Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II
Article
This work considers supervised learning to count from images and their corresponding point annotations. Where density-based counting methods typically use the point annotations only to create Gaussian-density ...
Article
Magnetic resonance (MR) and computer tomography (CT) imaging are valuable tools for diagnosing diseases and planning treatment. However, limitations such as radiation exposure and cost can restrict access to c...
Article
Each year, underwater remotely operated vehicles (ROVs) collect thousands of hours of video of unexplored ocean habitats revealing a plethora of information regarding biodiversity on Earth. However, fully util...
Article
Emergencies require various emergency departments to collaborate to achieve timely and effective emergency responses. Thus, the overall performance of emergency response is influenced not only by the efficienc...
Article
The medical literature is the most important way to demonstrate academic achievements and academic exchanges. Massive medical literature has become a huge treasure trove of knowledge. It is necessary to automa...
Article
The problem of fine-grained sketch-based image retrieval (FG-SBIR) is defined and investigated in this paper. In FG-SBIR, free-hand human sketch images are used as queries to retrieve photo images containing t...
Chapter and Conference Paper
Reconstructing magnetic resonance (MR) images from under-sampled data is a challenging problem due to various artifacts introduced by the under-sampling operation. Recent deep learning-based methods for MR ima...
Chapter and Conference Paper
Lossless image compression is an important technique for image storage and transmission when information loss is not allowed. With the fast development of deep learning techniques, deep neural networks have be...
Chapter and Conference Paper
Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas for patients with malignant gliomas in neuro-oncology with the help of conventional an...
Chapter and Conference Paper
Deep Neural Networks (or DNNs) must constantly cope with distribution changes in the input data when the task of interest or the data collection protocol changes. Retraining a network from scratch to combat th...
Chapter and Conference Paper
Accurate hepatic vessel segmentation and registration using ultrasound (US) can contribute to beneficial navigation during hepatic surgery. However, it is challenging due to noise and speckle in US imaging and...
Chapter and Conference Paper
During fetoscopic laser photocoagulation, a treatment for twin-to-twin transfusion syndrome (TTTS), the clinician first identifies abnormal placental vascular connections and laser ablates them to regulate blo...
Article
The large domain discrepancy between faces captured in polarimetric (or conventional) thermal and visible domains makes cross-domain face verification a highly challenging problem for human examiners as well a...
Book and Conference Proceedings
22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II
Book and Conference Proceedings
22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part I
Book and Conference Proceedings
22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI
Book and Conference Proceedings
22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V
Book and Conference Proceedings
22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV
Chapter and Conference Paper
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers...
Chapter and Conference Paper
Deep learning techniques are often criticized to heavily depend on a large quantity of labeled data. This problem is even more challenging in medical image analysis where the annotator expertise is often scar...