Recalibrating 3D ConvNets With Project & Excite
نویسندگان
چکیده
منابع مشابه
3D ConvNets with Optical Flow Based Regularization
Video classification using 3D convolutional neural networks still lags behind models with simple classifiers on top of rich, hand-engineered, spatio-temporal features for a number of prominent action recognition datasets. Many of these hand-designed features are built on top of estimates of optical flow. Thus we propose an extension to the 3D convolutional neural network model that incorporates...
متن کاملAutomatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular disease. However, this task is very challenging due to ambiguous cardiac borders and large anatomical variations among different subjects. In this paper, we propose a novel densely-connected vol...
متن کاملFast Reading Comprehension with ConvNets
State-of-the-art deep reading comprehension models are dominated by recurrent neural nets. Their sequential nature is a natural fit for language, but it also precludes parallelization within an instances and often becomes the bottleneck for deploying such models to latency critical scenarios. This is particularly problematic for longer texts. Here we present a convolutional architecture as an a...
متن کاملRecalibrating Reperfusion Waypoints.
The realization that thrombus was the cause and not the consequence of acute myocardial infarction was a transformative pathophysiologic insight.1 An even more stunning observation was the subsequent discovery that restoration of coronary patency could salvage ischemic myocardium and improve clinical outcomes in ST-elevation acute myocardial infarction (STEMI).2,3 Assertive clinical investigati...
متن کاملLetter-Based Speech Recognition with Gated ConvNets
In this paper we introduce a new speech recognition system, leveraging a simple letter-based ConvNet acoustic model. The acoustic model requires only audio transcription for training – no alignment annotations, nor any forced alignment step is needed. At inference, our decoder takes only a word list and a language model, and is fed with letter scores from the acoustic model – no phonetic word l...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Medical Imaging
سال: 2020
ISSN: 0278-0062,1558-254X
DOI: 10.1109/tmi.2020.2972059