Segmentation with mixed supervision: Confidence maximization helps knowledge distillation

Despite achieving promising results in a breadth of medical image segmentation tasks, deep neural networks require large training datasets with pixel-wise annotations. Obtaining these curated datasets is a cumbersome process which limits the applicability in scenarios. Mixed supervision is an appealing alternative for mitigating this obstacle. In this work, we propose a dual-branch architecture, where the upper branch (teacher) receives strong annotations, while the bottom one (student) is driven by limited supervision and guided by the upper branch. Combined with a standard cross-entropy loss over the labeled pixels, our novel formulation integrates two important terms: (i) a Shannon entropy loss defined over the less-supervised images, which encourages confident student predictions in the bottom branch; and (ii) a KL divergence term, which transfers the knowledge (i.e., predictions) of the strongly supervised branch to the less-supervised branch and guides the entropy (student-confidence) term to avoid trivial solutions. We show that the synergy between the entropy and KL divergence yields substantial improvements in performance. We also discuss an interesting link between Shannon-entropy minimization and standard pseudo-mask generation, and argue that the former should be preferred over the latter for leveraging information from unlabeled pixels. We evaluate the effectiveness of the proposed formulation through a series of quantitative and qualitative experiments using two publicly available datasets. Results demonstrate that our method significantly outperforms other strategies for semantic segmentation within a mixed-supervision framework, as well as recent semi-supervised approaches. Our code is publicly available: https://github.com/by-liu/ConfKD.Comment: To be published at Medical Image Analysis (Volume 83, January 2023). Code: https://github.com/by-liu/ConfKD. Note: this article is a journal extension of our paper in IPMI 2021 arXiv:2012.0805

Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length

Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task which can be sped up with semi-automated techniques. In this article, we present a suggestive deep active learning framework that seeks to minimise the annotation effort required to achieve a certain level of accuracy when labelling such a stack. The framework suggests, at every iteration, a specific region of interest (ROI) in one of the images for manual delineation. Using a deep segmentation neural network and a mixed cross-entropy loss function, we propose a principled strategy to estimate class probabilities for the whole stack, conditioned on heterogeneous partial segmentations of the 2D images, as well as on weak supervision in the form of image indices that bound each ROI. Using the estimated probabilities, we propose a novel active learning criterion based on predictions for the estimated segmentation performance and delineation effort, measured with average Dice scores and total delineated boundary length, respectively, rather than common surrogates such as entropy. The query strategy suggests the ROI that is expected to maximise the ratio between performance and effort, while considering the adjacency of structures that may have already been labelled – which decrease the length of the boundary to trace. We provide quantitative results on synthetically deformed MRI scans and real histological data, showing that our framework can reduce labelling effort by up to 60–70% without compromising accuracy

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

Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions

Deep learning has seen rapid growth in recent years and achieved state-of-the-art performance in a wide range of applications. However, training models typically requires expensive and time-consuming collection of large quantities of labeled data. This is particularly true within the scope of medical imaging analysis (MIA), where data are limited and labels are expensive to be acquired. Thus, label-efficient deep learning methods are developed to make comprehensive use of the labeled data as well as the abundance of unlabeled and weak-labeled data. In this survey, we extensively investigated over 300 recent papers to provide a comprehensive overview of recent progress on label-efficient learning strategies in MIA. We first present the background of label-efficient learning and categorize the approaches into different schemes. Next, we examine the current state-of-the-art methods in detail through each scheme. Specifically, we provide an in-depth investigation, covering not only canonical semi-supervised, self-supervised, and multi-instance learning schemes, but also recently emerged active and annotation-efficient learning strategies. Moreover, as a comprehensive contribution to the field, this survey not only elucidates the commonalities and unique features of the surveyed methods but also presents a detailed analysis of the current challenges in the field and suggests potential avenues for future research.Comment: Update Few-shot Method

Symbiotic deep learning for medical image analysis with applications in real-time diagnosis for fetal ultrasound screening

The last hundred years have seen a monumental rise in the power and capability of machines to perform intelligent tasks in the stead of previously human operators. This rise is not expected to slow down any time soon and what this means for society and humanity as a whole remains to be seen. The overwhelming notion is that with the right goals in mind, the growing influence of machines on our every day tasks will enable humanity to give more attention to the truly groundbreaking challenges that we all face together. This will usher in a new age of human machine collaboration in which humans and machines may work side by side to achieve greater heights for all of humanity. Intelligent systems are useful in isolation, but the true benefits of intelligent systems come to the fore in complex systems where the interaction between humans and machines can be made seamless, and it is this goal of symbiosis between human and machine that may democratise complex knowledge, which motivates this thesis. In the recent past, datadriven methods have come to the fore and now represent the state-of-the-art in many different fields. Alongside the shift from rule-based towards data-driven methods we have also seen a shift in how humans interact with these technologies. Human computer interaction is changing in response to data-driven methods and new techniques must be developed to enable the same symbiosis between man and machine for data-driven methods as for previous formula-driven technology. We address five key challenges which need to be overcome for data-driven human-in-the-loop computing to reach maturity. These are (1) the ’Categorisation Challenge’ where we examine existing work and form a taxonomy of the different methods being utilised for data-driven human-in-the-loop computing; (2) the ’Confidence Challenge’, where data-driven methods must communicate interpretable beliefs in how confident their predictions are; (3) the ’Complexity Challenge’ where the aim of reasoned communication becomes increasingly important as the complexity of tasks and methods to solve also increases; (4) the ’Classification Challenge’ in which we look at how complex methods can be separated in order to provide greater reasoning in complex classification tasks; and finally (5) the ’Curation Challenge’ where we challenge the assumptions around bottleneck creation for the development of supervised learning methods.Open Acces