Reviewerly/exHarmony huggingface dataset:
Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images

We propose Boundary-RL, a novel weakly supervised segmentation method that utilises only patch-level labels for training. We envision segmentation as a boundary detection problem, rather than a pixel-level classification as in previous works. This outlook on segmentation may allow for boundary delineation under challenging scenarios such as where noise artefacts may be present within the region-of-interest (ROI) boundaries, where traditional pixel-level classification-based weakly supervised methods may not be able to effectively segment the ROI. Particularly of interest, ultrasound images, where intensity values represent acoustic impedance differences between boundaries, may also benefit from the boundary delineation approach. Our method uses reinforcement learning to train a controller function to localise boundaries of ROIs using a reward derived from a pre-trained boundary-presence classifier. The classifier indicates when an object boundary is encountered within a patch, serving as weak supervision, as the controller modifies the patch location in a sequential Markov decision process. The classifier itself is trained using only binary patch-level labels of object presence, the only labels used during training of the entire boundary delineation framework. The use of a controller ensures that sliding window over the entire image is not necessary and reduces possible false-positives or -negatives by minimising number of patches passed to the boundary-presence classifier. We evaluate our approach for a clinically relevant task of prostate gland segmentation on trans-rectal ultrasound images. We show improved performance compared to other tested weakly supervised methods, using the same labels e.g., multiple instance learning.
More from Less: Self-supervised Knowledge Distillation for Routine Histopathology Data

Medical imaging technologies are generating increasingly large amounts of high-quality, information-dense data. Despite the progress, practical use of advanced imaging technologies for research and diagnosis remains limited by cost and availability, so more information-sparse data such as H &E stains are relied on in practice. The study of diseased tissue would greatly benefit from methods which can leverage these information-dense data to extract more value from routine, information-sparse data. Using self-supervised learning (SSL), we demonstrate that it is possible to distil knowledge during training from information-dense data into models which only require information-sparse data for inference. This improves downstream classification accuracy on information-sparse data, making it comparable with the fully-supervised baseline. We find substantial effects on the learned representations, and pairing with relevant data can be used to extract desirable features without the arduous process of manual labelling. This approach enables the design of models which require only routine images, but contain insights from state-of-the-art data, allowing better use of the available resources.
Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos Using NeRFs

Given that a conventional laparoscope only provides a two-dimensional (2-D) view, the detection and diagnosis of medical ailments can be challenging. To overcome the visual constraints associated with laparoscopy, the use of laparoscopic images and videos to reconstruct the three-dimensional (3-D) anatomical structure of the abdomen has proven to be a promising approach. Neural Radiance Fields (NeRFs) have recently gained attention thanks to their ability to generate photorealistic images from a 3-D static scene, thus facilitating a more comprehensive exploration of the abdomen through the synthesis of new views. This distinguishes NeRFs from alternative methods such as Simultaneous Localization and Mapping (SLAM) and depth estimation. In this paper, we present a comprehensive examination of NeRFs in the context of laparoscopy surgical videos, with the goal of rendering abdominal scenes in 3-D. Although our experimental results are promising, the proposed approach encounters substantial challenges, which require further exploration in future research.
Self-training with Domain-Mixed Data for Few-Shot Domain Adaptation in Medical Image Segmentation Tasks

Deep learning has shown significant progress in medical image analysis tasks such as semantic segmentation. However, deep learning models typically require large amounts of annotated data to achieve high accuracy; often a limiting factor in medical applications where labeled data is scarce. Few-shot domain adaptation (FSDA) is one approach to address this problem. It adapts a model trained on a source domain to a target domain which includes a few labeled data. In this paper, we present an FSDA method adapting pre-trained models to the target domain via domain-mixed data in the self-training framework. Our network follows the traditional encoder-decoder structure, which consists of a Transformer encoder, a DeeplabV3+ decoder for segmentation tasks and an auxiliary decoder for boundary-supervised learning. Our approach fine-tunes the source-domain pre-trained model with a few labeled examples from the target domain and by including unlabeled target domain data as well. We evaluate our method on two commonly used publicly available datasets for optic disc/cup and polyp segmentation, and show that it outperforms other state-of-the-art FSDA methods with only 5 labeled examples in the target domain. Overall, our FSDA method shows promising results and has potential to be applied to other medical imaging tasks with limited labeled data in the target domain.
NCIS: Deep Color Gradient Maps Regression and Three-Class Pixel Classification for Enhanced Neuronal Cell Instance Segmentation in Nissl-Stained Histological Images

Deep learning has proven to be more effective than other methods in medical image analysis, including the seemingly simple but challenging task of segmenting individual cells, an essential step for many biological studies. Comparative neuroanatomy studies are an example where the instance segmentation of neuronal cells is crucial for cytoarchitecture characterization. This paper presents an end-to-end framework to automatically segment single neuronal cells in Nissl-stained histological images of the brain, thus aiming to enable solid morphological and structural analyses for the investigation of changes in the brain cytoarchitecture. A U-Net-like architecture with an EfficientNet as the encoder and two decoding branches is exploited to regress four color gradient maps and classify pixels into contours between touching cells, cell bodies, or background. The decoding branches are connected through attention gates to share relevant features, and their outputs are combined to return the instance segmentation of the cells. The method was tested on images of the cerebral cortex and cerebellum, outperforming other recent deep-learning-based approaches for the instance segmentation of cells.
Contrastive Learning-Based Breast Tumor Segmentation in DCE-MRI

Precise and automated segmentation of tumors from breast dynamic contrast-enhanced magnetic resonance images (DCE-MRI) is crucial for obtaining quantitative morphological and functional information, thereby assisting subsequent diagnosis and treatment. However, many existing methods mainly focus on features within tumor regions and neglect enhanced background tissues, leading to the potential over-segmentation problem. To better distinguish tumor tissues from complex background structures (e.g., enhanced vessels), we propose a novel approach based on contrastive feature learning. Our method involves pre-training a highly sensitive encoder using contrastive learning, where tumor and background patches are utilized as paired positive-negative samples, to emphasize tumor tissues and to enhance their discriminative features. Furthermore, the well-trained encoder is employed for accurate tumor segmentation by using a feature fusion module in a global-to-local manner. Through extensive validations using a large dataset of breast DCE-MRI scans, our proposed model demonstrates superior segmentation performance, effectively reducing over-segmentation on enhanced tissue regions as expected.
Implicitly Solved Regularization for Learning-Based Image Registration

Deformable image registration is a fundamental step in many medical image analysis tasks and has attracted a large amount of research to develop efficient and accurate unsupervised machine learning approaches. Although much attention has been paid to finding suitable similarity measures, network architectures and training methods, much less research has been devoted to suitable regularization techniques to ensure the plausibility of the learned deformations. In this paper, we propose implicitly solved regularizers for unsupervised and weakly supervised learning of deformable image registration. In place of pure gradient descent with automatic differentiation, we combine efficient implicit solvers for the regularization term with the established gradient-based optimization regarding the network parameters. As a result, our approach is broadly applicable and can be combined with a range of similarity measures and network architectures. Our experiments with state-of-the-art network architectures show that the proposed approach has the potential to increase the smoothness, i.e. the plausibility, of the learned deformations and the registration accuracy measured as dice overlaps. Furthermore, we show that due to efficient GPU implementations of the implicit solvers, this increase in plausibility and accuracy comes at almost no additional cost in terms of computational time.
PE-MED: Prompt Enhancement for Interactive Medical Image Segmentation

Interactive medical image segmentation refers to the accurate segmentation of the target of interest through interaction (e.g., click) between the user and the image. It has been widely studied in recent years as it is less dependent on abundant annotated data and more flexible than fully automated segmentation. However, current studies have not fully explored user-provided prompt information (e.g., points), including the knowledge mined in one interaction, and the relationship between multiple interactions. Thus, in this paper, we introduce a novel framework equipped with prompt enhancement, called PE-MED, for interactive medical image segmentation. First, we introduce a Self-Loop strategy to generate warm initial segmentation results based on the first prompt. It can prevent the highly unfavorable scenarios, such as encountering a blank mask as the initial input after the first interaction. Second, we propose a novel Prompt Attention Learning Module (PALM) to mine useful prompt information in one interaction, enhancing the responsiveness of the network to user clicks. Last, we build a Time Series Information Propagation (TSIP) mechanism to extract the temporal relationships between multiple interactions and increase the model stability. Comparative experiments with other state-of-the-art (SOTA) medical image segmentation algorithms show that our method exhibits better segmentation accuracy and stability.
FFPN: Fourier Feature Pyramid Network for Ultrasound Image Segmentation

Ultrasound (US) image segmentation is an active research area that requires real-time and highly accurate analysis in many scenarios. The detect-to-segment (DTS) frameworks have been recently proposed to balance accuracy and efficiency. However, existing approaches may suffer from inadequate contour encoding or fail to effectively leverage the encoded results. In this paper, we introduce a novel Fourier-anchor-based DTS framework called Fourier Feature Pyramid Network (FFPN) to address the aforementioned issues. The contributions of this paper are two fold. First, the FFPN utilizes Fourier Descriptors to adequately encode contours. Specifically, it maps Fourier series with similar amplitudes and frequencies into the same layer of the feature map, thereby effectively utilizing the encoded Fourier information. Second, we propose a Contour Sampling Refinement (CSR) module based on the contour proposals and refined features produced by the FFPN. This module extracts rich features around the predicted contours to further capture detailed information and refine the contours. Extensive experimental results on three large and challenging datasets demonstrate that our method outperforms other DTS methods in terms of accuracy and efficiency. Furthermore, our framework can generalize well to other detection or segmentation tasks.
Differential Evolution Using Enhanced Mutation Strategy Based on Random Neighbor Selection

Symmetry in a differential evolution (DE) transforms a solution without impacting the family of solutions. For symmetrical problems in differential equations, DE is a strong evolutionary algorithm that provides a powerful solution to resolve global optimization problems. DE/best/1 and DE/rand/1 are the two most commonly used mutation strategies in DE. The former provides better exploitation while the latter ensures better exploration. DE/Neighbor/1 is an improved form of DE/rand/1 to maintain a balance between exploration and exploitation which was used with a random neighbor-based differential evolution (RNDE) algorithm. However, this mutation strategy slows down convergence. It should achieve a global minimum by using 1000 × D, where D is the dimension, but due to exploration and exploitation balancing trade-offs, it can not achieve a global minimum within the range of 1000 × D in some of the objective functions. To overcome this issue, a new and enhanced mutation strategy and algorithm have been introduced in this paper, called DE/Neighbor/2, as well as an improved random neighbor-based differential evolution algorithm. The new DE/Neighbor/2 mutation strategy also uses neighbor information such as DE/Neighbor/1; however, in addition, we add weighted differences after various tests. The DE/Neighbor/2 and IRNDE algorithm has also been tested on the same 27 commonly used benchmark functions on which the DE/Neighbor/1 mutation strategy and RNDE were tested. Experimental results demonstrate that the DE/Neighbor/2 mutation strategy and IRNDE algorithm show overall better and faster convergence than the DE/Neighbor/1 mutation strategy and RNDE algorithm. The parametric significance test shows that there is a significance difference in the performance of RNDE and IRNDE algorithms at the 0.05 level of significance.
Background and geochemical baseline values of chalcophile and siderophile elements in soils around the former mining area of Abbadia San Salvatore (Mt. Amiata, southern Tuscany, Italy)

Background and geochemical baseline values of Potentially Toxic Elements (PTEs) in soils developed on natural or anthropogenically-affected areas are increasingly important parameters as they represent highly useful tools at local, regional, national and European level. While background values are mostly applied to uncontaminated areas to verify the presence of high to anomalous concentrations of PTEs due to geogenic sources, the geochemical baseline values are better representing those areas where the anthropogenic activities have altered more or less significantly the background values. This is the case of the Municipality of Abbadia San Salvatore, mostly lying above the volcanic products of Mt. Amiata (Tuscany, central Italy) and, subordinately, above sedimentary formations, where >100 years of exploitation of cinnabar (HgS) and production of liquid mercury have affected the environmental matrices, including the pedosphere compartment. Consequently, 51 top-soils and 51 sub-soils, developed on the volcanic and sedimentary domains were collected and analyzed for PTEs (Hg, As, Sb, Cr, Co, Ni, Cu and V). A CoDA (Compositional Data Analysis) approach (robust Principal Component Analysis) combined with geochemical mapping allowed to account for the compositional nature of geochemical data and define the spatial distribution of the investigated chalcophile and siderophile elements. The results obtained by CoDa are useful to understand data variability and relative variations of the elements compared to the barycenter of the total composition. Independently by the substratum (volcanic or sedimentary), the concentrations of Hg, As, Sb, Cr, Co, Ni, Cu and V were highly variable and up to 258, 276, 110, 261, 32, 132, 80 and 173 mg/kg, respectively. Owing to the significant differences between the two geological domains, the two datasets were considered separately in order to define the geochemical baseline values of the analyzed PTEs computed using ProUCL 5.2.0 software. Accordingly, the geochemical baseline values of Hg, As, Cr, Co and V were much higher than the values imposed by the Italian Legislative Decree 152/06, suggesting the importance of exploring the geological features of the investigated territories and their anthropic history, particularly in Italy where poly-metallic sulfide ore deposits have been exploited in many regions since the Etruscan and Roman times.
Introduction to a recently released dataset entitled CSNCD: A Comprehensive Dataset of Chinese Seismic Network

CSN-GTSD is a large comprehensive seismological data set first released by the China Earthquake Networks Center Center. This dataset includes all the events and annotation information in the official report of China's unified earthquake cataloging, and the relevant annotations are manually annotated by the national station network cataloging personnel. The data has high reliability and can be used as important reference data for many data-driven studies. So far, the CSN-GTSD dataset has the longest time span published in China (2009-2022), with the highest number of events (over 1.3 million), the largest number of annotations (over 45 million), the widest epicenter distance range (0-180 °), the richest seismic phase types (over 50 types), the largest magnitude span (-2.0∼9.0 magnitudes), and the most comprehensive event types (12 types). The CSN-GTSD dataset can provide massive annotated high-quality learning samples for deep learning algorithms, constructing high-precision machine learning models. This dataset can also provide key core materials for the booming data-driven geoscience research, promote new artificial intelligence algorithms in geoscience research, and provide highly reliable annotation references for various algorithm evaluations, assisting geoscience research in the era of big data and artificial intelligence.
Prompt-based event relation identification with Constrained Prefix ATTention mechanism

Event Relation Identification (ERI) aims at mining the inter-event dependencies expressed in event-mentioned sentences. The main challenge of this task lies in recognizing the implicit clue for utterances without context words indicating the relation definitely. When confronting a lack of training samples, mainstream techniques fail to efficiently capture the subtle relations between events because the parameters of neural networks cannot be adequately fitted. Although there is a rising trend of using prompt learning to alleviate such issues, existing methods lack optimization of the prompt and prompts-tuning process. These deficiencies lead to two weaknesses: co-occurrence interference and amphibolous prompting. To this end, this paper proposes a Constrained Prefix ATTention mechanism (CPATT) and incorporates it into the traditional prompt-tuning process. In this fashion, our approach integrates context semantic features into dynamic prompts to mitigate co-occurrence interference. Moreover, CPATT supervises the guide effect of prompts via incorporating mutual exclusivity between categories into the loss function. The experimental results on two widely used datasets demonstrate that our method outperforms all state-of-the-art baselines, including GPT3.5-turbo, in terms of intra- and inter-sentence event relation identification tasks.
HEPM: High-efficiency pattern mining

Pattern mining (PM) is an important field of data mining and has gained considerable momentum recently, mainly owing to the massive growth of big data. PM often sets attentive objectives such as mining frequent or high utility patterns to obtain attractive patterns. High utility patterns address the defect of frequent patterns that cannot reveal the maximum profit. However, it neglects another vital factor, cost or investment. This paper proposes a new high-efficiency PM problem that considers both utility and investment. The problem aims to find patterns with the maximum profit-to-investment ratio. Our paper is devoted to studying high-efficiency itemsets in transaction databases. We first formulate the criteria for a high-efficiency PM problem. Subsequently, we propose a two-phase algorithm called HEPM and an improved one-phase algorithm called HEPMiner to discover high-efficiency patterns in a transaction database. We design a corresponding pruning strategy within HEPM to reduce the search space. In HEPMiner, we utilize a novel efficiency-list and an estimated efficiency co-occurrence structure in the pruning strategies to further improve the mining performance. Moreover, we derive the upper bounds of efficiency for both algorithms. The experimental results demonstrate the effectiveness and efficiency of our two algorithms.
Multi-level correlation learning for multi-view unsupervised feature selection

Recently, unsupervised multi-view feature selection has received a lot of interest. However, current methods facilitate feature selection by preserving the local consistency of multi-view data, which is defined based on similarities of data within each view, but ignore the global topological consistency in data, which is defined based on the cross-view topological similarities of data between views and is essential for revealing the distribution of multi-view data. In light of this, this paper proposes a novel multi-view unsupervised feature selection method with multi-level correlation learning, termed Multi-Level Correlation Learning for Multi-View Unsupervised Feature Selection (MLCL). It simultaneously derives the global topological correlation structure from the cross-view topological similarities of data and the local geometric correlation structure from the local similarities of data within each view, to take advantage of both global and local consistencies of multi-view data. An effective optimization algorithm is then developed to resolve the optimization problem for the proposed model. Extensive experiments on eight publicly available datasets show that the proposed MLCL outperforms several state-of-the-art unsupervised multi-view feature selection models.