Multimix: Sparingly-Supervised, Extreme Multitask Learning from Medical Images

Haque, Ayaan; Imran, Abdullah-Al-Zubaer; Wang, Adam; Terzopoulos, Demetri

doi:10.1109/isbi48211.2021.9434167

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…This will improve the generalizability of the CNN models. MTL can be defined as the optimization of several losses in a single model such that shared representation learning can execute related tasks [4]. Multi-task learning in the context of advanced learning is often carried out using either soft or hard parameters sharing of the convolution layer.…”

Section: Multi-task Learning Methodsmentioning

confidence: 99%

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A Multi-Task Deep Learning Framework for Simultaneous Detection of Thoracic Pathology through Image Classification

Zahrani,

Hedjar,

Mekhtiche

et al. 2024

JCC

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Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person's ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model.

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Section: Multi-task Learning Methodsmentioning

confidence: 99%

“…This will improve the generalization of the model. MTL can be defined as the optimization of several loss functions in a single model such that shared representation learning can execute related tasks [4].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Task Deep Learning Framework for Simultaneous Detection of Thoracic Pathology through Image Classification

Zahrani,

Hedjar,

Mekhtiche

et al. 2024

JCC

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“…Comparisons with multi-task learning methods. We further compared the performance of the proposed model with several state-of-the-art MTL methods [34,63,64] for normal tissue and lesion discrimination on the THH dataset to verify the effectiveness of our proposed cross-task guidance. Based on Table 4, CTG-Net has a significant advantage on most of the metrics compared to other methods.…”

Section: Plos Onementioning

confidence: 99%

CTG-Net: Cross-task guided network for breast ultrasound diagnosis

Yang

Suzuki

et al. 2022

PLoS ONE

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Deep learning techniques have achieved remarkable success in lesion segmentation and classification between benign and malignant tumors in breast ultrasound images. However, existing studies are predominantly focused on devising efficient neural network-based learning structures to tackle specific tasks individually. By contrast, in clinical practice, sonographers perform segmentation and classification as a whole; they investigate the border contours of the tissue while detecting abnormal masses and performing diagnostic analysis. Performing multiple cognitive tasks simultaneously in this manner facilitates exploitation of the commonalities and differences between tasks. Inspired by this unified recognition process, this study proposes a novel learning scheme, called the cross-task guided network (CTG-Net), for efficient ultrasound breast image understanding. CTG-Net integrates the two most significant tasks in computerized breast lesion pattern investigation: lesion segmentation and tumor classification. Further, it enables the learning of efficient feature representations across tasks from ultrasound images and the task-specific discriminative features that can greatly facilitate lesion detection. This is achieved using task-specific attention models to share the prediction results between tasks. Then, following the guidance of task-specific attention soft masks, the joint feature responses are efficiently calibrated through iterative model training. Finally, a simple feature fusion scheme is used to aggregate the attention-guided features for efficient ultrasound pattern analysis. We performed extensive experimental comparisons on multiple ultrasound datasets. Compared to state-of-the-art multi-task learning approaches, the proposed approach can improve the Dice’s coefficient, true-positive rate of segmentation, AUC, and sensitivity of classification by 11%, 17%, 2%, and 6%, respectively. The results demonstrate that the proposed cross-task guided feature learning framework can effectively fuse the complementary information of ultrasound image segmentation and classification tasks to achieve accurate tumor localization. Thus, it can aid sonographers to detect and diagnose breast cancer.

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“…Semi-supervised learning combines a small amount of labeled data with a large amount of unlabeled data during training (Chapelle, Scholkopf, and Zien 2009). As a result, semi-supervised learning has received much attention as an alternative to fully-labeled datasets, and many works and methods have been proposed (Chen et al 2020;Sohn et al 2020;Haque et al 2020;Laine and Aila 2017;Imran et al 2020;Sun et al 2019;Imran and Terzopoulos 2019b). In many real-world tasks, there are large datasets with only a small subset of labeled data, as annotations require domain expertise, are expensive, and are time consuming.…”

Section: Introductionmentioning

confidence: 99%

EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs

Haque

2020

Preprint

Self Cite

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Semi-supervised learning has been gaining attention as it allows for performing image analysis tasks such as classification with limited labeled data. Some popular algorithms using Generative Adversarial Networks (GANs) for semisupervised classification share a single architecture for classification and discrimination. However, this may require a model to converge to a separate data distribution for each task, which may reduce overall performance. While progress in semi-supervised learning has been made, less addressed are small-scale, fully-supervised tasks where even unlabeled data is unavailable and unattainable. We therefore, propose a novel GAN model namely External Classifier GAN (EC-GAN), that utilizes GANs and semi-supervised algorithms to improve classification in fully-supervised regimes. Our method leverages a GAN to generate artificial data used to supplement supervised classification. More specifically, we attach an external classifier, hence the name EC-GAN, to the GAN's generator, as opposed to sharing an architecture with the discriminator. Our experiments demonstrate that EC-GAN's performance is comparable to the shared architecture method, far superior to the standard data augmentation and regularization-based approach, and effective on a small, realistic dataset. 1

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Multimix: Sparingly-Supervised, Extreme Multitask Learning from Medical Images

Cited by 7 publications

References 7 publications

A Multi-Task Deep Learning Framework for Simultaneous Detection of Thoracic Pathology through Image Classification

A Multi-Task Deep Learning Framework for Simultaneous Detection of Thoracic Pathology through Image Classification

CTG-Net: Cross-task guided network for breast ultrasound diagnosis

EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs

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