Computer Vision in Biomedical Engineering: Spring 2019
UPDATE: All students must complete your presentations by Week 16. The presentation should be based on recently published top-tier journal paper. Please contact TA (jiaoyining AT sjtu.edu.cn) to check whether your selection is qualified. You need finalize your talk information at least one week in advance of your schedule.
欧阳熹：Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
霍加宇：FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction
牛京阳：Radiomic features and multilayer perceptron network classifier: a robust MRI classification strategy for distinguishing glioblastoma from primary central nervous system lymphoma
陈黎云：Learning Correspondence from the Cycle-consistency of Time
JOEL DZIDZORVI KWAME DISU: 1. Machine and deep learning for workflow recognition during surgery; 2. A Machine Learning Approach to Predicting Case Duration for Robot-Assisted Surgery
CLINTON ELIAN GANDANA: Needle in a haystack: Interactive surgical instrument recognition through perception and manipulation
彭丹卉：MULTIMODAL IMAGE DENOISING BASED ON COUPLED DICTIONARY LEARNING
张健源：HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs
陈硕：DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction
周明：Feature Selective Anchor-Free Module for Single-Shot Object Detection
叶倩倩：Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
张梦吉：Kervolutional Neural Networks
郭有容：Deep Group-shuffling Random Walk for Person Re-identification
施政恺：Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss
许晓薇：Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network
王晗：Averaging Weights Leads to Wider Optima and Better Generalization
周美宣：Deep Depth Completion of a Single RGB-D Image
徐子薇：Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
ZAWAR KHAN KHATTAK：Spatio-spectral Representation Learning for Electroencephalographic Gait-Pattern Classification
VALENTIN DILEK：First Insights on a Passive Major Depressive Disorder Prediction System with Incorporated Conversational Chatbot
JAMIRAH BINT AHMED：Deep Evolutionary Networks with Expedited Genetic Algorithm for Medical Image Denoising
MEHMOONA AZMAT：Optimal deep learning model for classification of lung cancer on CT images
赵旺源：Deformable Image Registration Using Functions of Bounded Deformation
李雯丽：Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning
李钧：nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
刘一明：End-to-end Adversarial Retinal Image Synthesis
葛艺蒙：An Unsupervised Learning Model for Deformable Medical Image Registration
徐健玮：CornerNet: Detecting Objects as Paired Keypoints
UPDATE: The final exam will be on Week 17 (June 19).
Briefly speaking, the purpose of computer vision is to let computers act like human beings in terms of visual perception. In the other word, computers are expected to visualize and understand this world via images, videos, etc. Computer vision has moved forward significantly during past decades. Its accomplishments are now widely applied to numerous areas including biomedical engineering. This course shall introduce basic theories of computer vision, as well as its classical solutions. Also, applications related with biomedical engineering shall be the focus in this course, for the sake of revealing the miracles of computer vision.
Note: This 3-credit graduate-level course is taught every spring semester and in English. All course materials are hosted through Jbox.
人工智能和医学（AI & Medicine）：2018春季学期
Note: All course materials are hosted through Jbox. This course will be moved to the spring semester since 2018.