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View article: Standardized Multi-Layer Tissue Maps for Enhanced Artificial Intelligence Integration and Search in Large-Scale Whole Slide Image Archives
Standardized Multi-Layer Tissue Maps for Enhanced Artificial Intelligence Integration and Search in Large-Scale Whole Slide Image Archives Open
A Whole Slide Image (WSI) is a high-resolution digital image created by scanning an entire glass slide containing a biological specimen, such as tissue sections or cell samples, at multiple magnifications. These images can be viewed, analy…
Model for Ki-67 Proliferation Index Predicton, Trained End-to-End on Routine Diagnostic Data Open
For breast cancer, the Ki-67 index gives important information on the patient’s prognosis and may predict the response to therapy. However, semi-automated methods for Ki-67 index calculation are prone to intra-and inter-observer variabilit…
Beyond Occlusion: In Search for Near Real-Time Explainability of CNN-Based Prostate Cancer Classification Open
Deep neural networks are starting to show their worth in critical applications such as assisted cancer diagnosis. However, for their outputs to get accepted in practice, the results they provide should be explainable in a way easily unders…
Learning Algorithms for Verification of Markov Decision Processes Open
We present a general framework for applying learning algorithms and heuristical guidance to the verification of Markov decision processes (MDPs). The primary goal of our techniques is to improve performance by avoiding an exhaustive explor…
Learning Algorithms for Verification of Markov Decision Processes Open
We present a general framework for applying learning algorithms and heuristical guidance to the verification of Markov decision processes (MDPs). The primary goal of our techniques is to improve performance by avoiding an exhaustive explor…
Shedding light on the black box of a neural network used to detect prostate cancer in whole slide images by occlusion-based explainability Open
Diagnostic histopathology faces increasing demands due to aging populations and expanding healthcare programs. Semi-automated diagnostic systems employing deep learning methods are one approach to alleviate this pressure. The learning mode…
xOpat: eXplainable Open Pathology Analysis Tool Open
Histopathology research quickly evolves thanks to advances in whole slide imaging (WSI) and artificial intelligence (AI). However, existing WSI viewers are tailored either for clinical or research environments, but none suits both. This hi…
On-the-fly Adaptation of Patrolling Strategies in Changing Environments Open
We consider the problem of efficient patrolling strategy adaptation in a changing environment where the topology of Defender's moves and the importance of guarded targets change unpredictably. The Defender must instantly switch to a new st…
Privacy Risks of Whole-Slide Image Sharing in Digital Pathology Open
Access to large volumes of so called whole-slide images, high-resolution scans of complete pathological slides, has become a cornerstone of development of novel artificial intelligence methods in digital pathology, but has broader impact f…
Privacy Risks of Whole-Slide Image Sharing in Digital Pathology Open
Access to large volumes of so-called whole-slide images —high-resolution scans of complete pathological slides—has become a cornerstone of the development of novel artificial intelligence methods in pathology for diagnostic use, education/…
Shedding Light on the Black Box of a Neural Network Used to Detect Prostate Cancer in Whole Slide Images by Occlusion-Based Explainability Open
Diagnostic histopathology is facing increasing demands due to aging populations and expanding healthcare programs. Semi-automated diagnostic systems employing deep learning methods are one approach to alleviate this pressure, with promisin…
Automated annotations of epithelial cells and stroma in <span>hematoxylin–eosin</span>‐stained whole‐slide images using cytokeratin re‐staining Open
The diagnosis of solid tumors of epithelial origin (carcinomas) represents a major part of the workload in clinical histopathology. Carcinomas consist of malignant epithelial cells arranged in more or less cohesive clusters of variable siz…
Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes Open
Markov decision processes (MDPs) are the defacto framework for sequential decision making in the presence of stochastic uncertainty. A classical optimization criterion for MDPs is to maximize the expected discounted-sum payoff, which ignor…
Reinforcement Learning of Risk-Constrained Policies in Markov Decision\n Processes Open
Markov decision processes (MDPs) are the defacto frame-work for sequential\ndecision making in the presence ofstochastic uncertainty. A classical\noptimization criterion forMDPs is to maximize the expected discounted-sum\npay-off, which ig…
Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes Open
Markov decision processes (MDPs) are the defacto frame-work for sequential decision making in the presence ofstochastic uncertainty. A classical optimization criterion forMDPs is to maximize the expected discounted-sum pay-off, which ignor…
Unbounded Orchestrations of Transducers for Manufacturing Open
There has recently been increasing interest in using reactive synthesis techniques to automate the production of manufacturing process plans. Previous work has assumed that the set of manufacturing resources is known and fixed in advance. …
Monte Carlo Tree Search for Verifying Reachability in Markov Decision Processes Open
The maximum reachability probabilities in a Markov decision process can be computed using value iteration (VI). Recently, simulation-based heuristic extensions of VI have been introduced, such as bounded real-time dynamic programming (BRTD…
Solving Patrolling Problems in the Internet Environment Open
We propose an algorithm for constructing efficient patrolling strategies in the Internet environment, where the protected targets are nodes connected to the network and the patrollers are software agents capable of detecting/preventing und…
Efficient Algorithms for Asymptotic Bounds on Termination Time in VASS Open
Vector Addition Systems with States (VASS) provide a well-known and fundamental model for the analysis of concurrent processes, parameterized systems, and are also used as abstract models of programs in resource bound analysis. In this pap…
Synthesizing Efficient Solutions for Patrolling Problems in the Internet Environment Open
We propose an algorithm for constructing efficient patrolling strategies in the Internet environment, where the protected targets are nodes connected to the network and the patrollers are software agents capable of detecting/preventing und…
Synthesizing Efficient Solutions for Patrolling Problems in the Internet Environment Open
We propose an algorithm for constructing efficient patrolling strategies in the Internet environment, where the protected targets are nodes connected to the network and the patrollers are software agents capable of detecting/preventing und…
Strategy Representation by Decision Trees in Reactive Synthesis Open
Graph games played by two players over finite-state graphs are central in many problems in computer science. In particular, graph games with $\omega$-regular winning conditions, specified as parity objectives, which can express properties …