang-Fu Kuo, Shun Miao

In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism.

One-Shot Learning One-Shot Segmentation

Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT

1 code implementation5 Sep 2020 Ke Yan, Jinzheng Cai, Youjing Zheng, Adam P. Harrison, Dakai Jin, YouBao Tang, Yuxing Tang, Lingyun Huang, Jing Xiao, Le Lu

For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations).

Lesion Detection Transfer Learning

Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression

no code implementations30 Aug 2020 Jinzheng Cai, Ke Yan, Chi-Tung Cheng, Jing Xiao, Chien-Hung Liao, Le Lu, Adam P. Harrison

Identifying, measuring and reporting lesions accurately and comprehensively from patient CT scans are important yet time-consuming procedures for physicians.

Lesion Detection

Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network

no code implementations29 Aug 2020 Chun-Hung Chao, Zhuotun Zhu, Dazhou Guo, Ke Yan, Tsung-Ying Ho, Jinzheng Cai, Adam P. Harrison, Xianghua Ye, Jing Xiao, Alan Yuille, Min Sun, Le Lu, Dakai Jin

Specifically, we first utilize a 3D convolutional neural network with ROI-pooling to extract the GTV$_{LN}$'s instance-wise appearance features.

Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images

no code implementations ECCV 2020 Haomin Chen, Yirui Wang, Kang Zheng, Weijian Li, Chi-Tung Cheng, Adam P. Harrison, Jing Xiao, Gregory D. Hager, Le Lu, Chien-Hung Liao, Shun Miao

A new contrastive feature learning component in our Siamese network is designed to optimize the deep image features being more salient corresponding to the underlying semantic asymmetries (caused by pelvic fracture occurrences).

Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets

no code implementations28 May 2020 Ke Yan, Jinzheng Cai, Adam P. Harrison, Dakai Jin, Jing Xiao, Le Lu

First, we learn a multi-head multi-task lesion detector using all datasets and generate lesion proposals on DeepLesion.

Ranked #3 on Medical Object Detection on DeepLesion (using extra training data)

Lesion Detection Medical Object Detection +1

Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation

no code implementations ECCV 2020 Ashwin Raju, Chi-Tung Cheng, Yunakai Huo, Jinzheng Cai, Junzhou Huang, Jing Xiao, Le Lu, ChienHuang Liao, Adam P. Harrison

In medical imaging, organ/pathology segmentation models trained on current publicly available and fully-annotated datasets usually do not well-represent the heterogeneous modalities, phases, pathologies, and clinical scenarios encountered in real environments.

Computed Tomography (CT) Domain Adaptation +1

Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification

no code implementations27 May 2020 Zhuotun Zhu, Ke Yan, Dakai Jin, Jinzheng Cai, Tsung-Ying Ho, Adam P. Harrison, Dazhou Guo, Chun-Hung Chao, Xianghua Ye, Jing Xiao, Alan Yuille, Le Lu

We focus on the detection and segmentation of oncology-significant (or suspicious cancer metastasized) lymph nodes (OSLNs), which has not been studied before as a computational task.

JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-Modal Image Alignment of Large-scale Pathological CT Scans

no code implementations ECCV 2020 Fengze Liu, Jingzheng Cai, Yuankai Huo, Chi-Tung Cheng, Ashwin Raju, Dakai Jin, Jing Xiao, Alan Yuille, Le Lu, Chien-Hung Liao, Adam P. Harrison

We extensively evaluate our JSSR system on a large-scale medical image dataset containing 1, 485 patient CT imaging studies of four different phases (i. e., 5, 940 3D CT scans with pathological livers) on the registration, segmentation and synthesis tasks.

Image Registration Multi-Task Learning +1

Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search

no code implementations CVPR 2020 Dazhou Guo, Dakai Jin, Zhuotun Zhu, Tsung-Ying Ho, Adam P. Harrison, Chun-Hung Chao, Jing Xiao, Alan Yuille, Chien-Yu Lin, Le Lu

This is the goal of our work, where we introduce stratified organ at risk segmentation (SOARS), an approach that stratifies OARs into anchor, mid-level, and small & hard (S&H) categories.

Neural Architecture Search

Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale

1 code implementation21 Jan 2020 Jinzheng Cai, Adam P. Harrison, Youjing Zheng, Ke Yan, Yuankai Huo, Jing Xiao, Lin Yang, Le Lu

This is the goal of our work, where we develop a powerful system to harvest missing lesions from the DeepLesion dataset at high precision.

CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT

no code implementations5 Sep 2019 Bo Zhou, Adam P. Harrison, Jiawen Yao, Chi-Tung Cheng, Jing Xiao, Chien-Hung Liao, Le Lu

This is the focus of our work, where we present a principled data curation tool to extract multi-phase CT liver studies and identify each scan's phase from a real-world and heterogenous hospital PACS dataset.

Deep Esophageal Clinical Target Volume Delineation using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk

no code implementations4 Sep 2019 Dakai Jin, Dazhou Guo, Tsung-Ying Ho, Adam P. Harrison, Jing Xiao, Chen-Kan Tseng, Le Lu

Clinical target volume (CTV) delineation from radiotherapy computed tomography (RTCT) images is used to define the treatment areas containing the gross tumor volume (GTV) and/or sub-clinical malignant disease for radiotherapy (RT).

Data Augmentation

Weakly Supervised Universal Fracture Detection in Pelvic X-rays

no code implementations4 Sep 2019 Yirui Wang, Le Lu, Chi-Tung Cheng, Dakai Jin, Adam P. Harrison, Jing Xiao, Chien-Hung Liao, Shun Miao

In this paper, we propose a two-stage hip and pelvic fracture detection method that executes localized fracture classification using weakly supervised ROI mining.

Multiple Instance Learning

Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs

no code implementations19 Jul 2018 Yu-Xing Tang, Xiaosong Wang, Adam P. Harrison, Le Lu, Jing Xiao, Ronald M. Summers

In addition, highly confident samples (measured by classification probabilities) and their corresponding class-conditional heatmaps (generated by the CNN) are extracted and further fed into the AGCL framework to guide the learning of more distinctive convolutional features in the next iteration.

14 Classification +2

CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement

no code implementations18 Jul 2018 Youbao Tang, Jinzheng Cai, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers

The first GAN reduces the noise in the CT image and the second GAN generates a higher resolution image with enhanced boundaries and high contrast.

Computed Tomography (CT) Image Enhancement +2

Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays

no code implementations3 Jul 2018 Jinzheng Cai, Le Lu, Adam P. Harrison, Xiaoshuang Shi, Pingjun Chen, Lin Yang

Given image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images.

General Classification Image Classification

Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks

no code implementations25 Jun 2018 Youbao Tang, Adam P. Harrison, Mohammadhadi Bagheri, Jing Xiao, Ronald M. Summers

Response evaluation criteria in solid tumors (RECIST) is the standard measurement for tumor extent to evaluate treatment responses in cancer patients.

Multi-Task Learning

White matter hyperintensity segmentation from T1 and FLAIR images using fully convolutional neural networks enhanced with residual connections

no code implementations19 Mar 2018 Dakai Jin, Ziyue Xu, Adam P. Harrison, Daniel J. Mollura

Segmentation and quantification of white matter hyperintensities (WMHs) are of great importance in studying and understanding various neurological and geriatric disorders.

Random Hinge Forest for Differentiable Learning

1 code implementation12 Feb 2018 Nathan Lay, Adam P. Harrison, Sharon Schreiber, Gitesh Dawer, Adrian Barbu

We propose random hinge forests, a simple, efficient, and novel variant of decision forests.

Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST

no code implementations25 Jan 2018 Jinzheng Cai, You-Bao Tang, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers

Toward this end, we introduce a convolutional neural network based weakly supervised self-paced segmentation (WSSS) method to 1) generate the initial lesion segmentation on the axial RECIST-slice; 2) learn the data distribution on RECIST-slices; 3) adapt to segment the whole volume slice by slice to finally obtain a volumetric segmentation.

Lesion Segmentation Super-Resolution

Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images

no code implementations12 Jun 2017 Adam P. Harrison, Ziyue Xu, Kevin George, Le Lu, Ronald M. Summers, Daniel J. Mollura

Pathological lung segmentation (PLS) is an important, yet challenging, medical image application due to the wide variability of pathological lung appearance and shape.

Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling

no code implementations19 Jan 2017 Mingchen Gao, Ziyue Xu, Le Lu, Adam P. Harrison, Ronald M. Summers, Daniel J. Mollura

Accurately predicting and detecting interstitial lung disease (ILD) patterns given any computed tomography (CT) slice without any pre-processing prerequisites, such as manually delineated regions of interest (ROIs), is a clinically desirable, yet challenging goal.

Computed Tomography (CT) Multi-Label Learning +1

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