Emergence of noncollinear magnetic ordering in small magnetic clusters: Mnn_n and As@Mnn_n

Using first-principles density functional calculations, we have studied the magnetic ordering in pure Mn$_n$ ($n=2-$10, 13, 15, 19) and As@Mn$_n$ ($n=1-$10) clusters. Although, for both pure and doped manganese clusters, there exists many collinear and noncollinear isomers close in energy, the smaller clusters with $n\leqslant$5 have collinear magnetic ground state and the emergence of noncollinear ground states is seen for $n\geqslant$6 clusters. Due to strong $p-d$ hybridization in As@Mn$_n$ clusters, the binding energy is substantially enhanced and the magnetic moment is reduced compared to the corresponding pure Mn$_n$ clusters.Comment: 10 Pages and 5 Figure

Surgical Aggregation: Federated Class-Heterogeneous Learning

The release of numerous chest x-ray datasets has spearheaded the development of deep learning models with expert-level performance. However, they have limited interoperability due to class-heterogeneity -- a result of inconsistent labeling schemes and partial annotations. Therefore, it is challenging to leverage these datasets in aggregate to train models with a complete representation of abnormalities that may occur within the thorax. In this work, we propose surgical aggregation, a federated learning framework for aggregating knowledge from class-heterogeneous datasets and learn a model that can simultaneously predict the presence of all disease labels present across the datasets. We evaluate our method using simulated and real-world class-heterogeneous datasets across both independent and identically distributed (iid) and non-iid settings. Our results show that surgical aggregation outperforms current methods, has better generalizability, and is a crucial first step towards tackling class-heterogeneity in federated learning to facilitate the development of clinically-useful models using previously non-interoperable chest x-ray datasets.Comment: 9 pages, 7 figures, 4 table

Technical note: Facilitating laparoscopic liver biopsy by the use of a single-handed disposable core biopsy needle

Despite the use of advanced radiological investigations, some liver lesions cannot be definitely diagnosed without a biopsy and histological examination. Laparoscopic Tru-Cut biopsy of the liver lesion is the preferred approach to achieve a good sample for histology. The mechanism of a Tru-Cut biopsy needle needs the use of both hands to load and fire the needle. This restricts the ability of the surgeon to direct the needle into the lesion utilising the laparoscopic ultrasound probe. We report a technique of laparoscopic liver biopsy using a disposable core biopsy instrument (BARD (R) disposable core biopsy needle) that can be used single-handedly. The needle can be positioned with laparoscopic graspers in order to reach posterior and superior lesions. This technique can easily be used in conjunction with laparoscopic ultrasound.M. I. Trochsler, Q. Ralph, F. Bridgewater, H. Kanhere, and Guy J. Madder

Text2Cohort: Facilitating Intuitive Access to Biomedical Data with Natural Language Cohort Discovery

The Imaging Data Commons (IDC) is a cloud-based database that provides researchers with open access to cancer imaging data, with the goal of facilitating collaboration. However, cohort discovery within the IDC database has a significant technical learning curve. Recently, large language models (LLM) have demonstrated exceptional utility for natural language processing tasks. We developed Text2Cohort, a LLM-powered toolkit to facilitate user-friendly natural language cohort discovery in the IDC. Our method translates user input into IDC queries using grounding techniques and returns the query's response. We evaluate Text2Cohort on 50 natural language inputs, from information extraction to cohort discovery. Our toolkit successfully generated responses with an 88% accuracy and 0.94 F1 score. We demonstrate that Text2Cohort can enable researchers to discover and curate cohorts on IDC with high levels of accuracy using natural language in a more intuitive and user-friendly way.Comment: 5 pages, 3 figures, 2 table

SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations

Segmentation is one of the most primary tasks in deep learning for medical imaging, owing to its multiple downstream clinical applications. However, generating manual annotations for medical images is time-consuming, requires high skill, and is an expensive effort, especially for 3D images. One potential solution is to aggregate knowledge from partially annotated datasets from multiple groups to collaboratively train global models using Federated Learning. To this end, we propose SegViz, a federated learning-based framework to train a segmentation model from distributed non-i.i.d datasets with partial annotations. The performance of SegViz was compared against training individual models separately on each dataset as well as centrally aggregating all the datasets in one place and training a single model. The SegViz framework using FedBN as the aggregation strategy demonstrated excellent performance on the external BTCV set with dice scores of 0.93, 0.83, 0.55, and 0.75 for segmentation of liver, spleen, pancreas, and kidneys, respectively, significantly ($p<0.05$) better (except spleen) than the dice scores of 0.87, 0.83, 0.42, and 0.48 for the baseline models. In contrast, the central aggregation model significantly ($p<0.05$) performed poorly on the test dataset with dice scores of 0.65, 0, 0.55, and 0.68. Our results demonstrate the potential of the SegViz framework to train multi-task models from distributed datasets with partial labels. All our implementations are open-source and available at https://anonymous.4open.science/r/SegViz-B74

Sclerosing Haemangiomas of the Liver: Two Cases of Mistaken Identity

We describe two cases where patients undergoing hepatic resection for metastatic disease of colorectal origin were found to have concomitant sclerosing haemangiomas. The typical radiological and histological appearances of these lesions are discussed

Zero-Shot Object Detection with Textual Descriptions

Object detection is important in real-world applications. Existing methods mainly focus on object detection with sufficient labelled training data or zero-shot object detection with only concept names. In this paper, we address the challenging problem of zero-shot object detection with natural language description, which aims to simultaneously detect and recognize novel concept instances with textual descriptions. We propose a novel deep learning framework to jointly learn visual units, visual-unit attention and word-level attention, which are combined to achieve word-proposal affinity by an element-wise multiplication. To the best of our knowledge, this is the first work on zero-shot object detection with textual descriptions. Since there is no directly related work in the literature, we investigate plausible solutions based on existing zero-shot object detection for a fair comparison. We conduct extensive experiments on three challenging benchmark datasets. The extensive experimental results confirm the superiority of the proposed model

ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging

As the adoption of Artificial Intelligence (AI) systems within the clinical environment grows, limitations in bandwidth and compute can create communication bottlenecks when streaming imaging data, leading to delays in patient care and increased cost. As such, healthcare providers and AI vendors will require greater computational infrastructure, therefore dramatically increasing costs. To that end, we developed ISLE, an intelligent streaming framework for high-throughput, compute- and bandwidth- optimized, and cost effective AI inference for clinical decision making at scale. In our experiments, ISLE on average reduced data transmission by 98.02% and decoding time by 98.09%, while increasing throughput by 2,730%. We show that ISLE results in faster turnaround times, and reduced overall cost of data, transmission, and compute, without negatively impacting clinical decision making using AI systems.Comment: 5 pages, 3 figures, 3 table

Spin-Charge Separation in Two Dimensions - A Numerical Study

The question of spin-charge separation in two-dimensional lattices has been addressed by numerical simulations of the motion of one hole in a half-filled band. The calculations have been performed on finite clusters with Hubbard and t-J models. By comparing the time evolution of spin and charge polarisation currents in one and two dimensions, evidence in favor of spin-charge separation in two dimensions is presented. In contrast with this, spin-charge separation is absent in a highly doped, metallic, system.Comment: RevTeX 3.0, 10 Pages, 6 PostScript Figures (on request

Atypical mycobacterial infection mimicking metastatic cholangiocarcinoma

Mycobacterial infections are rare in developed countries. Isolated involvement of the liver and biliary tree by mycobacterial infection is extremely rare. We report a case of a 45-year-old Caucasian female presenting with obstructive jaundice with a common bile duct stricture and multiple hypodense liver lesions raising suspicion of a metastatic cholangiocarcinoma. Percutaneous core biopsies of the liver lesions however suggested granulomatous process and histology at surgical excision confirmed this finding. Atypical mycobacteria (M. abcessus) sensitive to Amikacin were cultured from the surgical specimen proving the diagnosis. With the resurgence of tubercular and atypical mycobacterial infections in the developed world, it is important not to overlook these in differential diagnosis of various malignancies.Harsh A. Kanhere, Markus I. Trochsler, John Pierides, and Guy J. Madder