Invariant Representations through Adversarial Forgetting

We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechanism. We show that the forgetting mechanism serves as an information-bottleneck, which is manipulated by the adversarial training to learn invariance to unwanted factors. Empirical results show that the proposed framework achieves state-of-the-art performance at learning invariance in both nuisance and bias settings on a diverse collection of datasets and tasks.Comment: To appear in Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-20

Scanner invariant representations for diffusion MRI harmonization

Purpose In the present work, we describe the correction of diffusion‐weighted MRI for site and scanner biases using a novel method based on invariant representation. Theory and Methods Pooled imaging data from multiple sources are subject to variation between the sources. Correcting for these biases has become very important as imaging studies increase in size and multi‐site cases become more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory‐based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to underlying structures. To implement this, we use a deep learning method based on variational auto‐encoders (VAE) to construct scanner invariant encodings of the imaging data. Results To evaluate our method, we use training data from the 2018 MICCAI Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context. Conclusions As imaging studies continue to grow, the use of pooled multi‐site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data

Development of GIS Maps for Southeast Florida Coral Reefs

The present report outlines the results of an integrated mapping project undertaken to provide a habitat map of the shallow Broward County seafloor between the 0m and 35m contour. The study area stretched from Golden Beach in northern Dade County to just north of the Palm Beach County line. To produce this map and assure its compatibility with other, in particular NOAA, mapping products, a series of data were integrated. Data types included Laser Airborne Depth Sounder (LADS) bathymetry, multi- and single-beam bathymetry, acoustic seafloor discrimination, ecological assessments, and groundtruthing. The method used for acoustic seafloor discrimination was based on the first echo and its associated tail, and on the second echo returns of a 200 kHz signal. Two survey systems were employed: QTC View and Echoplus. A series of controlled experiments and field verifications indicated that it was possible to distinguish acoustically between different scattering classes that correlated to different seafloor types. For the production of the final map, information obtained from LADS bathymetry, NOAA classification and scattering classes obtained by QTC View and Echoplus was fused. The final map showed three well-developed linear reef complexes, a series of deep and shallow ridges believed to be old shorelines, a large sand area between the middle and outer reefs, and a considerable amount of colonized pavement. Due to the lack of distinct geomorphologic zones, the maps were based solely on habitat as defined by the NOAA biogeography program; however distinctions between areas such as linear reef, spur and groove, and colonized pavement were based on benthic cover (as seen by acoustic seafloor discrimination and biological transects) and geomorphology. The outer linear reef was subdivided into four habitats: aggregated patch reef, spur and groove, linear reef and deep colonized pavement. The area east of the outer linear reef consisted of a very patchy environment with large patches of reef interspersed amongst the deep sand. These were more prevalent close to the reef and tapered off eastward, becoming sandier. The spur and groove, linear reef, and deep colonized pavement comprised the outer reef and were separated mainly based on geomorphology. The outer reef was separated from the middle linear reef by a wide sandy plane (deep sand), which was characterized overall by a different scattering class in QTC View than the shallow sand found inshore. Acoustic ground discrimination identified patches of higher scatter and lower scatter amongst the outer, middle, and inner linear reefs suggesting distinct benthic cover between these structures. The eastern boundary of the middle reef was distinct and easily mapped whereas acoustic discrimination aided in determining the western boundary. The inner reef was the least distinct reef as it is not a mature reef. Much of this reef is patchy growth atop an inshore ridge and reef zonation is absent. Acoustic ground discrimination suggested that patches of higher versus lower scatter existed between and within the linear reefs, indicating that dense fauna is patchily distributed. Shoreward of the inner reef, another sand area or a mixture of sand and colonized pavements were found. Several nearshore ridges were mapped that could be classified as linear reef habitat, but were thought to be of non-reefal origin. Therefore these structures were mapped separately even though similar habitat comprises the inshore ridges, the inner linear reef, and the shallow colonized pavements. Excluded habitats such as submerged vegetation and large rubble zones were not detected sufficiently enough to be mapped during this effort. Groundtruthing by way of underwater video drop cameras and in situ biological assessments aided in the refinement of the mapping categories. Accuracy assessment of an independent grid of target points showed the map to have a users accuracy between 83% and 97% and a producers accuracy between 81% and 95%. These are acceptable accuracies and compare similarly to NOAA published map accuracies. In conclusion, the amalgamation of several mapping approaches and data products provided a representative map of Broward County submarine habitats that was accurate to a very satisfactory level. The results of this survey are a good example of how similar mapping products can be attained through different means. The method employed to map Broward County appears to have equally and accurately illustrated the benthic community as more traditional methods like photo interpretation. Similar methodology should be used in other areas where photo interpretation is not feasible due to either absence of data or the turbidity of the water