SPECIAL SESSION SUBMISSION INSTRUCTION

Interested in proposing a special session for the IWAIT-IFMIA 2019 in Singapore?

If you wish to solicit participants, post a call for papers now. The organizing committee can accept proposal for Special Session from authors that want to contribute to IWAIT-IFMIA 2019. There are two steps to submit a Special Session as follows;

  • Step 1: Before going to submit your special session proposal, please register in which special session you are interested.
  • Step 2: Please go to EasyChair Submission System to submit special session.
  • Step 3: Please send an email (mailto: iwait2020@umn.ac.id ), to confirm your participation.

List of Special Sessions

Special Session 1: Multidisciplinary Computational Anatomy

Session Chair: Yoshinobu Sato and Hidekata Hontani ​

Presentations

  1. Registration between Histopathological Images with Different Stains and an MRI Image of Pancreatic Cancer Tumor” (#248) Presenter(s):​ Prof Hidekata Hontani (Nagoya Institute of Technology)​​
  2. Unsupervised and Semi-Supervised Learning for Efficient Opacity Annotation of Diffuse Lung Diseases” (#249) Presenter(s):​ Dr Shingo Mabu (Yamaguchi University)​​​
  3. Multiscale and Functional Modeling of Musculoskeletal System for Diagnosis, Surgical Planning and Prognostic Assessment in Orthopedic Surgery” (#250) Presenter(s):​ Assoc Prof Yoshito Otake ​(Nara Institute of Science and Technology)​​
  4. Spatiotemporal Statistical Models of a Human Embryo” (#253) Presenter(s):​ Dr Atsushi Saito (Tokyo University of Agriculture and Technology)​​​
  5. Automated Detection of Abnormal Tumors on FDG-PET/CT Images Based on Anatomical Standardization using Organ Segmentations by Deep Learning” (#257) Presenter(s):​  Assoc Prof Takeshi Hara (Gifu University)​​

Special Session 2: Advanced Image Reconstruction Methods in Tomographic Medical Imaging

Session Chair(s): Jinah Park

Organizer: KAIST Special Interest Group on Future Emerging Technology of Medical Imaging

Invited Talk

Deep Learning-Based CBCT and PseudoCT Reconstruction for Practical Radiation Therapy” (#178) Presenter(s):​ TBA​

Presentations

  1. Metal Artifact Reduction of Dental CT using Optical Image of Dental Plaster Model” (#255) Presenter(s):​ Yejin Kim, Jihoon Cho (KAIST)​​
  2. MR Thermometry with Dual Echo bSSFP” (#259) Presenter(s):​ Seohee So (KAIST)​​​
  3. Arterial Spin Labeling MRI with Radiofrequency Pulse Modulation and Fourier Analysis” (#258) Presenter(s):​ Hyo-Im Heo (KAIST)​​​
  4. Deep Recurrent Learning of Brain Functional Dynamics for Data-driven Classification of Psychiatric Diagnosis: The Temporal Template Network” (#258) Presenter(s):​ Byung-Hoon Kim (KAIST)

Special Session 3: 

Deep Learning in Medical Imaging 1

Session Chairs: Hiroshi Fujita and Jong Hyo Kim

Invited Talks

  1. Data Enhancement of Deep Learning for Thoracic Imaging” (#148) Presenter(s):​ Prof Shoji Kido​ (Yamaguchi University​)
  2. A Two-stage Organ Classification Network based on 3D ResNet in Chest CT“(#190)​ Presenter(s): TBA
  3. Automated Breast Ultrasound Computer-Aided Diagnosis Using 3-D Convolutional Neural Network” (#137) Presenter(s): Prof Ruey-Feng Chang​ (National Cheng Kung University)
  4. Detection of Paroxysmal Atrial Fibrillation by Lorenz Plot Imaging of ECG R-R intervals” (#216)
    Presenter(s): Prof Junichiro Hayano (Nagoya City University)

Presentations

  1. ​​”Deep Learning for Breast Cancer Classification with Mammography” (#142) Presenter(s):​ Wei-Tse Yang (National Cheng Kung University)​​​​​​​​​​​​

Deep Learning in Medical Imaging 2
Session Chairs: Hee-Joung Kim and Shoji Kido​

Presentations

  1. ​​”A Tissue Classification Method of IVOCT Images Using Rectangle Region Cropped along The Circumferential Direction Based on Deep Learning” (#158) Presenter(s):​ Dr Xinbo Ren (Wakayama University​)​​​​​​​​​​​​
  2. ​”Deep Learning based Fully-Automated Segmentation of Abdominal Organs from CT Images” (#166) Presenter(s):​ Jieun Kim (University of Ulsan College of Medicine)
  3. Automatic Liver Segmentation with CT images based on 3D U-net Deep Learning Approach​” (#125) Presenter(s):​ Ting-Yu Su (National Cheng Kung University)​
  4. Noise Reduction Methods in Low-dose CT Data Combining Neural Networks and an Iterative Reconstruction Technique” (#230) Presenter(s):​ Dahim Choi (Ewha Womans University)
  5. Deep Convolutional Neural Network-Based Automated Lesion Detection in Wireless Capsule Endoscopy” (#238) Presenter(s):​ Yejin Jeon (Ewha Womans University)​
  6. Deep Learning in Medical Imaging: Engineer’s Point of View” (#256) Presenter(s):​ June-Goo Lee (Asan Medical Center)
  7. Deep Learning in Medical Imaging and Radiology: Radiologist’s View” (#263) Presenter(s):​ Chang Min Park​