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Computational pathology: challenges and promises for tissue analysis
The histological assessment of human tissue has emerged as the key challenge for
detection and treatment of cancer. A plethora of different data sources ranging from tissue …
detection and treatment of cancer. A plethora of different data sources ranging from tissue …
Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology
Artificial intelligence (AI) solutions that automatically extract information from digital histology
images have shown great promise for improving pathological diagnosis. Prior to routine use …
images have shown great promise for improving pathological diagnosis. Prior to routine use …
[HTML][HTML] nucleAIzer: a parameter-free deep learning framework for nucleus segmentation using image style transfer
Single-cell segmentation is typically a crucial task of image-based cellular analysis. We
present nucleAIzer, a deep-learning approach aiming toward a truly general method for …
present nucleAIzer, a deep-learning approach aiming toward a truly general method for …
Microscopy cell counting and detection with fully convolutional regression networks
This paper concerns automated cell counting and detection in microscopy images. The
approach we take is to use convolutional neural networks (CNNs) to regress a cell spatial …
approach we take is to use convolutional neural networks (CNNs) to regress a cell spatial …
End-to-end scene text recognition
This paper focuses on the problem of word detection and recognition in natural images. The
problem is significantly more challenging than reading text in scanned documents, and has …
problem is significantly more challenging than reading text in scanned documents, and has …
Learning to count objects in images
We propose a new supervised learning framework for visual object counting tasks, such as
estimating the number of cells in a microscopic image or the number of humans in …
estimating the number of cells in a microscopic image or the number of humans in …
Word spotting in the wild
We present a method for spotting words in the wild, ie, in real images taken in unconstrained
environments. Text found in the wild has a surprising range of difficulty. At one end of the …
environments. Text found in the wild has a surprising range of difficulty. At one end of the …
Class-agnostic counting
Nearly all existing counting methods are designed for a specific object class. Our work,
however, aims to create a counting model able to count any class of object. To achieve this …
however, aims to create a counting model able to count any class of object. To achieve this …
Count-ception: Counting by fully convolutional redundant counting
Counting objects in digital images is a process that should be replaced by machines. This
tedious task is time consuming and prone to errors due to fatigue of human annotators. The …
tedious task is time consuming and prone to errors due to fatigue of human annotators. The …
On detection of multiple object instances using hough transforms
Hough transform-based methods for detecting multiple objects use nonmaxima suppression
or mode seeking to locate and distinguish peaks in Hough images. Such postprocessing …
or mode seeking to locate and distinguish peaks in Hough images. Such postprocessing …