Parameter estimation of the homodyned K distribution based on neural networks and trainable fractional-order moments

no code implementations • 11 Oct 2022 • Michal Byra, Ziemowit Klimonda, Piotr Jarosik

We develop neural networks that can estimate the HK distribution parameters based on the signal-to-noise ratio, skewness and kurtosis calculated using fractional-order moments.

Estimating the ultrasound attenuation coefficient using convolutional neural networks -- a feasibility study

no code implementations • 19 May 2022 • Piotr Jarosik, Michal Byra, Marcin Lewandowski, Ziemowit Klimonda

Attenuation coefficient (AC) is a fundamental measure of tissue acoustical properties, which can be used in medical diagnostics.

Breast mass segmentation based on ultrasonic entropy maps and attention gated U-Net

no code implementations • 27 Jan 2020 • Michal Byra, Piotr Jarosik, Katarzyna Dobruch-Sobczak, Ziemowit Klimonda, Hanna Piotrzkowska-Wroblewska, Jerzy Litniewski, Andrzej Nowicki

In comparison to commonly applied segmentation methods, which use US images, our approach is based on quantitative entropy parametric maps.

Segmentation

Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

2 code implementations • 2 Apr 2018 • Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael Ong, Zhewei Huang, Shuchang Zhou, Anton Pechenko, Adam Stelmaszczyk, Piotr Jarosik, Mikhail Pavlov, Sergey Kolesnikov, Sergey Plis, Zhibo Chen, Zhizheng Zhang, Jiale Chen, Jun Shi, Zhuobin Zheng, Chun Yuan, Zhihui Lin, Henryk Michalewski, Piotr Miłoś, Błażej Osiński, Andrew Melnik, Malte Schilling, Helge Ritter, Sean Carroll, Jennifer Hicks, Sergey Levine, Marcel Salathé, Scott Delp

In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course.

reinforcement-learning Reinforcement Learning (RL)