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08:00 – 08:10 Registration
08:10 – 08:20 Opening Remarks


08:20 – 09:05 Plenary Talk

♣  Dr. Dinggang Shen, “Full-Stack, Full-Spectrum AI in Medical Imaging

09:05 – 09:50 Plenary Talk

♣  Dr. Hervé Delingette, “From Data-driven to Biophysics-based AI in Medical Image Analysis”


09:50 – 10:00 Coffee Break
10:00 – 11:00 Session 1: Computer-Aided Detection/Diagnosis

Session ChairDr. Mingxia Liu and Dr. Qingyu Zhao

[MLMI-O-1]     10:00~10:15     Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI

[MLMI-O-2]     10:15~10:30     Learning-based Bone Quality Classification Method for Spinal Metastasis

[MLMI-O-3]     10:30~10:45     Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection

[MLMI-O-4]     10:45~11:00     Lesion Detection with Deep Aggregated 3D Contextual Feature and Auxiliary Information


11:00 – 12:00 Poster Session (can be posted until the late afternoon)

[MLMI-P-1]    Unsupervised Conditional Consensus Adversarial Network for Brain Disease Identification with Structural MRI

[MLMI-P-2]    Semantic filtering through deep source separation on microscopy images

[MLMI-P-3]    FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans

[MLMI-P-4]    Detecting Lesion Bounding Ellipses with Gaussian Proposal Networks

[MLMI-P-5]    Relu cascade of feature pyramid networks for CT pulmonary nodule detection

[MLMI-P-6]    Joint Localization of Optic Disc and Fovea in Ultra-Widefield Fundus Images

[MLMI-P-7]    Reinforced Transformer for Medical Image Captioning

[MLMI-P-8]    MSAFusionNet: Multiple Subspace Attention Based Deep Multi-modal Fusion Network

[MLMI-P-9]    Ultrasound Liver Fibrosis Diagnosis using Multi-indicator guided Deep Neural Networks

[MLMI-P-10]  Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI

[MLMI-P-11]  BOLD fMRI-based Brain Perfusion Prediction Using Deep Dilated Wide Activation Networks

[MLMI-P-12]  Adaptive Functional Connectivity Network using Parallel Hierarchical BiLSTM for MCI Diagnosis

[MLMI-P-13]  Multi Task Convolutional Neural Network for Joint Bone Age Assessment and Ossification Center Detection from Hand Radiograph

[MLMI-P-14]  Spatial Regularized Classification Network for Spinal Dislocation Diagnosis

[MLMI-P-15]  GFD Faster R-CNN: Gabor Fractal DenseNet Faster R-CNN for automatic detection of esophageal abnormalities in endoscopic images

[MLMI-P-16]  A Relation Hashing Network Embedded with Prior Features for Skin Lesion Classification

[MLMI-P-17]  Semi-Supervised Multi-Task Learning with Chest X-Ray Images

[MLMI-P-18]  Novel Bi-directional Images Synthesis based on WGAN-GP with GMM-based Noise Generation

[MLMI-P-19]  Joint Shape Representation and Classification for Detecting PDAC

[MLMI-P-20]  Detecting abnormalities in resting-state dynamics: An unsupervised learning approach

[MLMI-P-21]  A Hybrid Multi-atrous and Multi-scale Network for Liver Lesion Detection

[MLMI-P-22]  Renal Cell Carcinoma Staging with Learnable Image Histogram-based Deep Neural Network

[MLMI-P-23]  Gated Recurrent Neural Networks for Accelerated Ventilation MRI

[MLMI-P-24]  A Cascaded Multi-Modality Analysis in Mild Cognitive Impairment

[MLMI-P-25]  An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis

[MLMI-P-26]  LSTMs and resting-state fMRI for classification and understanding of Parkinson’s disease

[MLMI-P-27]  Deep learning model integrating dilated convolution and deep supervision for brain tumor segmentation in multi-parametric MRI

[MLMI-P-28]  Brain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization

[MLMI-P-29]  Automated Segmentation of Skin Lesion Based on Pyramid Attention Network

[MLMI-P-30]  Privacy-preserving Federated Brain Tumour Segmentation

[MLMI-P-31]  Children’s Neuroblastoma Segmentation using Morphological Features

[MLMI-P-32]  Deep Active Lesion Segmentation

[MLMI-P-33]  Automatic Couinaud Segmentation from CT Volumes on Liver Using GLC-Unet

[MLMI-P-34]  Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation

[MLMI-P-35]  Learn to Step-wise Focus on Targets for Biomedical Image Segmentation

[MLMI-P-36]  Weakly Supervised Learning Strategy for Lung Defect Segmentation

[MLMI-P-37]  A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs

[MLMI-P-38]  High- and Low-Level Feature Enhancement for Medical Image Segmentation

[MLMI-P-39]  Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation

[MLMI-P-40]  Tree-LSTM: Using LSTM to Encode Memory in Anatomical Tree Prediction from 3D Images

[MLMI-P-41]  Automatic Rodent Brain MRI Lesion Segmentation with Fully Convolutional Networks

[MLMI-P-42]  Deep Residual Learning for Instrument Segmentation in Robotic Surgery

[MLMI-P-43]  Advancing Pancreas Segmentation in Multi-protocol MRI Volumes using Hausdorff-Sine Loss Function

[MLMI-P-44]  Biomedical Image Segmentation by Retina-like Sequential Attention Mechanism Using Only A Few Training Images

[MLMI-P-45]  Unsupervised Lesion Detection with Locally Gaussian Approximation

[MLMI-P-46]  Infant Brain Deformable Registration Using Global and Local Label-Driven Deep Regression Learning

[MLMI-P-47]  Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection

[MLMI-P-48]  Conv2Warp: An unsupervised deformable image registration with continuous convolution and warping

[MLMI-P-49]  FAIM-A ConvNet Method for Unsupervised 3D Medical Image Registration

[MLMI-P-50]  Pseudo-labeled bootstrapping and multi-stage transfer learning for the classification and localization of dysplasia in Barrett’s Esophagus

[MLMI-P-51]  Correspondence-Steered Volumetric Descriptor Learning Using Deep Functional Maps

[MLMI-P-52]  Dense-residual Attention Network for Skin Lesion Segmentation

[MLMI-P-53]  A Maximum Entropy Deep Reinforcement Learning Neural Tracker

[MLMI-P-54]  Multi-Scale Attentional Network for Multi-Focal Segmentation of Active Bleed after Pelvic Fractures

[MLMI-P-55]  Joint Holographic Detection and Reconstruction

[MLMI-P-56]  Weakly Supervised Segmentation by a Deep Geodesic Prior


12:00 – 13:00 Lunch
13:00 – 14:30 Session 2: Medical Image Segmentation

Session Chair: Dr. Heung-Il Suk and Dr. Jaeil Kim

[MLMI-O-5]     13:00~13:15     End-to-End Adversarial Shape Learning for Abdominal Organ Segmentation

[MLMI-O-6]     13:15~13:30     Boundary Aware Networks for Medical Image Segmentation

[MLMI-O-7]     13:30~13:45     Weakly Supervised Confidence Learning for Brain MR Image Dense Parcellation

[MLMI-O-8]     13:45~14:00     Lesion Detection by Efficiently Bridging 3D Context

[MLMI-O-9]     14:00~14:15     Cross-Modal Attention-Guided Convolutional Network for Multi-Modal Cardiac Segmentation

[MLMI-O-10]   14:15~14:30     Automatic Fetal Brain Extraction Using Multi-Stage U-Net with Deep Supervision


14:30 – 14:40 Coffee Break
14:40 – 16:10 Session 3: Registration and Reconstruction

Session Chair: Dr. Pingkun Yan and Dr. Marleen de Bruijne

[MLMI-O-11]   14:40~14:55     Communal Domain Metric Learning for Registration in Drifted Image Spaces

[MLMI-O-12]   14:55~15:10     Morphological Simplification of Brain MR Images by Deep Learning for Facilitating Deformable Registration

[MLMI-O-13]   15:10~15:25     Residual Attention Generative Adversarial Networks for Nuclei Detection on Routine Colon Cancer Histology Images

[MLMI-O-14]   15:25~15:40     Anatomy-Aware Self-supervised Fetal MRI Synthesis from Unpaired Ultrasound Images

[MLMI-O-15]   15:40~15:55     Select, Attend, and Transfer: Light, Learnable Skip Connections

[MLMI-O-16]   15:55~16:10     Confounder-Aware Visualization of ConvNets


16:10 – 16:20 Coffee Break
16:20 – 17:50 Session 4: Automated Medical Image Analysis

Session Chair: Dr. Jaeil Kim and Dr. Ziyue Xu

[MLMI-O-17]   16:20~16:35     DCCL: A Benchmark for Cervical Cytology Analysis

[MLMI-O-18]   16:35~16:50     WSI-Net: Branch-based and Hierarchy-aware Network for Segmentation and Classification of Breast Histopathological Whole-slide Images

[MLMI-O-19]   16:50~17:05     Globally-Aware Multiple Instance Classifier for Breast Cancer Screening

[MLMI-O-20]   17:05~17:20     Smartphone-Supported Malaria Diagnosis Based on Deep Learning

[MLMI-O-21]   17:20~17:35     Multi-Template based Auto-weighted Adaptive Structural Learning for ASD Diagnosis

[MLMI-O-22]   17:35~17:50     Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation


17:50 – 18:00 Closing Remarks (Best papers will be announced)