miscellaneous-10

Anatomy

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in anatomy-10

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BIG-bench

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i

Use these libraries to find anatomy-10 models and implementations

faceonlive/ai-research

2 papers 159

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BIG-bench

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Most implemented papers

Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem

G. Langs, H. Prosch, J. Hofmanninger, F. Prayer, Jeanny Pan, S. Röhrich•Thu Jan 30 2020

The accuracy and reliability of lung segmentation algorithms on demanding cases primarily relies on the diversity of the training data, highlighting the importance of data diversity compared to model choice.

515

Content

camma-public/surglatentgraph

2 papers 13

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Paper Graph

Disentangled representation learning in cardiac image analysis

S. Tsaftaris, A. Chartsias, G. Papanastasiou, S. Semple, D. Newby, R. Dharmakumar, T. Joyce, M. Williams•Thu Mar 21 2019

Spatial Decomposition Network (SDNet) is proposed, which factorises 2D medical images into spatial anatomical factors and non-spatial modality factors and is ideally suited for several medical image analysis tasks, such as semi-supervised segmentation, multi-task segmentation and regression, and image-to-image synthesis.

149 0

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Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-Supervised Learning

Jianming Liang, Zongwei Zhou, M. Gotway, F. Haghighi, M. Taher•Sat Feb 20 2021

This paper shows that visual words associated with rich semantics about human anatomy can be automatically harvested according to anatomical consistency via self-discovery, and that the self-discovered visual words can serve as strong yet free supervision signals for deep models to learn semantics-enriched generic image representation through self-supervision (self-classification and self-restoration).

127 0

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End-to-End Variational Networks for Accelerated MRI Reconstruction

F. Knoll, Jure Zbontar, Anuroop Sriram, Aaron Defazio, N. Yakubova, C. L. Zitnick, Tullie Murrell, Patricia M. Johnson•Mon Apr 13 2020

This paper presents a new approach to this problem that extends previously proposed variational methods by learning fully end-to-end and obtains new state-of-the-art results on the fastMRI dataset for both brain and knee MRIs.

376 0

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Deep Active Learning for Axon-Myelin Segmentation on Histology Data

C. Perone, J. Cohen-Adad, Melanie Lubrano di Scandalea, M. Boudreau•Wed Jul 10 2019

A framework for Deep Active Learning applied to a real-world scenario that relies on the U-Net architecture and overall uncertainty measure to suggest which sample to annotate, and shows evidence that this straightforward implementation achieves a high segmentation performance with very few labelled samples.

24 0

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KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations

Vishal M. Patel, Vishwanath A. Sindagi, Jeya Maria Jose Valanarasu, I. Hacihaliloglu•Sun Jun 07 2020

This work proposes an over-complete architecture (Ki-Net) which involves projecting the data onto higher dimensions (in the spatial sense) and results in significant improvements in the case of segmenting small anatomical landmarks and blurred noisy boundaries while obtaining better overall performance.

187 0

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Reinforcement Learning with Random Delays

C. Pal, Jonathan Binas, Yann Bouteiller, G. Beltrame, Simon Ramstedt•Mon Oct 05 2020

The anatomy of randomly delayed environments is studied, and it is shown that partially resampling trajectory fragments in hindsight allows for off-policy multi-step value estimation in Delay-Correcting Actor-Critic (DCAC), an algorithm based on Soft Actor- Critic with significantly better performance in environments with delays.

75 0

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Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction

J. Sonke, J. Teuwen, George Yiasemis, C. S'anchez•Wed Nov 17 2021

This work presents a novel Deep Learning-based Inverse Problem solver applied to the task of Accelerated MRI Reconstruction, called the Recurrent Variational Network (RecurrentVarNet), by exploiting the properties of Convolutional Recurrent Neural Networks and unrolled algorithms for solving Inverse Problems.

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MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization

N. Navab, Yuanwei Bi, Zhongliang Jiang, Ricarda Clarenbach, R. Ghotbi, A. Karlas•Tue Mar 21 2023

The proposed MI-SegNet, a novel mutual information (MI) based framework to explicitly disentangle the anatomical and domain feature representations, is proposed and validated, demonstrating the generalizability of the proposed domain-independent segmentation approach on several datasets with varying parameters and machines.

30 0

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