Jin‐Man Kim

Mingyuan Meng, Lei Bi, Dagan Feng et al.

In this study, we focus on brain tumor sequence registration between pre-operative and follow-up Magnetic Resonance Imaging (MRI) scans of brain glioma patients, in the context of Brain Tumor Sequence Registration challenge (BraTS-Reg 2022). Brain tumor registration is a fundamental requirement in brain image analysis for quantifying tumor changes. This is a challenging task due to large deformations and missing correspondences between pre-operative and follow-up scans. For this task, we adopt our recently proposed Non-Iterative Coarse-to-finE registration Networks (NICE-Net) - a deep learning-based method for coarse-to-fine registering images with large deformations. To overcome missing correspondences, we extend the NICE-Net by introducing dual deep supervision, where a deep self-supervised loss based on image similarity and a deep weakly-supervised loss based on manually annotated landmarks are deeply embedded into the NICE-Net. At the BraTS-Reg 2022, our method achieved a competitive result on the validation set (mean absolute error: 3.387) and placed 4th in the final testing phase (Score: 0.3544).

Hao Wang, Euijoon Ahn, Lei Bi et al.

The clinical diagnosis of skin lesion involves the analysis of dermoscopic and clinical modalities. Dermoscopic images provide a detailed view of the surface structures whereas clinical images offer a complementary macroscopic information. The visual diagnosis of melanoma is also based on seven-point checklist which involves identifying different visual attributes. Recently, supervised learning approaches such as convolutional neural networks (CNNs) have shown great performances using both dermoscopic and clinical modalities (Multi-modality). The seven different visual attributes in the checklist are also used to further improve the the diagnosis. The performances of these approaches, however, are still reliant on the availability of large-scaled labeled data. The acquisition of annotated dataset is an expensive and time-consuming task, more so with annotating multi-attributes. To overcome this limitation, we propose a self-supervised learning (SSL) algorithm for multi-modality skin lesion classification. Our algorithm enables the multi-modality learning by maximizing the similarities between paired dermoscopic and clinical images from different views. In addition, we generate surrogate pseudo-multi-labels that represent seven attributes via clustering analysis. We also propose a label-relation-aware module to refine each pseudo-label embedding and capture the interrelationships between pseudo-multi-labels. We validated the effectiveness of our algorithm using well-benchmarked seven-point skin lesion dataset. Our results show that our algorithm achieved better performances than other state-of-the-art SSL counterparts.

Hao Wang, Euijoon Ahn, Jinman Kim

Recent advances in supervised deep learning methods are enabling remote measurements of photoplethysmography-based physiological signals using facial videos. The performance of these supervised methods, however, are dependent on the availability of large labelled data. Contrastive learning as a self-supervised method has recently achieved state-of-the-art performances in learning representative data features by maximising mutual information between different augmented views. However, existing data augmentation techniques for contrastive learning are not designed to learn physiological signals from videos and often fail when there are complicated noise and subtle and periodic colour or shape variations between video frames. To address these problems, we present a novel self-supervised spatiotemporal learning framework for remote physiological signal representation learning, where there is a lack of labelled training data. Firstly, we propose a landmark-based spatial augmentation that splits the face into several informative parts based on the Shafer dichromatic reflection model to characterise subtle skin colour fluctuations. We also formulate a sparsity-based temporal augmentation exploiting Nyquist-Shannon sampling theorem to effectively capture periodic temporal changes by modelling physiological signal features. Furthermore, we introduce a constrained spatiotemporal loss which generates pseudo-labels for augmented video clips. It is used to regulate the training process and handle complicated noise. We evaluated our framework on 3 public datasets and demonstrated superior performances than other self-supervised methods and achieved competitive accuracy compared to the state-of-the-art supervised methods.

Lei Bi, Dagan Feng, Jinman Kim

Segmentation of skin lesions is considered as an important step in computer aided diagnosis (CAD) for automated melanoma diagnosis. In recent years, segmentation methods based on fully convolutional networks (FCN) have achieved great success in general images. This success is primarily due to the leveraging of large labelled datasets to learn features that correspond to the shallow appearance as well as the deep semantics of the images. However, the dependence on large dataset does not translate well into medical images. To improve the FCN performance for skin lesion segmentations, researchers attempted to use specific cost functions or add post-processing algorithms to refine the coarse boundaries of the FCN results. However, the performance of these methods is heavily reliant on the tuning of many parameters and post-processing techniques. In this paper, we leverage the state-of-the-art image feature learning method of generative adversarial network (GAN) for its inherent ability to produce consistent and realistic image features by using deep neural networks and adversarial learning concept. We improve upon GAN such that skin lesion features can be learned at different level of complexities, in a controlled manner. The outputs from our method is then augmented to the existing FCN training data, thus increasing the overall feature diversity. We evaluated our method on the ISIC 2018 skin lesion segmentation challenge dataset and showed that it was more accurate and robust when compared to the existing skin lesion segmentation methods.

Lei Bi, Jinman Kim, Ashnil Kumar et al.

Automatic segmentation of liver lesions is a fundamental requirement towards the creation of computer aided diagnosis (CAD) and decision support systems (CDS). Traditional segmentation approaches depend heavily upon hand-crafted features and a priori knowledge of the user. As such, these methods are difficult to adopt within a clinical environment. Recently, deep learning methods based on fully convolutional networks (FCNs) have been successful in many segmentation problems primarily because they leverage a large labelled dataset to hierarchically learn the features that best correspond to the shallow visual appearance as well as the deep semantics of the areas to be segmented. However, FCNs based on a 16 layer VGGNet architecture have limited capacity to add additional layers. Therefore, it is challenging to learn more discriminative features among different classes for FCNs. In this study, we overcome these limitations using deep residual networks (ResNet) to segment liver lesions. ResNet contain skip connections between convolutional layers, which solved the problem of the training degradation of training accuracy in very deep networks and thereby enables the use of additional layers for learning more discriminative features. In addition, we achieve more precise boundary definitions through a novel cascaded ResNet architecture with multi-scale fusion to gradually learn and infer the boundaries of both the liver and the liver lesions. Our proposed method achieved 4th place in the ISBI 2017 Liver Tumor Segmentation Challenge by the submission deadline.

Lei Bi, Jinman Kim, Euijoon Ahn et al.

Malignant melanoma has one of the most rapidly increasing incidences in the world and has a considerable mortality rate. Early diagnosis is particularly important since melanoma can be cured with prompt excision. Dermoscopy images play an important role in the non-invasive early detection of melanoma [1]. However, melanoma detection using human vision alone can be subjective, inaccurate and poorly reproducible even among experienced dermatologists. This is attributed to the challenges in interpreting images with diverse characteristics including lesions of varying sizes and shapes, lesions that may have fuzzy boundaries, different skin colors and the presence of hair [2]. Therefore, the automatic analysis of dermoscopy images is a valuable aid for clinical decision making and for image-based diagnosis to identify diseases such as melanoma [1-4]. Deep residual networks (ResNets) has achieved state-of-the-art results in image classification and detection related problems [5-8]. In this ISIC 2017 skin lesion analysis challenge [9], we propose to exploit the deep ResNets for robust visual features learning and representations.