Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images

In this paper we address the memory demands that come with the processing of 3-dimensional, high-resolution, multi-channeled medical images in deep learning. We exploit memory-efficient backpropagation techniques, to reduce the memory complexity of network training from being linear in the network's depth, to being roughly constant $-$ permitting us to elongate deep architectures with negligible memory increase. We evaluate our methodology in the paradigm of Image Quality Transfer, whilst noting its potential application to various tasks that use deep learning. We study the impact of depth on accuracy and show that deeper models have more predictive power, which may exploit larger training sets. We obtain substantially better results than the previous state-of-the-art model with a slight memory increase, reducing the root-mean-squared-error by $13\%$. Our code is publicly available.Comment: Accepted in: MICCAI 201

Disease Knowledge Transfer across Neurodegenerative Diseases

We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases. DKT is a joint-disease generative model of biomarker progressions, which exploits biomarker relationships that are shared across diseases. Our proposed method allows, for the first time, the estimation of plausible, multimodal biomarker trajectories in Posterior Cortical Atrophy (PCA), a rare neurodegenerative disease where only unimodal MRI data is available. For this we train DKT on a combined dataset containing subjects with two distinct diseases and sizes of data available: 1) a larger, multimodal typical AD (tAD) dataset from the TADPOLE Challenge, and 2) a smaller unimodal Posterior Cortical Atrophy (PCA) dataset from the Dementia Research Centre (DRC), for which only a limited number of Magnetic Resonance Imaging (MRI) scans are available. Although validation is challenging due to lack of data in PCA, we validate DKT on synthetic data and two patient datasets (TADPOLE and PCA cohorts), showing it can estimate the ground truth parameters in the simulation and predict unseen biomarkers on the two patient datasets. While we demonstrated DKT on Alzheimer's variants, we note DKT is generalisable to other forms of related neurodegenerative diseases. Source code for DKT is available online: https://github.com/mrazvan22/dkt.Comment: accepted at MICCAI 2019, 13 pages, 5 figures, 2 table

Progressive Subsampling for Oversampled Data -- Application to Quantitative MRI

We present PROSUB: PROgressive SUBsampling, a deep learning based, automated methodology that subsamples an oversampled data set (e.g. multi-channeled 3D images) with minimal loss of information. We build upon a recent dual-network approach that won the MICCAI MUlti-DIffusion (MUDI) quantitative MRI measurement sampling-reconstruction challenge, but suffers from deep learning training instability, by subsampling with a hard decision boundary. PROSUB uses the paradigm of recursive feature elimination (RFE) and progressively subsamples measurements during deep learning training, improving optimization stability. PROSUB also integrates a neural architecture search (NAS) paradigm, allowing the network architecture hyperparameters to respond to the subsampling process. We show PROSUB outperforms the winner of the MUDI MICCAI challenge, producing large improvements >18% MSE on the MUDI challenge sub-tasks and qualitative improvements on downstream processes useful for clinical applications. We also show the benefits of incorporating NAS and analyze the effect of PROSUB's components. As our method generalizes to other problems beyond MRI measurement selection-reconstruction, our code is https://github.com/sbb-gh/PROSU

Origin of the Sinai-Negev erg, Egypt and Israel: mineralogical and geochemical evidence for the importance of the Nile and sea level history

The Sinai-Negev erg occupies an area of 13,000 km2 in the deserts of Egypt and Israel. Aeolian sand of this erg has been proposed to be derived from the Nile Delta, but empirical data supporting this view are lacking. An alternative source sediment is sand from the large Wadi El Arish drainage system in central and northern Sinai. Mineralogy of the Negev and Sinai dunes shows that they are high in quartz, with much smaller amounts of K-feldspar and plagioclase. Both Nile Delta sands and Sinai wadi sands, upstream of the dunes, also have high amounts of quartz relative to K-feldspar and plagioclase. However, Sinai wadi sands have abundant calcite, whereas Nile Delta sands have little or no calcite. Overall, the mineralogical data suggest that the dunes are derived dominantly from the Nile Delta, with Sinai wadi sands being a minor contributor. Geochemical data that proxy for both the light mineral fraction (SiO2/10-Al2O3 + Na2O + K2O-CaO) and heavy mineral fraction (Fe2O3-MgO-TiO2) also indicate a dominant Nile Delta source for the dunes. Thus, we report here the first empirical evidence that the Sinai-Negev dunes are derived dominantly from the Nile Delta. Linkage of the Sinai-Negev erg to the Nile Delta as a source is consistent with the distribution of OSL ages of Negev dunes in recent studies. Stratigraphic studies show that during the Last Glacial period, when dune incursions in the Sinai-Negev erg began, what is now the Nile Delta area was characterized by a broad, sandy, minimally vegetated plain, with seasonally dry anastomosing channels. Such conditions were ideal for providing a ready source of sand for aeolian transport under what were probably much stronger glacial-age winds. With the post-glacial rise in sea level, the Nile River began to aggrade. Post-glacial sedimentation has been dominated by fine-grained silts and clays. Thus, sea level, along with favorable climatic conditions, emerges as a major influence on the timing of dune activity in the Sinai-Negev erg, through its control on the supply of sand from the Nile Delta. The mineralogy of the Sinai-Negev dunes is also consistent with a proposed hypothesis that these sediments are an important source of loess in Israel

The relationship between motivation, learning strategies, and choice of environment whether traditional or including an online component

Abstract This study examined how students' achievement goals, self-efficacy and learning strategies influenced their choice of an online, hybrid or traditional learning environment. One hundred thirty-two post-secondary students completed surveys soliciting their preferences for learning environments, reasons for their preference, their motivational orientation towards learning and learning strategies used. Findings indicated that most students preferred traditional learning environments. This preference was based on how well the environment matched their personal learning style and engaged them as students. Discriminant analyses indicated significant differences in motivational beliefs and learning strategies; students who preferred traditional environments showed a mastery goal orientation and greater willingness to apply effort while learning. Students who preferred less traditional environments presented as more confident that they could manage a non-traditional class. These findings have implications for understanding students' motivation for learning in diverse educational settings