Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction

Sparse-view cone-beam CT (CBCT) reconstruction is an important direction to reduce radiation dose and benefit clinical applications. Previous voxel-based generation methods represent the CT as discrete voxels, resulting in high memory requirements and limited spatial resolution due to the use of 3D decoders. In this paper, we formulate the CT volume as a continuous intensity field and develop a novel DIF-Net to perform high-quality CBCT reconstruction from extremely sparse (fewer than 10) projection views at an ultrafast speed. The intensity field of a CT can be regarded as a continuous function of 3D spatial points. Therefore, the reconstruction can be reformulated as regressing the intensity value of an arbitrary 3D point from given sparse projections. Specifically, for a point, DIF-Net extracts its view-specific features from different 2D projection views. These features are subsequently aggregated by a fusion module for intensity estimation. Notably, thousands of points can be processed in parallel to improve efficiency during training and testing. In practice, we collect a knee CBCT dataset to train and evaluate DIF-Net. Extensive experiments show that our approach can reconstruct CBCT with high image quality and high spatial resolution from extremely sparse views within 1.6 seconds, significantly outperforming state-of-the-art methods. Our code will be available at https://github.com/xmed-lab/DIF-Net.Comment: MICCAI'2

SCA-PVNet: Self-and-Cross Attention Based Aggregation of Point Cloud and Multi-View for 3D Object Retrieval

To address 3D object retrieval, substantial efforts have been made to generate highly discriminative descriptors of 3D objects represented by a single modality, e.g., voxels, point clouds or multi-view images. It is promising to leverage the complementary information from multi-modality representations of 3D objects to further improve retrieval performance. However, multi-modality 3D object retrieval is rarely developed and analyzed on large-scale datasets. In this paper, we propose self-and-cross attention based aggregation of point cloud and multi-view images (SCA-PVNet) for 3D object retrieval. With deep features extracted from point clouds and multi-view images, we design two types of feature aggregation modules, namely the In-Modality Aggregation Module (IMAM) and the Cross-Modality Aggregation Module (CMAM), for effective feature fusion. IMAM leverages a self-attention mechanism to aggregate multi-view features while CMAM exploits a cross-attention mechanism to interact point cloud features with multi-view features. The final descriptor of a 3D object for object retrieval can be obtained via concatenating the aggregated features from both modules. Extensive experiments and analysis are conducted on three datasets, ranging from small to large scale, to show the superiority of the proposed SCA-PVNet over the state-of-the-art methods

ME-PCN: Point Completion Conditioned on Mask Emptiness

Point completion refers to completing the missing geometries of an object from incomplete observations. Main-stream methods predict the missing shapes by decoding a global feature learned from the input point cloud, which often leads to deficient results in preserving topology consistency and surface details. In this work, we present ME-PCN, a point completion network that leverages `emptiness' in 3D shape space. Given a single depth scan, previous methods often encode the occupied partial shapes while ignoring the empty regions (e.g. holes) in depth maps. In contrast, we argue that these `emptiness' clues indicate shape boundaries that can be used to improve topology representation and detail granularity on surfaces. Specifically, our ME-PCN encodes both the occupied point cloud and the neighboring `empty points'. It estimates coarse-grained but complete and reasonable surface points in the first stage, followed by a refinement stage to produce fine-grained surface details. Comprehensive experiments verify that our ME-PCN presents better qualitative and quantitative performance against the state-of-the-art. Besides, we further prove that our `emptiness' design is lightweight and easy to embed in existing methods, which shows consistent effectiveness in improving the CD and EMD scores.Comment: Accepted to ICCV 2021; typos correcte

DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation

The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs), a field that has seen substantial growth in recent years. With the swift advancement of large language models, such as ChatGPT, the need to bridge the gap between the brain and languages becomes increasingly pressing. Current methods, however, require eye-tracking fixations or event markers to segment brain dynamics into word-level features, which can restrict the practical application of these systems. These event markers may not be readily available or could be challenging to acquire during real-time inference, and the sequence of eye fixations may not align with the order of spoken words. To tackle these issues, we introduce a novel framework, DeWave, that integrates discrete encoding sequences into open-vocabulary EEG-to-text translation tasks. DeWave uses a quantized variational encoder to derive discrete codex encoding and align it with pre-trained language models. This discrete codex representation brings forth two advantages: 1) it alleviates the order mismatch between eye fixations and spoken words by introducing text-EEG contrastive alignment training, and 2) it minimizes the interference caused by individual differences in EEG waves through an invariant discrete codex. Our model surpasses the previous baseline (40.1 and 31.7) by 3.06% and 6.34%, respectively, achieving 41.35 BLEU-1 and 33.71 Rouge-F on the ZuCo Dataset. Furthermore, this work is the first to facilitate the translation of entire EEG signal periods without needing word-level order markers (e.g., eye fixations), scoring 20.5 BLEU-1 and 29.5 Rouge-1 on the ZuCo Dataset, respectively. Codes and the final paper will be public soon

Task-Aware Sampling Layer for Point-Wise Analysis

Sampling, grouping, and aggregation are three important components in the multi-scale analysis of point clouds. In this paper, we present a novel data-driven sampler learning strategy for point-wise analysis tasks. Unlike the widely used sampling technique, Farthest Point Sampling (FPS), we propose to learn sampling and downstream applications jointly. Our key insight is that uniform sampling methods like FPS are not always optimal for different tasks: sampling more points around boundary areas can make the point-wise classification easier for segmentation. Towards this end, we propose a novel sampler learning strategy that learns sampling point displacement supervised by task-related ground truth information and can be trained jointly with the underlying tasks. We further demonstrate our methods in various point-wise analysis tasks, including semantic part segmentation, point cloud completion, and keypoint detection. Our experiments show that jointly learning of the sampler and task brings better performance than using FPS in various point-based networks.Comment: 14 pages, 13 figures and 14 table

Penerapan Pendekatan Pengajaran Terbalik (Reciprocal Teaching) Untuk Meningkatkan Kemandirian Belajar Biologi Siswa Kelas Vii-g SMP N 5 Karanganyar Tahun Pelajaran 2010/ 2011

– The objective of this study is to improve student independence in learning biology by implementing Inverted Teaching Approach (Reciprocal Teaching) on Environmental Management material. This research is a classroom action research. This research was conducted in two cycles. Each cycle consisted of planning, implementation of the action,observation, and reflection. The subjects of the study were VII-G class students of SMP Negeri 5 Karanganyar in the academic year of 2010/2011. The number of the students was 32. The technique and instrumen of collectiing data were questionnaire, observation, and interviews. The technique of analyzing data was descriptive analysis techniques. Triangulation technique was used in data validation. The results proved that by implementing Inverted Teaching Approach (Reciprocal Teaching) students\u27 independence in learning biology enhanced. It is based on the results of questionnaires, observations and interviews. The questionnaire of students\u27 learning independence showed that the mean percentage of students\u27 achievement in each indicator in pre-cycle, cycle I, and cycle II was 67.97%, 72.55%, and 77.58% respectively. The observation of students\u27 learning independence showed that the mean percentage of students\u27 achievement in each indicator in pre-cycle, cycle I, and cycle II was 39.68%, 67.5%, and 80.62% respectively. It can be concluded that the implementation of Inverted Teaching Approach (Reciprocal Teaching) can enhance students learning independence

Światowa produktywność badań w dziedzinie endokrynologii i metabolizmu — analiza bibliometryczna

  Introduction: Recently, significant contributions to the study of endocrinology and metabolism have been made. The national contribution, however, has not been reported. The aim of this study was to assess national efforts in the field of endocrinology and metabolism. Material and methods: A Web of Science search was performed using subject categories “endocrinology & metabolism” to identify articles published from 2010 to 2014. The total and per capita numbers of articles and citations were analysed for different countries. Results: A total of 79,394 articles were published on endocrinology and metabolism from 2010 to 2014. Most were published in North America, East Asia, and Europe. The majority (82.28%) were reported by authors in high-income countries, 17.64% were published in middle-income countries, and only 0.08% were published in low-income countries. Authors in the United States published the most articles (27.38%), followed by China (7.22%), Italy (5.70%), the United Kingdom (5.6%), and Japan (5.54%). Articles published by authors in the United States had the most citations (260,934). A positive correlation was found between the number of publications and population/gross domestic product (GDP; p < 0.01). When normalised to population size, the ranking for the most publications was Denmark, Sweden, and the Netherlands; when normalised to GDP, the ranking was Denmark, Greece, and the Netherlands. Conclusions: The majority of endocrinology and metabolism articles were published by authors from high-income countries with few from low-income countries. The United States was the most productive country. However, when population size and GDP were considered, some European countries were ranked higher. (Endokrynol Pol 2015; 66 (5): 434–442)    Wstęp: Ostatnio pojawiło się wiele znaczących publikacji na temat badań z dziedziny endokrynologii i metabolizmu. Narodowy wkład na tym polu został jednak pominięty. Celem niniejszego badania była ocena krajowych badań w dziedzinie endokrynologii i metabolizmu. Materiał i metody: Wyszukiwanie za pomocą Web of Science przeprowadzono z wykorzystaniem kategorii podmiotowych „endokrynologia i metabolizm”, aby zidentyfikować artykuły opublikowane w latach 2010–2014. Analizie poddano łączną liczbę artykułów i cytowań, a także ich liczbę przypadającą na osobę w odniesieniu do różnych krajów. Wyniki: W latach 2010–2014 opublikowano łącznie 79 394 artykułów na temat endokrynologii i metabolizmu. Większość artykułów pochodziła z Ameryki Północnej, Azji Wschodniej i Europy. Większość artykułów (82,28%) napisali autorzy z krajów o wysokich dochodach, 17,64% opublikowano w krajach średnio zamożnych, a jedynie 0,08% artykułów opublikowano w krajach o niskich dochodach. Najwięcej artykułów publikowali autorzy ze Stanów Zjednoczonych (27,38%), następnie z Chin (7,22%), Włoch (5,70%), Wielkiej Brytanii (5,6%) i Japonii (5,54%). Prace publikowane przez amerykańskich autorów zawierały największą liczbę cytowań (260 934). Stwierdzono pozytywny związek między liczbą publikacji i populacją/produktem krajowym brutto (PKB; p < 0,01). Po unormalizowaniu do liczebności populacji, w rankingu krajów o najwyższej liczbie publikacji znalazły się Dania, Szwecja oraz Holandia. Gdy znormalizowano wyniki pod względem PKB, w rankingu znalazły się Dania, Grecja oraz Holandia. Wnioski: Większość artykułów z dziedziny endokrynologii i metabolizmu została opublikowana przez autorów z krajów o wysokich dochodach; w krajach o niskich dochodach ukazało się niewiele artykułów. Stany Zjednoczone wykazały największą produktywność, jednak kiedy brano pod uwagę liczebność populacji i PKB, niektóre kraje europejskie zajmowały wyższą pozycję. (Endokrynol Pol 2015; 66 (5): 434–442)