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The Milky Way in-situ halo, also known as the Splash, consists of old (Age $>$ 10 Gyr), metal-rich ([Fe/H] $> -0.7$), high-$α$ stars, i.e., thick disk-like chemistry, on halo-like orbits (eccentricity > 0.6). Its origin is linked to stars formed in the disk and dynamically heated by either internal or external agents. In this work, we investigate its low-$α$ counterpart, the low-$α$ Splash, motivated by recent findings of an old thin disk population. We conjecture that any mechanism capable of heating disk stars should affect both of present-day high- and low-$α$ old populations. Using data from the APOGEE DR17 spectroscopic catalog, we identify metal-rich low-$α$ stars with halo-like kinematics similar to those of the classical high-$α$ Splash. We investigate their possible heating mechanisms using the GASTRO suite of simulations, which allows us to explore the effects of star-forming clumps as well as a major merger in the proto-disk of a Milky Way analog galaxy. Our main results show that only clumpy Milky Way models are able to produce Splash populations through scattering by clumps in the early Galaxy, including the low-$α$ counterpart, whereas the model including onl

This work is corresponding to the Gastro Competition for multi-label classification from capsule endoscopic videos (CEV). Deep learning network based on Transformers are fined-tune for this task. The based online mode is Google Vision Transformer (ViT) batch16 with 224 x 224 resolutions. In total, 17 labels are classified, which are mouth, esophagus, stomach, small intestine, colon, z-line, pylorus, ileocecal valve, active bleeding, angiectasia, blood, erosion, erythema, hematin, lymphangioectasis, polyp, and ulcer. For test dataset of three videos, the overall mAP @0.5 is 0.0205 whereas the overall mAP @0.95 is 0.0196.

We present PRISM-Consult, a clinician-aligned panel-of-experts architecture that extends the compact PRISM sequence model into a routed family of domain specialists. Episodes are tokenized as structured clinical events; a light-weight router reads the first few tokens and dispatches to specialist models (Cardiac-Vascular, Pulmonary, Gastro-Oesophageal, Musculoskeletal, Psychogenic). Each specialist inherits PRISM's small transformer backbone and token template, enabling parameter efficiency and interpretability. This initial study evaluates a scoped panel of five specialist families defined by high-impact ED diagnostic groups. On real-world Emergency Department cohorts, specialists exhibit smooth convergence with low development perplexities across domains, while the router achieves high routing quality and large compute savings versus consult-all under a safety-first policy. We detail the data methodology (initial vs.\ conclusive ICD-9 families), routing thresholds and calibration, and report per-domain results to avoid dominance by common events. The framework provides a practical path to safe, auditable, and low-latency consult at scale, and we outline validation steps-external/

VQA (Visual Question Answering) combines Natural Language Processing (NLP) with image understanding to answer questions about a given image. It has enormous potential for the development of medical diagnostic AI systems. Such a system can help clinicians diagnose gastro-intestinal (GI) diseases accurately and efficiently. Although many of the multimodal LLMs available today have excellent VQA capabilities in the general domain, they perform very poorly for VQA tasks in specialized domains such as medical imaging. This study is a submission for ImageCLEFmed-MEDVQA-GI 2025 subtask 1 that explores the adaptation of the Florence2 model to answer medical visual questions on GI endoscopy images. We also evaluate the model performance using standard metrics like ROUGE, BLEU and METEOR

Based on global genomic status, the cancer tumor is classified as Microsatellite Instable (MSI) and Microsatellite Stable (MSS). Immunotherapy is used to diagnose MSI, whereas radiation and chemotherapy are used for MSS. Therefore, it is significant to classify a gastro-intestinal (GI) cancer tumor into MSI vs. MSS to provide appropriate treatment. The existing literature showed that deep learning could directly predict the class of GI cancer tumors from histological images. However, deep learning (DL) models are susceptible to various threats, including membership inference attacks, model extraction attacks, etc. These attacks render the use of DL models impractical in real-world scenarios. To make the DL models useful and maintain privacy, we integrate differential privacy (DP) with DL. In particular, this paper aims to predict the state of GI cancer while preserving the privacy of sensitive data. We fine-tuned the Normalizer Free Net (NF-Net) model. We obtained an accuracy of 88.98\% without DP to predict (GI) cancer status. When we fine-tuned the NF-Net using DP-AdamW and adaptive DP-AdamW, we got accuracies of 74.58% and 76.48%, respectively. Moreover, we investigate the Weigh

Generalizable dense feature matching in endoscopic images is crucial for robot-assisted tasks, including 3D reconstruction, navigation, and surgical scene understanding. Yet, it remains a challenge due to difficult visual conditions (e.g., weak textures, large viewpoint variations) and a scarcity of annotated data. To address these challenges, we propose EndoMatcher, a generalizable endoscopic image matcher via large-scale, multi-domain data pre-training. To address difficult visual conditions, EndoMatcher employs a two-branch Vision Transformer to extract multi-scale features, enhanced by dual interaction blocks for robust correspondence learning. To overcome data scarcity and improve domain diversity, we construct Endo-Mix6, the first multi-domain dataset for endoscopic matching. Endo-Mix6 consists of approximately 1.2M real and synthetic image pairs across six domains, with correspondence labels generated using Structure-from-Motion and simulated transformations. The diversity and scale of Endo-Mix6 introduce new challenges in training stability due to significant variations in dataset sizes, distribution shifts, and error imbalance. To address them, a progressive multi-objectiv

n clinical, if a patient presents with nonmechanical obstructive dysphagia, esophageal chest pain, and gastro esophageal reflux symptoms, the physician will usually assess the esophageal dynamic function. High-resolution manometry (HRM) is a clinically commonly used technique for detection of esophageal dynamic function comprehensively and objectively. However, after the results of HRM are obtained, doctors still need to evaluate by a variety of parameters. This work is burdensome, and the process is complex. We conducted image processing of HRM to predict the esophageal contraction vigor for assisting the evaluation of esophageal dynamic function. Firstly, we used Feature-Extraction and Histogram of Gradients (FE-HOG) to analyses feature of proposal of swallow (PoS) to further extract higher-order features. Then we determine the classification of esophageal contraction vigor normal, weak and failed by using linear-SVM according to these features. Our data set includes 3000 training sets, 500 validation sets and 411 test sets. After verification our accuracy reaches 86.83%, which is higher than other common machine learning methods.

We demonstrate training of a Generative Adversarial Network (GAN) for prediction of optical property maps (scattering and absorption) using spatial frequency domain imaging (SFDI) image data sets generated synthetically with free open-source 3D modelling and rendering software, Blender. The flexibility of Blender is exploited to simulate 3 models with real-life relevance to clinical SFDI of diseased tissue: flat samples, flat samples with spheroidal tumours and cylindrical samples with spheroidal tumours representing imaging inside a tubular organ e.g. the gastro-intestinal tract. In all 3 scenarios we show the GAN provides accurate reconstruction of optical properties from single SFDI images with mean normalised error ranging from 1-1.2% for absorption and 0.7-1.2% for scattering, resulting in visually improved contrast for tumour spheroid structures. This compares favourably with 25% absorption error and 10% scattering error achieved using GANs on experimental SFDI data. However, some of this improvement is due to lower noise and availability of perfect ground truths so we therefore cross-validate our synthetically-trained GAN with a GAN trained on experimental data and observe v

Gastro-Intestinal Tract cancer is considered a fatal malignant condition of the organs in the GI tract. Due to its fatality, there is an urgent need for medical image segmentation techniques to segment organs to reduce the treatment time and enhance the treatment. Traditional segmentation techniques rely upon handcrafted features and are computationally expensive and inefficient. Vision Transformers have gained immense popularity in many image classification and segmentation tasks. To address this problem from a transformers' perspective, we introduced a hybrid CNN-transformer architecture to segment the different organs from an image. The proposed solution is robust, scalable, and computationally efficient, with a Dice and Jaccard coefficient of 0.79 and 0.72, respectively. The proposed solution also depicts the essence of deep learning-based automation to improve the effectiveness of the treatment

Highly imbalanced datasets are ubiquitous in medical image classification problems. In such problems, it is often the case that rare classes associated to less prevalent diseases are severely under-represented in labeled databases, typically resulting in poor performance of machine learning algorithms due to overfitting in the learning process. In this paper, we propose a novel mechanism for sampling training data based on the popular MixUp regularization technique, which we refer to as Balanced-MixUp. In short, Balanced-MixUp simultaneously performs regular (i.e., instance-based) and balanced (i.e., class-based) sampling of the training data. The resulting two sets of samples are then mixed-up to create a more balanced training distribution from which a neural network can effectively learn without incurring in heavily under-fitting the minority classes. We experiment with a highly imbalanced dataset of retinal images (55K samples, 5 classes) and a long-tail dataset of gastro-intestinal video frames (10K images, 23 classes), using two CNNs of varying representation capabilities. Experimental results demonstrate that applying Balanced-MixUp outperforms other conventional sampling sc

In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is emerging as a novel, minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Since the development of this technology, medical device companies and many research groups have made substantial progress in converting passive capsule endoscopes to robotic active capsule endoscopes with most of the functionality of current active flexible endoscopes. However, robotic capsule endoscopy still has some challenges. In particular, the use of such devices to generate a precise three-dimensional (3D) mapping of the entire inner organ remains an unsolved problem. Such global 3D maps of inner organs would help doctors to detect the location and size of diseased areas more accurately and intuitively, thus permitting more reliable diagnoses. To our knowledge, this paper presents the first complete pipeline for a complete 3D visual map reconstruction of the stomach. The proposed pipeline is modular and includes a preprocessing module, an image registration module, and a final shape-from-shading-based 3D reconstruction module; the 3D

This paper describes the five development stages of the rope worm, which could be human parasite. Rope worms have been discovered as a result of cleansing enemas. Thousands or people have passed the rope worms from all over the World. Adult stages live in human gastro-intestinal tract and are anaerobic. They move inside the body by releasing gas bubbles utilizing jet propulsion. These worms look like a rope, and can be over a meter long. The development stages were identified based on their morphology. The fifth stage looks like a tough string of mucus about a meter long. The fourth stage looks similar, but the rope worm is shorter and has softer slimier body. The third stage looks like branched jellyfish. The second stage is viscous snot, or mucus with visible gas bubbles that act as suction cups. The first stage is slimier mucus with fewer bubbles, which can reside almost anywhere in the body. Rope worms have cellular structure, based on optical microscopy, DAPI staining and DNA analysis, however, the data collected is not sufficient to identify the specie. Removal methods are also mentioned in the paper.

The aim of the current study was to investigate the morphological structure of one of the most common reptilian species in Egypt, Varanus niloticus or Nile monitor. Specimens for histological examination were collected from the esophageus, stomach and small intestine of the Nile monitor and processed for paraffin embedding. Sections were stained with haematoxylin and eosin for general morphology. Periodic Acid Schiff's (PAS) and Alcian Blue (AB) staining methods were applied to detect the different types of the mucous con-tents of the gastro-intestinal tract. Some paraffin sections were stained with Grimelius silver impregnation technique for localization of the enteroendocrine cells. The folded esophageal mucosa had ciliated columnar epithelium with mucous secreting goblet cells, which stained positive with PAS and AB. The esophageal mucosa was folded and the lining epithelium was ciliated columnar epithelium with mucous secreting goblet cells, which stained positive with PAS and AB. The stomach was divided into fundic and pyloric regions. The mucosa was thrown into gastric pits, into which the gastric glands opened. The surface epithelium was mucous secreting columnar cells and s

New US rules would legalize quiet supersonic flights without the sonic boom