Yam Code
Sign up
Login
New paste
Home
Trending
Archive
English
English
Tiếng Việt
भारत
Sign up
Login
New Paste
Browse
healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer's type.Adverse drug reactions (ADRs) are detrimental and unexpected clinical incidents caused by drug intake. The increasing availability of massive quantities of longitudinal event data such as electronic health records (EHRs) has redefined ADR discovery as a big data analytics problem, where data-hungry deep neural networks are especially suitable because of the abundance of the data. To this end, we introduce neural self-controlled case series (NSCCS), a deep learning framework for ADR discovery from EHRs. NSCCS rigorously follows a self-controlled case series design to adjust implicitly and efficiently for individual heterogeneity. In this way, NSCCS is robust to time-invariant confounding issues and thus more capable of identifying associations that reflect the underlying mechanism between various types of drugs and adverse conditions. We apply NSCCS to a large-scale, real-world EHR dataset and empirically demonstrate its superior performance with comprehensive experiments on a benchmark ADR discovery task.Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. However, public mitochondria segmentation datasets only contain hundreds of instances with simple shapes. It is unclear if existing methods achieving human-level accuracy on these small datasets are robust in practice. To this end, we introduce the MitoEM dataset, a 3D mitochondria instance segmentation dataset with two (30μm)3 volumes from human and rat cortices respectively, 3, 600× larger than previous benchmarks. With around 40K instances, we find a great diversity of mitochondria in terms of shape and density. For evaluation, we tailor the implementation of the average precision (AP) metric for 3D data with a 45× speedup. On MitoEM, we find existing instance segmentation methods often fail to correctly segment mitochondria with complex shapes or close contacts with other instances. Thus, our MitoEM dataset poses new challenges to the field. We release our code and data https//donglaiw.github.io/page/mitoEM/index.html.Interest is growing rapidly in using deep learning to classify biomedical images, and interpreting these deep-learned models is necessary for life-critical decisions and scientific discovery. Effective interpretation techniques accelerate biomarker discovery and provide new insights into the etiology, diagnosis, and treatment of disease. Most interpretation techniques aim to discover spatially-salient regions within images, but few techniques consider imagery with multiple channels of information. For instance, highly multiplexed tumor and tissue images have 30-100 channels and require interpretation methods that work across many channels to provide deep molecular insights. We propose a novel channel embedding method that extracts features from each channel. We then use these features to train a classifier for prediction. Using this channel embedding, we apply an interpretation method to rank the most discriminative channels. To validate our approach, we conduct an ablation study on a synthetic dataset. Moreover, we demonstrate that our method aligns with biological findings on highly multiplexed images of breast cancer cells while outperforming baseline pipelines. https://www.selleckchem.com/products/pomhex.html Code is available at https//sabdelmagid.github.io/miccai2020-project/.Deformable image registration between Computed Tomography (CT) images and Magnetic Resonance (MR) imaging is essential for many image-guided therapies. In this paper, we propose a novel translation-based unsupervised deformable image registration method. Distinct from other translation-based methods that attempt to convert the multimodal problem (e.g., CT-to-MR) into a unimodal problem (e.g., MR-to-MR) via image-to-image translation, our method leverages the deformation fields estimated from both (i) the translated MR image and (ii) the original CT image in a dual-stream fashion, and automatically learns how to fuse them to achieve better registration performance. The multimodal registration network can be effectively trained by computationally efficient similarity metrics without any ground-truth deformation. Our method has been evaluated on two clinical datasets and demonstrates promising results compared to state-of-the-art traditional and learning-based methods.Craniofacial syndromes often involve skeletal defects of the head. Studying the development of the chondrocranium (the part of the endoskeleton that protects the brain and other sense organs) is crucial to understanding genotype-phenotype relationships and early detection of skeletal malformation. Our goal is to segment craniofacial cartilages in 3D micro-CT images of embryonic mice stained with phosphotungstic acid. However, due to high image resolution, complex object structures, and low contrast, delineating fine-grained structures in these images is very challenging, even manually. Specifically, only experts can differentiate cartilages, and it is unrealistic to manually label whole volumes for deep learning model training. We propose a new framework to progressively segment cartilages in high-resolution 3D micro-CT images using extremely sparse annotation (e.g., annotating only a few selected slices in a volume). Our model consists of a lightweight fully convolutional network (FCN) to accelerate the training speed and generate pseudo labels (PLs) for unlabeled slices. Meanwhile, we take into account the reliability of PLs using a bootstrap ensemble based uncertainty quantification method. Further, our framework gradually learns from the PLs with the guidance of the uncertainty estimation via self-training. Experiments show that our method achieves high segmentation accuracy compared to prior arts and obtains performance gains by iterative self-training.
Paste Settings
Paste Title :
[Optional]
Paste Folder :
[Optional]
Select
Syntax Highlighting :
[Optional]
Select
Markup
CSS
JavaScript
Bash
C
C#
C++
Java
JSON
Lua
Plaintext
C-like
ABAP
ActionScript
Ada
Apache Configuration
APL
AppleScript
Arduino
ARFF
AsciiDoc
6502 Assembly
ASP.NET (C#)
AutoHotKey
AutoIt
Basic
Batch
Bison
Brainfuck
Bro
CoffeeScript
Clojure
Crystal
Content-Security-Policy
CSS Extras
D
Dart
Diff
Django/Jinja2
Docker
Eiffel
Elixir
Elm
ERB
Erlang
F#
Flow
Fortran
GEDCOM
Gherkin
Git
GLSL
GameMaker Language
Go
GraphQL
Groovy
Haml
Handlebars
Haskell
Haxe
HTTP
HTTP Public-Key-Pins
HTTP Strict-Transport-Security
IchigoJam
Icon
Inform 7
INI
IO
J
Jolie
Julia
Keyman
Kotlin
LaTeX
Less
Liquid
Lisp
LiveScript
LOLCODE
Makefile
Markdown
Markup templating
MATLAB
MEL
Mizar
Monkey
N4JS
NASM
nginx
Nim
Nix
NSIS
Objective-C
OCaml
OpenCL
Oz
PARI/GP
Parser
Pascal
Perl
PHP
PHP Extras
PL/SQL
PowerShell
Processing
Prolog
.properties
Protocol Buffers
Pug
Puppet
Pure
Python
Q (kdb+ database)
Qore
R
React JSX
React TSX
Ren'py
Reason
reST (reStructuredText)
Rip
Roboconf
Ruby
Rust
SAS
Sass (Sass)
Sass (Scss)
Scala
Scheme
Smalltalk
Smarty
SQL
Soy (Closure Template)
Stylus
Swift
TAP
Tcl
Textile
Template Toolkit 2
Twig
TypeScript
VB.Net
Velocity
Verilog
VHDL
vim
Visual Basic
WebAssembly
Wiki markup
Xeora
Xojo (REALbasic)
XQuery
YAML
HTML
Paste Expiration :
[Optional]
Never
Self Destroy
10 Minutes
1 Hour
1 Day
1 Week
2 Weeks
1 Month
6 Months
1 Year
Paste Status :
[Optional]
Public
Unlisted
Private (members only)
Password :
[Optional]
Description:
[Optional]
Tags:
[Optional]
Encrypt Paste
(
?
)
Create New Paste
You are currently not logged in, this means you can not edit or delete anything you paste.
Sign Up
or
Login
Site Languages
×
English
Tiếng Việt
भारत