Graph-based tools for microscopic cellular image segmentation

TL;DR: This paper embeds the clustering concept into the multiple instance learning (MIL) setting and derive a principled solution to performing the above three tasks in an integrated framework and introduces contextual constraints as a prior for MCIL, which further reduces the ambiguity in MIL.

TL;DR: An innovative platform in which multi-scale computer vision algorithms perform fast analysis of a histopathological WSI, which relies on application-driven for high-resolution and generic for low-resolution image analysis algorithms embedded in a multi- scale framework to rapidly identify the high power fields of interest used by the pathologist to assess a global grading.

TL;DR: The formulation introduces nonlocal patch-based configurations for image processing and extends PDEs-based approach to the processing of arbitrary data such as nonuniform high dimensional data.

TL;DR: The need for creating a database for benchmarking intensity based algorithms, and the need for further research in graph based segmentation for automated real time applications are highlighted.

TL;DR: This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches.

TL;DR: Semi-supervised learning (SSL) as discussed by the authors is the middle ground between supervised learning (in which all training examples are labeled) and unsupervised training (where no label data are given).

TL;DR: A semi-supervised framework that incorporates labeled and unlabeled data in a general-purpose learner is proposed and properties of reproducing kernel Hilbert spaces are used to prove new Representer theorems that provide theoretical basis for the algorithms.

TL;DR: In this article, the authors make a distinction between inductive semi-supervised learning and transductive learning, where the goal is to learn a predictor that predicts future test data better than the predictor learned from the labeled training data alone.