Automatic lymphocyte detection on gastric cancer IHC images using deep learning

Tumor-infiltrating lymphocytes (TILs) have received considerable attention in recent years, as evidence suggests they are related to cancer prognosis. Distribution and localization of these and other types of immune cells are of special interest for pathologists, and frequently involve manual examination on Immunohistochemistry (IHC) Images. We present a model based on Deep Convolutional Neural Networks for Automatic lymphocyte detection on IHC images of gastric cancer. The dataset created as part of this work is publicly available for future research.Tesi

Towards Interpretable Machine Learning in Medical Image Analysis

Over the past few years, ML has demonstrated human expert level performance in many medical image analysis tasks. However, due to the black-box nature of classic deep ML models, translating these models from the bench to the bedside to support the corresponding stakeholders in the desired tasks brings substantial challenges. One solution is interpretable ML, which attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, interpretability is not a property of the ML model but an affordance, i.e., a relationship between algorithm and user. Thus, prototyping and user evaluations are critical to attaining solutions that afford interpretability. Following human-centered design principles in highly specialized and high stakes domains, such as medical image analysis, is challenging due to the limited access to end users. This dilemma is further exacerbated by the high knowledge imbalance between ML designers and end users. To overcome the predicament, we first define 4 levels of clinical evidence that can be used to justify the interpretability to design ML models. We state that designing ML models with 2 levels of clinical evidence: 1) commonly used clinical evidence, such as clinical guidelines, and 2) iteratively developed clinical evidence with end users are more likely to design models that are indeed interpretable to end users. In this dissertation, we first address how to design interpretable ML in medical image analysis that affords interpretability with these two different levels of clinical evidence. We further highly recommend formative user research as the first step of the interpretable model design to understand user needs and domain requirements. We also indicate the importance of empirical user evaluation to support transparent ML design choices to facilitate the adoption of human-centered design principles. All these aspects in this dissertation increase the likelihood that the algorithms afford interpretability and enable stakeholders to capitalize on the benefits of interpretable ML. In detail, we first propose neural symbolic reasoning to implement public clinical evidence into the designed models for various routinely performed clinical tasks. We utilize the routinely applied clinical taxonomy for abnormality classification in chest x-rays. We also establish a spleen injury grading system by strictly following the clinical guidelines for symbolic reasoning with the detected and segmented salient clinical features. Then, we propose the entire interpretable pipeline for UM prognostication with cytopathology images. We first perform formative user research and found that pathologists believe cell composition is informative for UM prognostication. Thus, we build a model to analyze cell composition directly. Finally, we conduct a comprehensive user study to assess the human factors of human-machine teaming with the designed model, e.g., whether the proposed model indeed affords interpretability to pathologists. The human-centered design process is proven to be truly interpretable to pathologists for UM prognostication. All in all, this dissertation introduces a comprehensive human-centered design for interpretable ML solutions in medical image analysis that affords interpretability to end users

Descripción automática de imágenes

[ES] El propósito de este Trabajo es el estudio, implementación, y desarrollo de sistemas, basados en Deep Learning, orientados a la generación automática de descripciones de imágenes o Image Captioning. Este campo aúna las áreas del Procesamiento del Lenguaje Natural (PLN), y de la Visión por Computador (VPC). Antes proceder a la implementación, se ha realizado un análisis de los diferentes enfoques utilizados para abordar esta tarea, los corpus disponibles formato: [Imagen - Caption/s], y las arquitecturas o modelos utilizados. Tras este análisis, se ha optado, inicialmente, por abordarlo desde el enfoque más usual: basado en modelos del lenguaje, con una arquitectura Codificador-Decodificador. Para ello, se realiza una codificación de: las descripciones (captions) a un espacio vectorial de Embeddings Word2Vec, por una parte, y, por otra, las imágenes haciendo uso redes convolucionales CNN. Con esta información codificada, el Decodificador es el encargado de aprender un Modelo del Lenguaje con redes neuronales recurrentes RNN capaz de Generar descripciones. Las diferentes implementaciones de este trabajo se han realizado bajo la plataforma de software Python, empleando la biblioteca de código abierto TensorFlow, orientada al entrenamiento de modelos de Aprendizaje automático, y el framework de alto nivel para el aprendizaje, Keras.[EN] The purpose of this work is the study, implementation, and development of Deep Learning systems, oriented to Image Captioning. This field combines the areas of Natural Language Processing (NLP) and Computer Vision (CV). Before proceeding to the implementation, an analysis of the different approaches used to tackle this task has been carried out, the available corpora with format: [Image - Caption/s], and the architectures or models used. After this analysis, it has been chosen, initially, to approach it from the most usual approach: based on language models, with an Encoder-Decoder architecture. For this purpose, the descriptions (captions) are encoded in a vector space of Word2Vec Embeddings on the one hand, and on the other hand, the images using CNN convolutional networks. With this encoded information, the Decoder is in charge of learning a Language Model with Recurrent Neural Network RNN capable of generating descriptions. The different implementations of this work have been carried out under the Python software platform, using the open-source library TensorFlow, oriented to the training of Machine Learning models, and the high-level framework for learning, Keras.Pallarés Font De Mora, P. (2021). Descripción automática de imágenes. Universitat Politècnica de València. http://hdl.handle.net/10251/175035TFG

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered