19 research outputs found
A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching
We study the quadratic assignment problem, in computer vision also known as
graph matching. Two leading solvers for this problem optimize the Lagrange
decomposition duals with sub-gradient and dual ascent (also known as message
passing) updates. We explore s direction further and propose several additional
Lagrangean relaxations of the graph matching problem along with corresponding
algorithms, which are all based on a common dual ascent framework. Our
extensive empirical evaluation gives several theoretical insights and suggests
a new state-of-the-art any-time solver for the considered problem. Our
improvement over state-of-the-art is particularly visible on a new dataset with
large-scale sparse problem instances containing more than 500 graph nodes each.Comment: Added acknowledgment
Towards Hierarchical Regional Transformer-based Multiple Instance Learning
The classification of gigapixel histopathology images with deep multiple
instance learning models has become a critical task in digital pathology and
precision medicine. In this work, we propose a Transformer-based multiple
instance learning approach that replaces the traditional learned attention
mechanism with a regional, Vision Transformer inspired self-attention
mechanism. We present a method that fuses regional patch information to derive
slide-level predictions and show how this regional aggregation can be stacked
to hierarchically process features on different distance levels. To increase
predictive accuracy, especially for datasets with small, local morphological
features, we introduce a method to focus the image processing on high attention
regions during inference. Our approach is able to significantly improve
performance over the baseline on two histopathology datasets and points towards
promising directions for further research.Comment: To be published as ICCV 2023 workshop pape
A study of lagrangean decompositions and dual ascent solvers for graph matching
We study the quadratic assignment problem, in computer vision also known as graph matching. Two leading solvers for this problem optimize the Lagrange decomposition duals with sub-gradient and dual ascent (also known as message passing) updates. We explore this direction further and propose several additional Lagrangean relaxations of the graph matching problem along with corresponding algorithms, which are all based on a common dual ascent framework. Our extensive empirical evaluation gives several theoretical insights and suggests a new state-of-the-art anytime solver for the considered problem. Our improvement over state-of-the-art is particularly visible on a new dataset with large-scale sparse problem instances containing more than 500 graph nodes each
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Automated detection and quantification of single RNAs at cellular resolution in zebrafish embryos.
Analysis of differential gene expression is crucial for the study of cell fate and behavior during embryonic development. However, automated methods for the sensitive detection and quantification of RNAs at cellular resolution in embryos are lacking. With the advent of single-molecule fluorescence in situ hybridization (smFISH), gene expression can be analyzed at single-molecule resolution. However, the limited availability of protocols for smFISH in embryos and the lack of efficient image analysis pipelines have hampered quantification at the (sub)cellular level in complex samples such as tissues and embryos. Here, we present a protocol for smFISH on zebrafish embryo sections in combination with an image analysis pipeline for automated transcript detection and cell segmentation. We use this strategy to quantify gene expression differences between different cell types and identify differences in subcellular transcript localization between genes. The combination of our smFISH protocol and custom-made, freely available, analysis pipeline will enable researchers to fully exploit the benefits of quantitative transcript analysis at cellular and subcellular resolution in tissues and embryos
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress
and for bridging the current chasm between artificial intelligence (AI)
research and its translation into practice. However, increasing evidence shows
that particularly in image analysis, metrics are often chosen inadequately in
relation to the underlying research problem. This could be attributed to a lack
of accessibility of metric-related knowledge: While taking into account the
individual strengths, weaknesses, and limitations of validation metrics is a
critical prerequisite to making educated choices, the relevant knowledge is
currently scattered and poorly accessible to individual researchers. Based on a
multi-stage Delphi process conducted by a multidisciplinary expert consortium
as well as extensive community feedback, the present work provides the first
reliable and comprehensive common point of access to information on pitfalls
related to validation metrics in image analysis. Focusing on biomedical image
analysis but with the potential of transfer to other fields, the addressed
pitfalls generalize across application domains and are categorized according to
a newly created, domain-agnostic taxonomy. To facilitate comprehension,
illustrations and specific examples accompany each pitfall. As a structured
body of information accessible to researchers of all levels of expertise, this
work enhances global comprehension of a key topic in image analysis validation.Comment: Shared first authors: Annika Reinke, Minu D. Tizabi; shared senior
authors: Paul F. J\"ager, Lena Maier-Hei
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in
helping us further understand the relationship between histology and patient
outcome. To drive innovation in this area, we setup a community-wide challenge
using the largest available dataset of its kind to assess nuclear segmentation
and cellular composition. Our challenge, named CoNIC, stimulated the
development of reproducible algorithms for cellular recognition with real-time
result inspection on public leaderboards. We conducted an extensive
post-challenge analysis based on the top-performing models using 1,658
whole-slide images of colon tissue. With around 700 million detected nuclei per
model, associated features were used for dysplasia grading and survival
analysis, where we demonstrated that the challenge's improvement over the
previous state-of-the-art led to significant boosts in downstream performance.
Our findings also suggest that eosinophils and neutrophils play an important
role in the tumour microevironment. We release challenge models and WSI-level
results to foster the development of further methods for biomarker discovery
A connectome and analysis of the adult Drosophila central brain.
The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain
Graph matching problems for annotating C. Elegans
Graph matching problems as described in "Active Graph Matching for Automatic Joint Segmentation and Annotation of C. Elegans." by Kainmueller, Dagmar and Jug, Florian and Rother, Carsten and Myers, Gene, MICCAI 2014. Problems are in OpenGM2 hdf5 format (see http://hciweb2.iwr.uni-heidelberg.de/opengm/) and a custom text format used by the feature matching solver described in "Feature Correspondence via Graph Matching: Models and Global Optimization." by Lorenzo Torresani, Vladimir Kolmogorov and Carsten Rother, ECCV 2008, code at http://pub.ist.ac.at/~vnk/software/GraphMatching-v1.02.src.zip