Interpretable Medical Image Classification using Prototype Learning and Privileged Information

Interpretability is often an essential requirement in medical imaging. Advanced deep learning methods are required to address this need for explainability and high performance. In this work, we investigate whether additional information available during the training process can be used to create an understandable and powerful model. We propose an innovative solution called Proto-Caps that leverages the benefits of capsule networks, prototype learning and the use of privileged information. Evaluating the proposed solution on the LIDC-IDRI dataset shows that it combines increased interpretability with above state-of-the-art prediction performance. Compared to the explainable baseline model, our method achieves more than 6 % higher accuracy in predicting both malignancy (93.0 %) and mean characteristic features of lung nodules. Simultaneously, the model provides case-based reasoning with prototype representations that allow visual validation of radiologist-defined attributes.Comment: MICCAI 2023 Medical Image Computing and Computer Assisted Interventio

Algorithms and Applications of Novel Capsule Networks

Convolutional neural networks, despite their profound impact in countless domains, suffer from significant shortcomings. Linearly-combined scalar feature representations and max pooling operations lead to spatial ambiguities and a lack of robustness to pose variations. Capsule networks can potentially alleviate these issues by storing and routing the pose information of extracted features through their architectures, seeking agreement between the lower-level predictions of higher-level poses at each layer. In this dissertation, we make several key contributions to advance the algorithms of capsule networks in segmentation and classification applications. We create the first ever capsule-based segmentation network in the literature, SegCaps, by introducing a novel locally-constrained dynamic routing algorithm, transformation matrix sharing, the concept of a deconvolutional capsule, extension of the reconstruction regularization to segmentation, and a new encoder-decoder capsule architecture. Following this, we design a capsule-based diagnosis network, D-Caps, which builds off SegCaps and introduces a novel capsule-average pooling technique to handle to larger medical imaging data. Finally, we design an explainable capsule network, X-Caps, which encodes high-level visual object attributes within its capsules by utilizing a multi-task framework and a novel routing sigmoid function which independently routes information from child capsules to parents. Predictions come with human-level explanations, via object attributes, and a confidence score, by training our network directly on the distribution of expert labels, modeling inter-observer agreement and punishing over/under confidence during training. This body of work constitutes significant algorithmic advances to the application of capsule networks, especially in real-world biomedical imaging data

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho

Self-explaining AI as an alternative to interpretable AI

The ability to explain decisions made by AI systems is highly sought after, especially in domains where human lives are at stake such as medicine or autonomous vehicles. While it is often possible to approximate the input-output relations of deep neural networks with a few human-understandable rules, the discovery of the double descent phenomena suggests that such approximations do not accurately capture the mechanism by which deep neural networks work. Double descent indicates that deep neural networks typically operate by smoothly interpolating between data points rather than by extracting a few high level rules. As a result, neural networks trained on complex real world data are inherently hard to interpret and prone to failure if asked to extrapolate. To show how we might be able to trust AI despite these problems we introduce the concept of self-explaining AI. Self-explaining AIs are capable of providing a human-understandable explanation of each decision along with confidence levels for both the decision and explanation. For this approach to work, it is important that the explanation actually be related to the decision, ideally capturing the mechanism used to arrive at the explanation. Finally, we argue it is important that deep learning based systems include a "warning light" based on techniques from applicability domain analysis to warn the user if a model is asked to extrapolate outside its training distribution. For a video presentation of this talk see https://www.youtube.com/watch?v=Py7PVdcu7WY& .Comment: 10pgs, 2 column forma

“Just” accuracy? Procedural fairness demands explainability in AI‑based medical resource allocations

Funding for open access publishing: Universidad de Granada/ CBUA. This research is funded by the project “Detección y eliminación de sesgos en algoritmos de triaje y localización para la COVID-19” of the call Ayudas Fundación BBVA a Equipos de Investigación Científica SARS-CoV-2 y COVID-19, en el área de Humanidades. JR also thanks a La Caixa Foundation INPhINIT Retaining Fellowship (LCF/BQ/ DR20/11790005). DR-A thanks the funding of the Spanish Research Agency (codes FFI2017-88913-P and PID2020-118729RB-I00). IPJ also thanks the funding of the Spanish Research Agency (code PID2019-105422GB-I00).The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because it helps to maximize patients’ benefits and optimizes limited resources. However, we claim that the opaqueness of the algorithmic black box and its absence of explainability threatens core commitments of procedural fairness such as accountability, avoidance of bias, and transparency. To illustrate this, we discuss liver transplantation as a case of critical medical resources in which the lack of explainability in AI-based allocation algorithms is procedurally unfair. Finally, we provide a number of ethical recommendations for when considering the use of unexplainable algorithms in the distribution of health-related resources.Funding for open access publishing: Universidad de Granada/ CBUAProject “Detección y eliminación de sesgos en algoritmos de triaje y localización para la COVID-19” of the call Ayudas Fundación BBVA a Equipos de Investigación Científica SARS-CoV-2 y COVID-19, en el área de HumanidadesLa Caixa Foundation INPhINIT Retaining Fellowship (LCF/BQ/ DR20/11790005)Spanish Research Agency (codes FFI2017-88913-P and PID2020-118729RB-I00)Spanish Research Agency (code PID2019-105422GB-I00

Novel Computational Approaches For Multidimensional Brain Image Analysis

The overall goal of this dissertation is focused on addressing challenging problems in 1D, 2D/3D and 4D neuroimaging by developing novel algorithms that combine signal processing and machine learning techniques. One of these challenging tasks is the accurate localization of the eloquent language cortex in brain resection pre-surgery patients. This is especially important since inaccurate localization can lead to diminshed functionalities and thus, a poor quality of life for the patient. The first part of this dissertation addresses this problem in the case of drug-resistant epileptic patients. We propose a novel machine learning based algorithm to establish an alternate electrical stimulation-free approach, electro-corticography (ECoG) as a viable technique for localization of the eloqeunt language cortex. We process the 1D signals in frequency domain to train a classifier and identify language responsive electrodes from the surface of the brain. We then enhance the proposed approach by developing novel multi-modal deep learning algorithms. We test different aspects of the experimental paradigm and identify the best features and models for classification. Another difficult neuroimaging task is that of identifying biomarkers of a disease. This is even more challenging considering that skill acquisition leads to neurological changes. We propose to help understand these changes in the brain of chess masters via a multi-modal approach that combines 3D and 4D imaging modalities in a novel way. The proposed approaches may help narrow the regions to be tested in pre-surgical localization tasks and in better surgery planning. The proposed work may also pave the way for a holistic view of the human brain by combining several modalities into one. Finally, we deal with the problem of learning strong signal representations/features by proposing a novel capsule based variational autoencoder, B-Caps. The proposed B-Caps helps in learning a strong feature representation that can be used with multi-dimensional data

“Just” accuracy? Procedural fairness demands explainability in AI‑based medical resource allocation

The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because it helps to maximize patients’ benefits and optimizes limited resources. However, we claim that the opaqueness of the algorithmic black box and its absence of explainability threatens core commitments of procedural fairness such as accountability, avoidance of bias, and transparency. To illustrate this, we discuss liver transplantation as a case of critical medical resources in which the lack of explainability in AI-based allocation algorithms is procedurally unfair. Finally, we provide a number of ethical recommendations for when considering the use of unexplainable algorithms in the distribution of health-related resources

Deep Learning-based Solutions to Improve Diagnosis in Wireless Capsule Endoscopy

[eng] Deep Learning (DL) models have gained extensive attention due to their remarkable performance in a wide range of real-world applications, particularly in computer vision. This achievement, combined with the increase in available medical records, has made it possible to open up new opportunities for analyzing and interpreting healthcare data. This symbiotic relationship can enhance the diagnostic process by identifying abnormalities, patterns, and trends, resulting in more precise, personalized, and effective healthcare for patients. Wireless Capsule Endoscopy (WCE) is a non-invasive medical imaging technique used to visualize the entire Gastrointestinal (GI) tract. Up to this moment, physicians meticulously review the captured frames to identify pathologies and diagnose patients. This manual process is time- consuming and prone to errors due to the challenges of interpreting the complex nature of WCE procedures. Thus, it demands a high level of attention, expertise, and experience. To overcome these drawbacks, shorten the screening process, and improve the diagnosis, efficient and accurate DL methods are required. This thesis proposes DL solutions to the following problems encountered in the analysis of WCE studies: pathology detection, anatomical landmark identification, and Out-of-Distribution (OOD) sample handling. These solutions aim to achieve robust systems that minimize the duration of the video analysis and reduce the number of undetected lesions. Throughout their development, several DL drawbacks have appeared, including small and imbalanced datasets. These limitations have also been addressed, ensuring that they do not hinder the generalization of neural networks, leading to suboptimal performance and overfitting. To address the previous WCE problems and overcome the DL challenges, the proposed systems adopt various strategies that utilize the power advantage of Triplet Loss (TL) and Self-Supervised Learning (SSL) techniques. Mainly, TL has been used to improve the generalization of the models, while SSL methods have been employed to leverage the unlabeled data to obtain useful representations. The presented methods achieve State-of-the-art results in the aforementioned medical problems and contribute to the ongoing research to improve the diagnostic of WCE studies.[cat] Els models d’aprenentatge profund (AP) han acaparat molta atenció a causa del seu rendiment en una àmplia gamma d'aplicacions del món real, especialment en visió per ordinador. Aquest fet, combinat amb l'increment de registres mèdics disponibles, ha permès obrir noves oportunitats per analitzar i interpretar les dades sanitàries. Aquesta relació simbiòtica pot millorar el procés de diagnòstic identificant anomalies, patrons i tendències, amb la conseqüent obtenció de diagnòstics sanitaris més precisos, personalitzats i eficients per als pacients. La Capsula endoscòpica (WCE) és una tècnica d'imatge mèdica no invasiva utilitzada per visualitzar tot el tracte gastrointestinal (GI). Fins ara, els metges revisen minuciosament els fotogrames capturats per identificar patologies i diagnosticar pacients. Aquest procés manual requereix temps i és propens a errors. Per tant, exigeix un alt nivell d'atenció, experiència i especialització. Per superar aquests inconvenients, reduir la durada del procés de detecció i millorar el diagnòstic, es requereixen mètodes eficients i precisos d’AP. Aquesta tesi proposa solucions que utilitzen AP per als següents problemes trobats en l'anàlisi dels estudis de WCE: detecció de patologies, identificació de punts de referència anatòmics i gestió de mostres que pertanyen fora del domini. Aquestes solucions tenen com a objectiu aconseguir sistemes robustos que minimitzin la durada de l'anàlisi del vídeo i redueixin el nombre de lesions no detectades. Durant el seu desenvolupament, han sorgit diversos inconvenients relacionats amb l’AP, com ara conjunts de dades petits i desequilibrats. Aquestes limitacions també s'han abordat per assegurar que no obstaculitzin la generalització de les xarxes neuronals, evitant un rendiment subòptim. Per abordar els problemes anteriors de WCE i superar els reptes d’AP, els sistemes proposats adopten diverses estratègies que aprofiten l'avantatge de la Triplet Loss (TL) i les tècniques d’auto-aprenentatge. Principalment, s'ha utilitzat TL per millorar la generalització dels models, mentre que els mètodes d’autoaprenentatge s'han emprat per aprofitar les dades sense etiquetar i obtenir representacions útils. Els mètodes presentats aconsegueixen bons resultats en els problemes mèdics esmentats i contribueixen a la investigació en curs per millorar el diagnòstic dels estudis de WCE

Ecology of <i>Cryptococcus neoformans</i> in Vietnam

Cryptococcal meningitis is an infection with Cryptococcus species; almost all disease occurs in immunosuppressed patients due to C. neoformans. Cryptococcus spp are environmental saprophytes; the ability to cause disease is considered an accident of adaptation to their ecological niche. In Southeast Asia, cryptococcal meningitis in immunocompetent individuals is well-described and is almost always due to the C. neoformans ST5 (VNIa-5) lineage. I hypothesize this lineage has relative increased pathogenicity. The aims of this thesis were to: identify the ecological niche of C. neoformans; define the diversity of C. neoformans in Vietnam; compare the virulence of different genotypes, and; define transcriptional differences associated with virulence. I identified spatial clustering of lineages VNIa-4, VNIa-5 and VNIa-32. Environmental sampling yielded 123 cryptococci the majority of which were non-pathogenic. Using the G. mellonella infection model I identified an ‘ecological imprint’ on isolates. Within a lineage, the virulence of an isolate is determined by its source – environmental, immunosuppressed or immunocompetent patient. Moreover, the virulence of environmental C. neoformans can be upregulated by clinical strains of that lineage. This ‘induction’ effect is regulated by a protein/peptide signaling system. Comparative transcriptomics revealed 1700 genes differentially expressed between clinical and environmental isolates. Gene ontology (GO) terms relevant to metabolism and cellular biosynthesis were enriched in clinical isolates; GO terms relevant to cell cycle and cell division were significantly enriched in induced environmental isolates. Clinical and induced environmental isolates had highly similar transcriptomes. A number of genes previously confirmed as virulence determinants were upregulated in clinical and induced environmental isolates; a third of differentially expressed genes between the induced and `naive' isolates were hypothetical proteins. These are novel candidates implicated in the upregulation of virulence. My findings provide insight into ecology of C. neoformans in Vietnam and the genetic control of virulence