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Breast cancer metastasis prediction plays a key role in
clinical decision-making and secondary analysis. Traditionally,
metastasis classification models have been developed
using structured tabular clinical data, but these approaches
may result in data loss and lack of contextual information. A
multi-modal approach is presented in this article for predicting
breast cancer metastasis by converting structured clinical
data into unstructured text, which provides more contextual
information, and then converting that text into histopathology

Correlative imaging workflows are now widely used in bio-imaging and aims to image the same sample using at least two different and complementary imaging modalities. Part of the workflow relies on finding the transformation linking a source image to a target image. We are specifically interested in the estimation of registration error in point-based registration.

The use of photo-activated fluorescent molecules to create long sequences of low-density, diffraction-limited images gives us the ability to achieve highly-precise molecule localizations. However, this methodology requires lengthy imaging times, resulting in poor temporal resolution. This is particularly problematic when dynamic interactions of live cells on short time scales are of interest. We consider the problem of shortening dramatically the acquisition times in super-resolution microscopy down to seconds, in order to image the cellular dynamics during T-cell activation.

Segmentation of biological images is a challenging task, due to non convex shapes, intensity inhomogeneity and clustered cells. To address these issues, a new algorithm is proposed based on the B-spline level set method. The implicit function of the level set is modelled as a continuous parametric function represented with the B-spline basis. It is different from the discrete formulation associated with conventional level set. In this paper the proposed framework takes into account properties of biological images.

Hyperspectral (HS) imaging retrieves information from data obtained across a wide spectral range of spectral channels. The object to reconstruct is a 3D cube, where two coordinates are spatial and the third one is spectral. We assume that this cube is complex-valued, i.e. characterized spatially frequency varying amplitude and phase. The observations are squared magnitudes measured as intensities summarized over the spectrum. The HS phase retrieval problem is formulated as a reconstruction of the HS complex-valued object cube from Gaussian noisy intensity observations.

Conventional Computed Tomography (CT) systems use a single X-ray source and an arc of detectors mounted on a rotating gantry to acquire a set of projection data. Novel CT systems are now being pioneered in which a complete ring of distributed X-ray sources and detectors are electronically turned on and off, without any mechanical motion, to acquire a set of projections for tomographic reconstruction. This paper discusses new sensing and reconstruction paradigms enabled by this new CT architecture.

Calcium imaging has become a fundamental neural imaging technique, aiming to recover the individual activity of hundreds of neurons in a cortical region. Current methods (mostly matrix factorization) are aimed at detecting neurons in the field-of-view and then inferring the corresponding time-traces. In this paper, we reverse the modeling and instead aim to minimize the spatial inference, while focusing on finding the set of temporal traces present in the data.

Brain tumor can be a fatal disease in the world. With the aim of improving survival rates, many computerized algorithms have been proposed to assist the pathologists to make a diagnosis, using Whole Slide Pathology Images (WSI). Most methods focus on performing patch-level classification and aggregating the patch-level results to obtain the image classification. Since not all patches carry diagnostic information, it is thus important for our algorithm to recognize discriminative and non-discriminative patches.

We present a semi-blind, spatially-variant deconvolution technique aimed at optical microscopy that combines a local estimation step of the point spread function (PSF) and deconvolution using a spatially variant, regularized Richardson-Lucy algorithm. To find the local PSF map in a computationally tractable way, we train a convolutional neural network to perform regression of an optical parametric model on synthetically blurred image patches.