An Aggregation of Aggregation Methods in Computational Pathology

Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide images (WSIs) often process a large number of tiles (sub-images) and require aggregating predictions from the tiles in order to predict WSI-level labels. In this paper, we present a review of existing literature on various types of aggregation methods with a view to help guide future research in the area of computational pathology (CPath). We propose a general CPath workflow with three pathways that consider multiple levels and types of data and the nature of computation to analyse WSIs for predictive modelling. We categorize aggregation methods according to the context and representation of the data, features of computational modules and CPath use cases. We compare and contrast different methods based on the principle of multiple instance learning, perhaps the most commonly used aggregation method, covering a wide range of CPath literature. To provide a fair comparison, we consider a specific WSI-level prediction task and compare various aggregation methods for that task. Finally, we conclude with a list of objectives and desirable attributes of aggregation methods in general, pros and cons of the various approaches, some recommendations and possible future directions.Comment: 32 pages, 4 figure

Local Training for Radial Basis Function Networks: Towards Solving the Hidden Unit Problem

This work examines training methods for radial basis function networks (RBFNs). First, the theoretical and practical motivation for RBFNs is reviewed, as are two currently popular training methods. Next a new training method is developed using well known results from functional analysis. This method trains each kidden unit individually, and is thus called the local training method. The structure of the method allows analysis of individual hidden units; moreover a covariance-related quantity is defined that gives insight into how many hidden units to employ. Two examples illustrate the usefulness of the method. Lastly, an ad hoc method to further improve RBFN performance is demonstrated

Automatic taxonomy evaluation

This thesis would not be made possible without the generous support of IATA.Les taxonomies sont une représentation essentielle des connaissances, jouant un rôle central dans de nombreuses applications riches en connaissances. Malgré cela, leur construction est laborieuse que ce soit manuellement ou automatiquement, et l'évaluation quantitative de taxonomies est un sujet négligé. Lorsque les chercheurs se concentrent sur la construction d'une taxonomie à partir de grands corpus non structurés, l'évaluation est faite souvent manuellement, ce qui implique des biais et se traduit souvent par une reproductibilité limitée. Les entreprises qui souhaitent améliorer leur taxonomie manquent souvent d'étalon ou de référence, une sorte de taxonomie bien optimisée pouvant service de référence. Par conséquent, des connaissances et des efforts spécialisés sont nécessaires pour évaluer une taxonomie. Dans ce travail, nous soutenons que l'évaluation d'une taxonomie effectuée automatiquement et de manière reproductible est aussi importante que la génération automatique de telles taxonomies. Nous proposons deux nouvelles méthodes d'évaluation qui produisent des scores moins biaisés: un modèle de classification de la taxonomie extraite d'un corpus étiqueté, et un modèle de langue non supervisé qui sert de source de connaissances pour évaluer les relations hyperonymiques. Nous constatons que nos substituts d'évaluation corrèlent avec les jugements humains et que les modèles de langue pourraient imiter les experts humains dans les tâches riches en connaissances.Taxonomies are an essential knowledge representation and play an important role in classification and numerous knowledge-rich applications, yet quantitative taxonomy evaluation remains to be overlooked and left much to be desired. While studies focus on automatic taxonomy construction (ATC) for extracting meaningful structures and semantics from large corpora, their evaluation is usually manual and subject to bias and low reproducibility. Companies wishing to improve their domain-focused taxonomies also suffer from lacking ground-truths. In fact, manual taxonomy evaluation requires substantial labour and expert knowledge. As a result, we argue in this thesis that automatic taxonomy evaluation (ATE) is just as important as taxonomy construction. We propose two novel taxonomy evaluation methods for automatic taxonomy scoring, leveraging supervised classification for labelled corpora and unsupervised language modelling as a knowledge source for unlabelled data. We show that our evaluation proxies can exert similar effects and correlate well with human judgments and that language models can imitate human experts on knowledge-rich tasks

A computational dynamical model of human visual cortex for visual search and feature-based attention

Visual attention can be deployed to locations within the visual array (spatial attention), to individual features such as colour and form (feature-based attention), or to entire objects (object-based attention). Objects are composed of features to form a perceived ‘whole’. This compositional object representation reduces the storage demands by avoiding the need to store every type of object experienced. However, this approach exposes a problem of binding these constituent features (e.g. form and colour) into objects. The problem is made explicit in the higher areas of the ventral stream as information about a feature’s location is absent. For feature-based attention and search, activations flow from the inferotemporal cortex to primary visual cortex without spatial cues from the dorsal stream, therefore the neural effect is applied to all locations across the visual field [79, 60, 7, 52]. My research hypothesis is that biased competition occurs independently for each cued feature, and is implemented by lateral inhibition between a feedforward and a feedback network through a cortical micro-circuit architecture. The local competition for each feature can be combined in the dorsal stream via spatial congruence to implement a secondary spatial attention mechanism, and in early visual areas to bind together the distributed featural representation of a target object

HistoMIL: A Python package for training multiple instance learning models on histopathology slides

Hematoxylin and eosin (H&E) stained slides are widely used in disease diagnosis. Remarkable advances in deep learning have made it possible to detect complex molecular patterns in these histopathology slides, suggesting automated approaches could help inform pathologists’ decisions. Multiple instance learning (MIL) algorithms have shown promise in this context, outperforming transfer learning (TL) methods for various tasks, but their implementation and usage remains complex. We introduce HistoMIL, a Python package designed to streamline the implementation, training and inference process of MIL-based algorithms for computational pathologists and biomedical researchers. It integrates a self-supervised learning module for feature encoding, and a full pipeline encompassing TL and three MIL algorithms: ABMIL, DSMIL, and TransMIL. The PyTorch Lightning framework enables effortless customization and algorithm implementation. We illustrate HistoMIL's capabilities by building predictive models for 2,487 cancer hallmark genes on breast cancer histology slides, achieving AUROC performances of up to 85%

Introduction in IND and recursive partitioning

This manual describes the IND package for learning tree classifiers from data. The package is an integrated C and C shell re-implementation of tree learning routines such as CART, C4, and various MDL and Bayesian variations. The package includes routines for experiment control, interactive operation, and analysis of tree building. The manual introduces the system and its many options, gives a basic review of tree learning, contains a guide to the literature and a glossary, and lists the manual pages for the routines and instructions on installation