Learning with Limited Labeled Data in Biomedical Domain by Disentanglement and Semi-Supervised Learning

In this dissertation, we are interested in improving the generalization of deep neural networks for biomedical data (e.g., electrocardiogram signal, x-ray images, etc). Although deep neural networks have attained state-of-the-art performance and, thus, deployment across a variety of domains, similar performance in the clinical setting remains challenging due to its ineptness to generalize across unseen data (e.g., new patient cohort). We address this challenge of generalization in the deep neural network from two perspectives: 1) learning disentangled representations from the deep network, and 2) developing efficient semi-supervised learning (SSL) algorithms using the deep network. In the former, we are interested in designing specific architectures and objective functions to learn representations, where variations in the data are well separated, i.e., disentangled. In the latter, we are interested in designing regularizers that encourage the underlying neural function\u27s behavior toward a common inductive bias to avoid over-fitting the function to small labeled data. Our end goal is to improve the generalization of the deep network for the diagnostic model in both of these approaches. In disentangled representations, this translates to appropriately learning latent representations from the data, capturing the observed input\u27s underlying explanatory factors in an independent and interpretable way. With data\u27s expository factors well separated, such disentangled latent space can then be useful for a large variety of tasks and domains within data distribution even with a small amount of labeled data, thus improving generalization. In developing efficient semi-supervised algorithms, this translates to utilizing a large volume of the unlabelled dataset to assist the learning from the limited labeled dataset, commonly encountered situation in the biomedical domain. By drawing ideas from different areas within deep learning like representation learning (e.g., autoencoder), variational inference (e.g., variational autoencoder), Bayesian nonparametric (e.g., beta-Bernoulli process), learning theory (e.g., analytical learning theory), function smoothing (Lipschitz Smoothness), etc., we propose several leaning algorithms to improve generalization in the associated task. We test our algorithms on real-world clinical data and show that our approach yields significant improvement over existing methods. Moreover, we demonstrate the efficacy of the proposed models in the benchmark data and simulated data to understand different aspects of the proposed learning methods. We conclude by identifying some of the limitations of the proposed methods, areas of further improvement, and broader future directions for the successful adoption of AI models in the clinical environment

Data efficient deep learning for medical image analysis: A survey

The rapid evolution of deep learning has significantly advanced the field of medical image analysis. However, despite these achievements, the further enhancement of deep learning models for medical image analysis faces a significant challenge due to the scarcity of large, well-annotated datasets. To address this issue, recent years have witnessed a growing emphasis on the development of data-efficient deep learning methods. This paper conducts a thorough review of data-efficient deep learning methods for medical image analysis. To this end, we categorize these methods based on the level of supervision they rely on, encompassing categories such as no supervision, inexact supervision, incomplete supervision, inaccurate supervision, and only limited supervision. We further divide these categories into finer subcategories. For example, we categorize inexact supervision into multiple instance learning and learning with weak annotations. Similarly, we categorize incomplete supervision into semi-supervised learning, active learning, and domain-adaptive learning and so on. Furthermore, we systematically summarize commonly used datasets for data efficient deep learning in medical image analysis and investigate future research directions to conclude this survey.Comment: Under Revie

CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW

Semi-supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data. In certain cases, it enables the large numbers of unlabeled data required to be utilized in comparison with usually limited collections of labeled data. In standard classification methods in machine learning, only a labeled collection is used to train the classifier. In addition, labelled instances are difficult to acquire since they necessitate the assistance of annotators, who serve in an occupation that is identified by their label. A complete audit without a supervisor is fairly easy to do, but nevertheless represents a significant risk to the enterprise, as there have been few chances to safely experiment with it so far. By utilizing a large number of unsupervised inputs along with the supervised inputs, the semi-supervised learning solves this issue, to create a good training sample. Since semi-supervised learning requires fewer human effort and allows greater precision, both theoretically or in practice, it is of critical interest

Enhancing Breast Cancer Prediction Using Unlabeled Data

Selles väitekirjas esitatakse sildistamata andmeid kasutav süvaõppe lähenemine rinna infiltratiivse duktaalse kartsinoomi koeregioonide automaatseks klassifitseerimiseks rinnavähi patoloogilistes digipreparaatides. Süvaõppe meetodite tööpõhimõte on sarnane inimajule, mis töötab samuti mitmetel tõlgendustasanditel. Need meetodid on osutunud tulemuslikeks ka väga keerukate probleemide nagu pildiliigituse ja esemetuvastuse lahendamisel, ületades seejuures varasemate lahendusviiside efektiivsust. Süvaõppeks on aga vaja suurt hulka sildistatud andmeid, mida võib olla keeruline saada, eriti veel meditsiinis, kuna nii haiglad kui ka patsiendid ei pruugi olla nõus sedavõrd delikaatset teavet loovutama. Lisaks sellele on masinõppesüsteemide saavutatavate aina paremate tulemuste hinnaks nende süsteemide sisemise keerukuse kasv. Selle sisemise keerukuse tõttu muutub raskemaks ka nende süsteemide töö mõistmine, mistõttu kasutajad ei kipu neid usaldama. Meditsiinilisi diagnoose ei saa järgida pimesi, kuna see võib endaga kaasa tuua patsiendi tervise kahjustamise. Mudeli mõistetavuse tagamine on seega oluline viis süsteemi usaldatavuse tõstmiseks, eriti just masinõppel põhinevate mudelite laialdasel rakendamisel sellistel kriitilise tähtsusega aladel nagu seda on meditsiin. Infiltratiivne duktaalne kartsinoom on üks levinumaid ja ka agressiivsemaid rinnavähi vorme, moodustades peaaegu 80% kõigist juhtumitest. Selle diagnoosimine on patoloogidele väga keerukas ja ajakulukas ülesanne, kuna nõuab võimalike pahaloomuliste kasvajate avastamiseks paljude healoomuliste piirkondade uurimist. Samas on infiltratiivse duktaalse kartsinoomi digipatoloogias täpne piiritlemine vähi agressiivsuse hindamise aspektist ülimalt oluline. Käesolevas uurimuses kasutatakse konvolutsioonilist närvivõrku arendamaks välja infiltratiivse duktaalse kartsinoomi diagnoosimisel rakendatav pooleldi juhitud õppe skeem. Välja pakutud raamistik suurendab esmalt väikest sildistatud andmete hulka generatiivse võistlusliku võrgu loodud sünteetiliste meditsiiniliste kujutistega. Seejärel kasutatakse juba eelnevalt treenitud võrku, et selle suurendatud andmekogumi peal läbi viia kujutuvastus, misjärel sildistamata andmed sildistatakse andmesildistusalgoritmiga. Töötluse tulemusena saadud sildistatud andmeid eelmainitud konvolutsioonilisse närvivõrku sisestades saavutatakse rahuldav tulemus: ROC kõvera alla jääv pindala ja F1 skoor on vastavalt 0.86 ja 0.77. Lisaks sellele võimaldavad välja pakutud mõistetavuse tõstmise tehnikad näha ka meditsiinilistele prognooside otsuse tegemise protsessi seletust, mis omakorda teeb süsteemi usaldamise kasutajatele lihtsamaks. Käesolev uurimus näitab, et konvolutsioonilise närvivõrgu tehtud otsuseid aitab paremini mõista see, kui kasutajatele visualiseeritakse konkreetse juhtumi puhul infiltratiivse duktaalse kartsinoomi positiivse või negatiivse otsuse langetamisel süsteemi jaoks kõige olulisemaks osutunud piirkondi.The following thesis presents a deep learning (DL) approach for automatic classification of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BC) using unlabeled data. DL methods are similar to the way the human brain works across different interpretation levels. These techniques have shown to outperform traditional approaches of the most complex problems such as image classification and object detection. However, DL requires a broad set of labeled data that is difficult to obtain, especially in the medical field as neither the hospitals nor the patients are willing to reveal such sensitive information. Moreover, machine learning (ML) systems are achieving better performance at the cost of becoming increasingly complex. Because of that, they become less interpretable that causes distrust from the users. Model interpretability is a way to enhance trust in a system. It is a very desirable property, especially crucial with the pervasive adoption of ML-based models in the critical domains like the medical field. With medical diagnostics, the predictions cannot be blindly followed as it may result in harm to the patient. IDC is one of the most common and aggressive subtypes of all breast cancers accounting nearly 80% of them. Assessment of the disease is a very time-consuming and challenging task for pathologists, as it involves scanning large swatches of benign regions to identify an area of malignancy. Meanwhile, accurate delineation of IDC in WSI is crucial for the estimation of grading cancer aggressiveness. In the following study, a semi-supervised learning (SSL) scheme is developed using the deep convolutional neural network (CNN) for IDC diagnosis. The proposed framework first augments a small set of labeled data with synthetic medical images, generated by the generative adversarial network (GAN) that is followed by feature extraction using already pre-trained network on the larger dataset and a data labeling algorithm that labels a much broader set of unlabeled data. After feeding the newly labeled set into the proposed CNN model, acceptable performance is achieved: the AUC and the F-measure accounting for 0.86, 0.77, respectively. Moreover, proposed interpretability techniques produce explanations for medical predictions and build trust in the presented CNN. The following study demonstrates that it is possible to enable a better understanding of the CNN decisions by visualizing areas that are the most important for a particular prediction and by finding elements that are the reasons for IDC, Non-IDC decisions made by the network

Classification of lung diseases using deep learning models

Although deep learning-based models show high performance in the medical field, they required large volumes of data which is problematic due to the protection of patient privacy and lack of publically available medical databases. In this thesis, we address the problem of medical data scarcity by considering the task of pulmonary disease detection in chest X-Ray images using small volume datasets (<1000 samples). We implement three deep convolution neural networks pre-trained on the ImageNet dataset (VGG16, ResNet-50, and InveptionV3) and asses them in the lung disease classification tasks transfer learning approach. We created a pipeline that applied segmentation on Chest X-Ray images before classifying them and we compared the performance of our framework with the existing one. We demonstrated that pre-trained models and simple classifiers such as shallow neural networks can compete with the complex systems. We also implemented activation maps for our system. The analysis of class activation maps shows that not only does the segmentation improve results in terms of accuracy but also focuses models on medically relevant areas of lungs. We validated our techniques on the publicly available Shenzhen and Montgomery datasets and compared them to the currently available solutions. Our method was able to reach the same level of accuracy as the best performing models trained on the Montgomery dataset however, the advantage of our approach is a smaller number of trainable parameters. What is more, our InceptionV3 based model almost tied with the best performing solution on the Shenzhen dataset but as previously, it is computationally less expensive