13,968 research outputs found

    A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network

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    Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. This work designs a customized workflow for the small and imbalanced data set of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patients stratification

    Data-Efficient Machine Learning with Focus on Transfer Learning

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    Machine learning (ML) has attracted a significant amount of attention from the artifi- cial intelligence community. ML has shown state-of-art performance in various fields, such as signal processing, healthcare system, and natural language processing (NLP). However, most conventional ML algorithms suffer from three significant difficulties: 1) insufficient high-quality training data, 2) costly training process, and 3) domain dis- crepancy. Therefore, it is important to develop solutions for these problems, so the future of ML will be more sustainable. Recently, a new concept, data-efficient ma- chine learning (DEML), has been proposed to deal with the current bottlenecks of ML. Moreover, transfer learning (TL) has been considered as an effective solution to address the three shortcomings of conventional ML. Furthermore, TL is one of the most active areas in the DEML. Over the past ten years, significant progress has been made in TL. In this dissertation, I propose to address the three problems by developing a software- oriented framework and TL algorithms. Firstly, I introduce a DEML framework and a evaluation system. Moreover, I present two novel TL algorithms and applications on real-world problems. Furthermore, I will first present the first well-defined DEML framework and introduce how it can address the challenges in ML. After that, I will give an updated overview of the state-of-the-art and open challenges in the TL. I will then introduce two novel algorithms for two of the most challenging TL topics: distant domain TL and cross-modality TL (image-text). A detailed algorithm introduction and preliminary results on real-world applications (Covid-19 diagnosis and image clas- sification) will be presented. Then, I will discuss the current trends in TL algorithms and real-world applications. Lastly, I will present the conclusion and future research directions

    Transfer Learning in Medical Image Classification: Challenges and Opportunities

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    Transfer Learning is currently popular in Medical Image classification. Transfer Learning methods are extensively applied with CNN’s such as Res-net, Densenet, VGG16, Inception, etc. for various medical diagnosis tasks. CNN’s are around since the 1980s, but 60-80 percent of the TL research in MIC is done in the last three years. While CNN’s can be traditionally used as they are, they have been ensembled, segmented and improvised in recent days to resolve multiple MIC problems. This Review identified three main challenges in implementing Transfer Learning for Medical Image Classification (1) Overparameterization of deep CNN’s (2) Expensive Computations and (3) Insufficient availability of labeled data in the Medical field. The study also identified the opportunities in the form of Light-weight architectures and Multi-stage Transfer Learning which could potentially mitigate the above-mentioned challenges
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