Modeling brand choice using boosted and stacked neural networks

The brand choice problem in marketing has recently been addressed with methods from computational intelligence such as neural networks. Another class of methods from computational intelligence, the so-called ensemble methods such as boosting and stacking have never been applied to the brand choice problem, as far as we know. Ensemble methods generate a number of models for the same problem using any base method and combine the outcomes of these different models. It is well known that in many cases the predictive performance of ensemble methods significantly exceeds the predictive performance of the their base methods. In this report we use boosting and stacking of neural networks and apply this to a scanner dataset that is a benchmark dataset in the marketing literature. Using these methods, we find a significant improvement in predictive performance on this dataset.

Modeling brand choice using boosted and stacked neural networks

The brand choice problem in marketing has recently been addressed with methods from computational intelligence such as neural networks. Another class of methods from computational intelligence, the so-called ensemble methods such as boosting and stacking have never been applied to the brand choice problem, as far as we know. Ensemble methods generate a number of models for the same problem using any base method and combine the outcomes of these different models. It is well known that in many cases the predictive performance of ensemble methods significantly exceeds the predictive performance of the their base methods. In this report we use boosting and stacking of neural networks and apply this to a scanner dataset that is a benchmark dataset in the marketing literature. Using these methods, we find a significant improvement in predictive performance on this dataset

Few-shot weakly supervised detection and retrieval in histopathology whole-slide images

In this work, we propose a deep learning system for weakly supervised object detection in digital pathology whole slide images. We designed the system to be organ- and object-agnostic, and to be adapted on-the-fly to detect novel objects based on a few examples provided by the user. We tested our method on detection of healthy glands in colon biopsies and ductal carcinoma in situ (DCIS) of the breast, showing that (1) the same system is capable of adapting to detect requested objects with high accuracy, namely 87% accuracy assessed on 582 detections in colon tissue, and 93% accuracy assessed on 163 DCIS detections in breast tissue; (2) in some settings, the system is capable of retrieving similar cases with little to none false positives (i.e., precision equal to 1.00); (3) the performance of the system can benefit from previously detected objects with high confidence that can be reused in new searches in an iterative fashion

Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images

Algorithms proposed in computational pathology can allow to automatically analyze digitized tissue samples of histopathological images to help diagnosing diseases. Tissue samples are scanned at a high-resolution and usually saved as images with several magnification levels, namely whole slide images (WSIs). Convolutional neural networks (CNNs) represent the state-of-the-art computer vision methods targeting the analysis of histopathology images, aiming for detection, classification and segmentation. However, the development of CNNs that work with multi-scale images such as WSIs is still an open challenge. The image characteristics and the CNN properties impose architecture designs that are not trivial. Therefore, single scale CNN architectures are still often used. This paper presents Multi_Scale_Tools, a library aiming to facilitate exploiting the multi-scale structure of WSIs. Multi_Scale_Tools currently include four components: a pre-processing component, a scale detector, a multi-scale CNN for classification and a multi-scale CNN for segmentation of the images. The pre-processing component includes methods to extract patches at several magnification levels. The scale detector allows to identify the magnification level of images that do not contain this information, such as images from the scientific literature. The multi-scale CNNs are trained combining features and predictions that originate from different magnification levels. The components are developed using private datasets, including colon and breast cancer tissue samples. They are tested on private and public external data sources, such as The Cancer Genome Atlas (TCGA). The results of the library demonstrate its effectiveness and applicability. The scale detector accurately predicts multiple levels of image magnification and generalizes well to independent external data. The multi-scale CNNs outperform the single-magnification CNN for both classification and segmentation tasks. The code is developed in Python and it will be made publicly available upon publication. It aims to be easy to use and easy to be improved with additional functions

Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

The digitalization of clinical workflows and the increasing performance of deep learning algorithms are paving the way towards new methods for tackling cancer diagnosis. However, the availability of medical specialists to annotate digitized images and free-text diagnostic reports does not scale with the need for large datasets required to train robust computer-aided diagnosis methods that can target the high variability of clinical cases and data produced. This work proposes and evaluates an approach to eliminate the need for manual annotations to train computer-aided diagnosis tools in digital pathology. The approach includes two components, to automatically extract semantically meaningful concepts from diagnostic reports and use them as weak labels to train convolutional neural networks (CNNs) for histopathology diagnosis. The approach is trained (through 10-fold cross-validation) on 3’769 clinical images and reports, provided by two hospitals and tested on over 11’000 images from private and publicly available datasets. The CNN, trained with automatically generated labels, is compared with the same architecture trained with manual labels. Results show that combining text analysis and end-to-end deep neural networks allows building computer-aided diagnosis tools that reach solid performance (micro-accuracy = 0.908 at image-level) based only on existing clinical data without the need for manual annotations

Learning to detect lymphocytes in immunohistochemistry with deep learning

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