Browsing by author "Bi, Lei"
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An Automated and Robust Tumours Detection and Segmentation Framework for Whole-Body PET-CT Studies
Bi, LeiPublished 2013-05-24A dual-modality positron emission tomography – computed tomography (PET-CT) is one of widely used medical imaging system. It combines functional (from PET) with anatomical (from CT) information, in a co-aligned space, ...USyd AccessMasters Thesis -
Automated thresholded region classification using a robust feature selection method for PET-CT
Bi, Lei; Kim, Jinman; Wen, Lingfeng; Feng, Dagan; Fulham, MichaelPublished 2015-07-23Fluorodeoxyglucose Positron Emission Tomography - Computed Tomography (FDG PET-CT) is the preferred imaging modality for staging the lymphomas. Sites of disease usually appear as foci of increased FDG uptake. Thresholding ...Open AccessConference paper -
Automatic Detection and Classification of Regions of FDG Uptake in Whole-Body PET-CT Lymphoma Studies
Bi, Lei; Kim, Jinman; Kuman, Ashnil; Wen, Lingfeng; Feng, Dagan; Fulham, MichaelPublished 2017-09-01Open AccessArticle -
Classification of thresholded regions based on selective use of PET, CT and PET-CT image features
Bi, Lei; Kim, Jinman; Feng, Dagan; Fulham, MichaelPublished 2014-11-06Fluorodeoxyglucose positron emission tomography - computed tomography (FDG PET-CT) is the preferred image modality for lymphoma diagnosis. Sites of disease generally appear as foci of increased FDG uptake. Thresholding ...Open AccessConference paper -
Deep Cascaded Fully Convolutional Networks for Medical Image Segmentation
Bi, LeiPublished 2018-03-31Segmentation of regions of interest (ROIs) in medical images is an important step for image analysis in computer aided diagnosis (CAD) systems. Recently, deep learning methods based on fully convolutional networks (FCN) ...USyd AccessPhD Doctorate -
Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks
Bi, Lei; Kim, Jinman; Ahn, Euijoon; Kumar, Ashnil; Fulham, Michael; Feng, DaganPublished 2017-06-07Open AccessArticle -
Dual-Path Adversarial Learning for Fully Convolutional Network (FCN)-Based Medical Image Segmentation
Bi, Lei; Feng, David Dagan; Kim, JinmanPublished 2018-01-01Segmentation of regions of interest (ROIs) in medical images is an important step for image analysis in computer-aided diagnosis systems. In recent years, segmentation methods based on fully convolutional networks (FCNs) ...Open AccessArticle -
Improving Skin Lesion Segmentation via Stacked Adversarial Learning
Bi, Lei; Feng, Dagan; Fulham, Michael; Kim, JinmanPublished 2019-01-01Segmentation of skin lesions is an essential step in computer aided diagnosis (CAD) for the automated melanoma diagnosis. Recently, segmentation methods based on fully convolutional networks (FCNs) have achieved great ...Open AccessConference paper -
Multi-Label Classification of Multi-Modality Skin Lesion via Hyper-Connected Convolutional Neural Network
Bi, Lei; Feng, David Dagan; Fulham, Michael; Kim, JinmanPublished 2020-01-01Objective: Clinical and dermoscopy images (multi-modality image pairs) are routinely used sequentially in the assessment of skin lesions. Clinical images characterize a lesion’s geometry and color; dermoscopy depicts ...EmbargoedArticle -
Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images
Ahn, Euijoon; Kim, Jinman; Bi, Lei; Kumar, Ashnil; Li, Changyang; Fulham, Michael; Feng, DaganPublished 2017-01-16Open AccessArticle -
Stacked fully convolutional networks with multi-channel learning: application to medical image segmentation
Bi, Lei; Kim, Jinman; Kumar, Ashnil; Fulham, Michael; Feng, DaganPublished 2017-05-04The automated segmentation of regions of interest (ROIs) in medical imaging is the fundamental requirement for the derivation of high-level semantics for image analysis in clinical decision support systems. Traditional ...Open AccessArticle -
Step-wise Integration of Deep Class-specific Learning for Dermoscopic Image Segmentation
Bi, Lei; Kim, Jinman; Ahn, Euijoon; Kumar, Ashnil; Feng, Dagan; Fulham, MichaelPublished 2018-01-01The segmentation of abnormal regions on dermoscopic images is an important step for automated computer aided diagnosis (CAD) of skin lesions. Recent methods based on fully convolutional networks (FCN) have been very ...Open AccessArticle
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