Entity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical ambiguity of language. But while the two tasks are pretty similar, they differ in a fundamental respect: in EL the textual mention can be linked to a named entity which may or may not contain the exact mention, while in WSD there is a perfect match between the word form (better, its lemma) and a suitable word sense. In this paper we present Babelfy, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations. Our experiments show state-of- the-art performances on both tasks on 6 different datasets, including a multilingual setting. Babelfy is online at http://babelfy.org

/pdf/entity-linking-meets-word-sense-disambiguation-a-unified-2zo57he615.pdf

Entity Linking meets Word Sense Disambiguation: A Unified Approach

The Stanford typed dependencies representation was designed to provide a simple description of the grammatical relationships in a sentence that can easily be understood and effectively used by people without linguistic expertise who want to extract textual relations. In particular, rather than the phrase structure representations that have long dominated in the computational linguistic community, it represents all sentence relationships uniformly as typed dependency relations. That is, as triples of a relation between pairs of words, such as “the subject of distributes is Bell.” Our experience is that this simple, uniform representation is quite accessible to non-linguists thinking about tasks involving information extraction from text and is quite effective in relation extraction applications. Here is an example sentence:

/pdf/stanford-typed-dependencies-manual-2lwrx08xq4.pdf

Stanford typed dependencies manual

This paper details the coreference resolution system submitted by Stanford at the CoNLL-2011 shared task. Our system is a collection of deterministic coreference resolution models that incorporate lexical, syntactic, semantic, and discourse information. All these models use global document-level information by sharing mention attributes, such as gender and number, across mentions in the same cluster. We participated in both the open and closed tracks and submitted results using both predicted and gold mentions. Our system was ranked first in both tracks, with a score of 57.8 in the closed track and 58.3 in the open track.

/pdf/stanforda-s-multi-pass-sieve-coreference-resolution-system-1xszi21vep.pdf

Stanfordâ€™s Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task

High radiation dose in computed tomography (CT) scans increases the lifetime risk of cancer and has become a major clinical concern. Recently, iterative reconstruction algorithms with total variation (TV) regularization have been developed to reconstruct CT images from highly undersampled data acquired at low mAs levels in order to reduce the imaging dose. Nonetheless, the low-contrast structures tend to be smoothed out by the TV regularization, posing a great challenge for the TV method. To solve this problem, in this work we develop an iterative CT reconstruction algorithm with edge-preserving TV (EPTV) regularization to reconstruct CT images from highly undersampled data obtained at low mAs levels. The CT image is reconstructed by minimizing energy consisting of an EPTV norm and a data fidelity term posed by the x-ray projections. The EPTV term is proposed to preferentially perform smoothing only on the non-edge part of the image in order to better preserve the edges, which is realized by introducing a penalty weight to the original TV norm. During the reconstruction process, the pixels at the edges would be gradually identified and given low penalty weight. Our iterative algorithm is implemented on graphics processing unit to improve its speed. We test our reconstruction algorithm on a digital NURBS-based cardiac-troso phantom, a physical chest phantom and a Catphan phantom. Reconstruction results from a conventional filtered backprojection (FBP) algorithm and a TV regularization method without edge-preserving penalty are also presented for comparison purposes. The experimental results illustrate that both the TV-based algorithm and our EPTV algorithm outperform the conventional FBP algorithm in suppressing the streaking artifacts and image noise under a low-dose context. Our edge-preserving algorithm is superior to the TV-based algorithm in that it can preserve more information of low-contrast structures and therefore maintain acceptable spatial resolution.

/pdf/low-dose-ct-reconstruction-via-edge-preserving-total-24kiprx7v4.pdf

Low-dose CT reconstruction via edge-preserving total variation regularization

The graphics processing unit (GPU) has emerged as a competitive platform for computing massively parallel problems. Many computing applications in medical physics can be formulated as data-parallel tasks that exploit the capabilities of the GPU for reducing processing times. The authors review the basic principles of GPU computing as well as the main performance optimization techniques, and survey existing applications in three areas of medical physics, namely image reconstruction, dose calculation and treatment plan optimization, and image processing.

/pdf/gpu-computing-in-medical-physics-a-review-i97kr9j4ih.pdf

GPU computing in medical physics: a review.

Purpose: Monte Carlo methods are considered the gold standard for dosimetric computations in radiotherapy. Their execution time is however still an obstacle to the routine use of Monte Carlo packages in a clinical setting. To address this problem, a completely new, and designed from the ground up for the GPU, Monte Carlo dose calculation package for voxelized geometries is proposed: GPUMCD. Method : GPUMCD implements a coupled photon-electron Monte Carlo simulation for energies in the range 0.01 MeV to 20 MeV. An analogue simulation of photon interactions is used and a Class II condensed history method has been implemented for the simulation of electrons. A new GPU random number generator, some divergence reduction methods as well as other optimization strategies are also described. GPUMCD was run on a NVIDIA GTX480 while single threaded implementations of EGSnrc and DPM were run on an Intel Core i7 860. Results : Dosimetric results obtained with GPUMCD were compared to EGSnrc. In all but one test case, 98% or more of all significant voxels passed a gamma criteria of 2%-2mm. In terms of execution speed and efficiency, GPUMCD is more than 900 times faster than EGSnrc and more than 200 times faster than DPM, a Monte Carlo package aiming fast executions. Absolute execution times of less than 0.3 s are found for the simulation of 1M electrons and 4M photons in water for monoenergetic beams of 15 MeV, including GPU-CPU memory transfers. Conclusion : GPUMCD, a new GPU-oriented Monte Carlo dose calculation platform, has been compared to EGSnrc and DPM in terms of dosimetric results and execution speed. Its accuracy and speed make it an interesting solution for full Monte Carlo dose calculation in radiation oncology.

GPUMCD: a new GPU-oriented Monte Carlo dose calculation platform

Purpose: Monte Carlo methods are considered as the gold standard for dosimetric computations in radiotherapy. Their execution time is, however, still an obstacle to the routine use of Monte Carlo packages in a clinical setting. To address this problem, a completely new, and designed from the ground up for the GPU, Monte Carlodose calculation package for voxelized geometries is proposed:GPUMCD. Method: GPUMCD implements a coupled photon-electron Monte Carlo simulation for energies in the range of 0.01–20 MeV. An analog simulation of photon interactions is used and a class II condensed history method has been implemented for the simulation of electrons. A new GPU random number generator, some divergence reduction methods, as well as other optimization strategies are also described. GPUMCD was run on a NVIDIA GTX480, while single threaded implementations of EGSnrc and DPM were run on an Intel Core i7 860. Results: Dosimetric results obtained withGPUMCD were compared to EGSnrc. In all but one test case, 98% or more of all significant voxels passed the gamma criteria of 2%-2 mm. In terms of execution speed and efficiency, GPUMCD is more than 900 times faster than EGSnrc and more than 200 times faster than DPM, a Monte Carlo package aiming fast executions. Absolute execution times of less than 0.3 s are found for the simulation of 1M electrons and 4M photons in water for monoenergetic beams of 15 MeV, including GPU-CPU memory transfers. Conclusion: GPUMCD, a new GPU-oriented Monte Carlodose calculation platform, has been compared to EGSnrc and DPM in terms of dosimetric results and execution speed. Its accuracy and speed make it an interesting solution for full Monte Carlodose calculation in radiation oncology.

GPUMCD: A new GPU-oriented Monte Carlo dose calculation platform

Airflow modelling in a computer room

A new hybrid imaging-treatment modality, the MRI-Linac, involves the irradiation of the patient in the presence of a strong magnetic field. This field acts on the charged particles, responsible for depositing dose, through the Lorentz force. These conditions require a dose calculation engine capable of taking into consideration the effect of the magnetic field on the dose distribution during the planning stage. Also in the case of a change in anatomy at the time of treatment, a fast online replanning tool is desirable. It is improbable that analytical solutions such as pencil beam calculations can be efficiently adapted for dose calculations within a magnetic field. Monte Carlo simulations have therefore been used for the computations but the calculation speed is generally too slow to allow online replanning. In this work, GPUMCD, a fast graphics processing unit (GPU)-based Monte Carlo dose calculation platform, was benchmarked with a new feature that allows dose calculations within a magnetic field. As a proof of concept, this new feature is validated against experimental measurements. GPUMCD was found to accurately reproduce experimental dose distributions according to a 2%-2 mm gamma analysis in two cases with large magnetic field-induced dose effects: a depth-dose phantom with an air cavity and a lateral-dose phantom surrounded by air. Furthermore, execution times of less than 15 s were achieved for one beam in a prostate case phantom for a 2% statistical uncertainty while less than 20 s were required for a seven-beam plan. These results indicate that GPUMCD is an interesting candidate, being fast and accurate, for dose calculations for the hybrid MRI-Linac modality.

https://iopscience.iop.org/article/10.1088/0031-9155/56/16/003/pdf

Fast dose calculation in magnetic fields with GPUMCD

The numerical calculation of dose is central to treatment planning in radiation therapy and is at the core of optimization strategies for modern delivery techniques. In a clinical environment, dose calculation algorithms are required to be accurate and fast. The accuracy is typically achieved through the integration of patient-specific data and extensive beam modeling, which generally results in slower algorithms. In order to alleviate execution speed problems, the authors have implemented a modern dose calculation algorithm on a massively parallel hardware architecture. More specifically, they have implemented a convolution-superposition photon beam dose calculation algorithm on a commodity graphics processing unit (GPU). They have investigated a simple porting scenario as well as slightly more complex GPU optimization strategies. They have achieved speed improvement factors ranging from 10 to 20 times with GPU implementations compared to central processing unit (CPU) implementations, with higher values corresponding to larger kernel and calculation grid sizes. In all cases, they preserved the numerical accuracy of the GPU calculations with respect to the CPU calculations. These results show that streaming architectures such as GPUs can significantly accelerate dose calculation algorithms and let envision benefits for numerically intensive processes such as optimizing strategies, in particular, for complex delivery techniques such as IMRT and are therapy.

Benoît Ozell

Papers

GPUMCD: a new GPU-oriented Monte Carlo dose calculation platform

GPUMCD: A new GPU-oriented Monte Carlo dose calculation platform

Airflow modelling in a computer room

Fast dose calculation in magnetic fields with GPUMCD

Fast convolution-superposition dose calculation on graphics hardware