Deep Learning with Limited Labels for Medical Imaging

Recent advancements in deep learning-based AI technologies provide an automatic tool to revolutionise medical image computing. Training a deep learning model requires a large amount of labelled data. Acquiring labels for medical images is extremely challenging due to the high cost in terms of both money and time, especially for the pixel-wise segmentation task of volumetric medical scans. However, obtaining unlabelled medical scans is relatively easier compared to acquiring labels for those images. This work addresses the pervasive issue of limited labels in training deep learning models for medical imaging. It begins by exploring different strategies of entropy regularisation in the joint training of labelled and unlabelled data to reduce the time and cost associated with manual labelling for medical image segmentation. Of particular interest are consistency regularisation and pseudo labelling. Specifically, this work proposes a well-calibrated semi-supervised segmentation framework that utilises consistency regularisation on different morphological feature perturbations, representing a significant step towards safer AI in medical imaging. Furthermore, it reformulates pseudo labelling in semi-supervised learning as an Expectation-Maximisation framework. Building upon this new formulation, the work explains the empirical successes of pseudo labelling and introduces a generalisation of the technique, accompanied by variational inference to learn its true posterior distribution. The applications of pseudo labelling in segmentation tasks are also presented. Lastly, this work explores unsupervised deep learning for parameter estimation of diffusion MRI signals, employing a hierarchical variational clustering framework and representation learning

EM Wave Propagation Speed, Comments on “Measurement of Time Delay of Alternating Electrical Field in Wires” and “Physical Principles of Measuring the Speed of Alternating Electrical Field

The time shift of an electromagnetic wave at a single frequency between a transmitter and a receiver can be used to determine the phase velocity of the wave propagation only if there is no reflection at the receiver or the reflection is very small. The reflection adds additional phase shifts to the composted wave of an incident wave and a reflected wave so that the time difference of the composted wave is shifted between the transmitter and the receiver. This time difference may be either decreased or increased and even negative in a certain condition. Ignoring the phase shift and time shift induced by the reflection, the authors of two articles recently published on “Modern Physics” wrongly claim of “the speed of alternating electric field can be 20 times faster than the speed of light”. The two articles are: “Measurement of Time Delay of Alternating Electrical Field in Wires” (Modern Physics, 2015, 5, 29-34) and “Physical Principles of Measuring the Speed of Alternating Electrical Field” (Modern Physics, 2015, 5, 35-39). In this communication note, theory and experiments are presented to falsify their claim. 只有在没有反射或反射因素极小的情况下，在发射端和接收端电磁场的时间差可以用于计算电磁场从反 射端到接收端的传播相速度。由于反射，测量到的合成波的相位是入射波和反射波相位的合成，由反射 而引入的相位变化导致发射和接收端之间的同相位电磁波的时间差的移动。这种两点间的合成波的时间 差由于反射既能增加也可能减小，在一定的情况下，时间差还可以是负数。由于忽视因反射而引起的相 位差或时间差的变化，最近发表在《现代物理》上的两篇文章的作者们用带有反射的合成波的时间差计 算电场传播速度，并和光在自由空间里传播相速度比较，进而在两篇文章中错误地宣称：“交变电场的 速度超过光速20倍以上。”这两篇在《现代物理》上发表的文章是：“导线中交流电场时间延迟的测定” (现代物理，2015，5，29-34)和“交变电场速度测量的物理原理”(现代物理，2015，5，35-39)。此 评论文章用理论和实验数据推翻其文章“超光速20倍”的结论

The peculiar filamentary HI structure of NGC 6145

In this paper, we report the peculiar HI morphology of the cluster spiral galaxy NGC 6145, which has a 150 kpc HI filament on one side that is nearly parallel to its major axis. This filament is made up of several HI clouds and the diffuse HI gas between them, with no optical counterparts. We compare its HI distribution with other one-sided HI distributions in the literature, and find that the overall HI distribution is very different from the typical tidal and ram-pressure stripped HI shape, and its morphology is inconsistent with being a pure accretion event. Only about 30% of the total HI gas is anchored on the stellar disk, while most of HI gas forms the filament in the west. At a projected distance of 122 kpc, we find a massive elliptical companion (NGC 6146) with extended radio emission, whose axis points to an HI gap in NGC 6145. The velocity of the HI filament shows an overall light-of- sight motion of 80 to 180 km/s with respect to NGC 6145. Using the long-slit spectra of NGC 6145 along its major stellar axis, we find that some outer regions show enhanced star formation, while in contrast, almost no star formation activities are found in its center (less than 2 kpc). Pure accretion, tidal or ram-pressure stripping is not likely to produce the observed HI filament. An alternative explanation is the jet-stripping from NGC 6146, although direct evidence for a jet-cold gas interaction has not been found.Comment: 12 pages, 6 figures; Accepted for publication in A

Learning To Pay Attention To Mistakes

In convolutional neural network based medical image segmentation, the periphery of foreground regions representing malignant tissues may be disproportionately assigned as belonging to the background class of healthy tissues \cite{attenUnet}\cite{AttenUnet2018}\cite{InterSeg}\cite{UnetFrontNeuro}\cite{LearnActiveContour}. This leads to high false negative detection rates. In this paper, we propose a novel attention mechanism to directly address such high false negative rates, called Paying Attention to Mistakes. Our attention mechanism steers the models towards false positive identification, which counters the existing bias towards false negatives. The proposed mechanism has two complementary implementations: (a) "explicit" steering of the model to attend to a larger Effective Receptive Field on the foreground areas; (b) "implicit" steering towards false positives, by attending to a smaller Effective Receptive Field on the background areas. We validated our methods on three tasks: 1) binary dense prediction between vehicles and the background using CityScapes; 2) Enhanced Tumour Core segmentation with multi-modal MRI scans in BRATS2018; 3) segmenting stroke lesions using ultrasound images in ISLES2018. We compared our methods with state-of-the-art attention mechanisms in medical imaging, including self-attention, spatial-attention and spatial-channel mixed attention. Across all of the three different tasks, our models consistently outperform the baseline models in Intersection over Union (IoU) and/or Hausdorff Distance (HD). For instance, in the second task, the "explicit" implementation of our mechanism reduces the HD of the best baseline by more than $26\%$, whilst improving the IoU by more than $3\%$. We believe our proposed attention mechanism can benefit a wide range of medical and computer vision tasks, which suffer from over-detection of background.Comment: Accepted at BMVC 202

Disentangling Human Error from the Ground Truth in Segmentation of Medical Images

Recent years have seen increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depends on the quality of labels. This problem is particularly pertinent in the medical image domain, where both the annotation cost and inter-observer variability are high. In a typical label acquisition process, different human experts provide their estimates of the 'true' segmentation labels under the influence of their own biases and competence levels. Treating these noisy labels blindly as the ground truth limits the performance that automatic segmentation algorithms can achieve. In this work, we present a method for jointly learning, from purely noisy observations alone, the reliability of individual annotators and the true segmentation label distributions, using two coupled CNNs. The separation of the two is achieved by encouraging the estimated annotators to be maximally unreliable while achieving high fidelity with the noisy training data. We first define a toy segmentation dataset based on MNIST and study the properties of the proposed algorithm. We then demonstrate the utility of the method on three public medical imaging segmentation datasets with simulated (when necessary) and real diverse annotations: 1) MSLSC (multiple-sclerosis lesions); 2) BraTS (brain tumours); 3) LIDC-IDRI (lung abnormalities). In all cases, our method outperforms competing methods and relevant baselines particularly in cases where the number of annotations is small and the amount of disagreement is large. The experiments also show strong ability to capture the complex spatial characteristics of annotators' mistakes

MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels

Semi-supervised learning (SSL) is a promising machine learning paradigm to address the issue of label scarcity in medical imaging. SSL methods were originally developed in image classification. The state-of-the-art SSL methods in image classification utilise consistency regularisation to learn unlabelled predictions which are invariant to input level perturbations. However, image level perturbations violate the cluster assumption in the setting of segmentation. Moreover, existing image level perturbations are hand-crafted which could be sub-optimal. Therefore, it is a not trivial to straightforwardly adapt existing SSL image classification methods in segmentation. In this paper, we propose MisMatch, a semi-supervised segmentation framework based on the consistency between paired predictions which are derived from two differently learnt morphological feature perturbations. MisMatch consists of an encoder and two decoders. One decoder learns positive attention for foreground on unlabelled data thereby generating dilated features of foreground. The other decoder learns negative attention for foreground on the same unlabelled data thereby generating eroded features of foreground. We first develop a 2D U-net based MisMatch framework and perform extensive cross-validation on a CT-based pulmonary vessel segmentation task and show that MisMatch statistically outperforms state-of-the-art semi-supervised methods when only 6.25\% of the total labels are used. In a second experiment, we show that U-net based MisMatch outperforms state-of-the-art methods on an MRI-based brain tumour segmentation task. In a third experiment, we show that a 3D MisMatch outperforms a previous method using input level augmentations, on a left atrium segmentation task. Lastly, we find that the performance improvement of MisMatch over the baseline might originate from its better calibration

Characterization of 45º-tilted fiber grating and its polarization function in fiber ring laser

We have proposed and demonstrated a fiber ring laser with single-polarization output using an intracavity 45°-tilted fiber grating (45°-TFG). The properties of the 45°-TFG have been investigated both theoretically and experimentally. The fiber ring laser incorporating the 45°-TFG has been systematically characterized, showing a significant improvement in the polarization extinction ratio (PER) and achieving a PER of >30 dB. The slope efficiencies of the ring laser with and without the 45°-TFG have been measured. This laser shows a very stable polarized output with a PER variation of less than 2 dB for 5 hours at laboratory conditions. In addition, we also demonstrated the tunability of the laser