An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features, and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches

Siamese Instance Search for Tracking

In this paper we present a tracker, which is radically different from state-of-the-art trackers: we apply no model updating, no occlusion detection, no combination of trackers, no geometric matching, and still deliver state-of-the-art tracking performance, as demonstrated on the popular online tracking benchmark (OTB) and six very challenging YouTube videos. The presented tracker simply matches the initial patch of the target in the first frame with candidates in a new frame and returns the most similar patch by a learned matching function. The strength of the matching function comes from being extensively trained generically, i.e., without any data of the target, using a Siamese deep neural network, which we design for tracking. Once learned, the matching function is used as is, without any adapting, to track previously unseen targets. It turns out that the learned matching function is so powerful that a simple tracker built upon it, coined Siamese INstance search Tracker, SINT, which only uses the original observation of the target from the first frame, suffices to reach state-of-the-art performance. Further, we show the proposed tracker even allows for target re-identification after the target was absent for a complete video shot.Comment: This paper is accepted to the IEEE Conference on Computer Vision and Pattern Recognition, 201

Taxonomic revision of the genus Lactarius (Russulales, Basidiomycota) in Korea

The genus Lactarius Pers. (Russulales) is a cosmopolitan group of Basidiomycota that forms ectomycorrhizal relationships primarily with both deciduous and coniferous trees. Although the genus has been well-studied in Europe and North America, only fragmentary researches have been carried out on Asian species. In particular, the distribution of Lactarius species in South Korea is poorly understood due to insufficient morphological descriptions and a lack of DNA sequence data. In addition, the misuse of European and North American names has added to confusion regarding the taxonomy of Asian Lactarius species. In this study, the diversity of Lactarius in South Korea was evaluated by employing both morphological and phylogenetic approaches. A multi-locus phylogenetic analysis of 729 Lactarius specimens collected between 1960 and 2017 was performed using the internal transcribed spacer (ITS) region, partial nuclear ribosomal large subunit (nrLSU), partial second largest subunit of RNA polymerase II (rpb2), and minichromosome maintenance complex component 7 (mcm7). 49 Lactarius species were identified in three Lactarius subgenera: L. subg. Russularia (17 spp.), L. subg. Lactarius (22 spp.), and L. subg. Plinthogalus (10 spp.). Among them, 28 Lactarius species were identified as new to science, while just 17 were previously described Lactarius species. Four of the taxa remain un-named due to paucity of materials. A key to Korean Lactarius species, molecular phylogenies, a summary of diversity, and detailed description are provided

Origin, evolution and dynamic context of a Neoglacial lateral-frontal moraine at Austre Lovénbreen, Svalbard

Moraines marking the Neoglacial limits in Svalbard are commonly ice cored. Investigating the nature of this relict ice is important because it can aid our understanding of former glacier dynamics. This paper examines the composition of the lateral–frontal moraine associated with the Neoglacial limit at Austre Lovénbreen and assesses the likely geomorphological evolution. The moraine was investigated using ground-penetrating radar (GPR), with context being provided by structural mapping of the glacier based on an oblique aerial image from 1936 and vertical aerial imagery from 2003. Multiple up-glacier dipping reflectors and syncline structures are found in the GPR surveys. The reflectors are most clearly defined in lateral positions, where the moraine is substantially composed of ice. The frontal area of the moraine is dominantly composed of debris. The core of the lateral part of the moraine is likely to consist of stacked sequences of basal ice that have been deformed by strong longitudinal compression. The long term preservation potential of the ice-dominated lateral moraine is negligible, whereas the preservation of the debris-dominated frontal moraine is high. A glacier surface bulge, identified on the 1936 aerial imagery, provides evidence that Austre Lovénbreen has previously displayed surge activity, although it is highly unlikely to do so in the near future in its current state. This research shows the value of relict buried ice that is preserved in landforms to aiding our understanding of former glacier characteristics

Scalable Nonlinear Embeddings for Semantic Category-based Image Retrieval

We propose a novel algorithm for the task of supervised discriminative distance learning by nonlinearly embedding vectors into a low dimensional Euclidean space. We work in the challenging setting where supervision is with constraints on similar and dissimilar pairs while training. The proposed method is derived by an approximate kernelization of a linear Mahalanobis-like distance metric learning algorithm and can also be seen as a kernel neural network. The number of model parameters and test time evaluation complexity of the proposed method are O(dD) where D is the dimensionality of the input features and d is the dimension of the projection space - this is in contrast to the usual kernelization methods as, unlike them, the complexity does not scale linearly with the number of training examples. We propose a stochastic gradient based learning algorithm which makes the method scalable (w.r.t. the number of training examples), while being nonlinear. We train the method with up to half a million training pairs of 4096 dimensional CNN features. We give empirical comparisons with relevant baselines on seven challenging datasets for the task of low dimensional semantic category based image retrieval.Comment: ICCV 2015 preprin