Framework of MsHop. by Jia Li (160557)

“…This paper introduces a novel classification algorithm, ASGBC, intended to tackle related challenges in diagnosing gallbladder cancer using B-ultrasound images. Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”

Results of ablation study. by Jia Li (160557)

“…This paper introduces a novel classification algorithm, ASGBC, intended to tackle related challenges in diagnosing gallbladder cancer using B-ultrasound images. Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”

Kappa consistency ranges. by Jia Li (160557)

“…This paper introduces a novel classification algorithm, ASGBC, intended to tackle related challenges in diagnosing gallbladder cancer using B-ultrasound images. Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”

Performance metrics under different noise levels. by Jia Li (160557)

“…This paper introduces a novel classification algorithm, ASGBC, intended to tackle related challenges in diagnosing gallbladder cancer using B-ultrasound images. Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”

Results of ablation study. by Jia Li (160557)

“…This paper introduces a novel classification algorithm, ASGBC, intended to tackle related challenges in diagnosing gallbladder cancer using B-ultrasound images. Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”

The calcitron model and calcium-based plasticity rules. by Toviah Moldwin (10866708)

“…<b>(H)</b> Fixed points (black) and learning rates (pink) in the asymptotic fixed point – learning rate (FPLR) version of the calcium control hypothesis. …”

Connectivity changes that underlie the main effects of hand and task epoch. by Ali Rezaei (4238548)

“…Positive (red) and negative (blue) values show increases and decreases in connectivity, respectively, for Baseline to Early learning/transfer (leftmost panel) and for Early to Late learning/transfer (adjacent right panel). …”

Reward-based motor task and subject performance. by Corson N. Areshenkoff (20378635)

“…(<b>E</b>) Relationship between subjects’ reaction time (RT, left) and movement time (MT, right) as a function of learning period. …”

Image_1_Loneliness, online learning and student outcomes in college students living with disabilities: results from the National College Health Assessment Spring 2022.pdf by William Bevens (9319157)

Table 1_Abnormal subthalamic nucleus functional connectivity and machine learning classification in Parkinson’s disease: a multisite functional magnetic resonance imaging study.doc... by Bin Qin (544949)

“…This multisite study analyzed pooled resting-state functional magnetic resonance imaging (rs-fMRI) data to characterize subthalamic nucleus (STN) functional connectivity (FC) abnormalities and to evaluate their utility in machine learning classification of PD.</p>Methods<p>We analyzed rs-fMRI data from 232 participants (158 PD patients and 74 healthy controls [HCs]) across four repositories: Parkinson’s Progression Markers Initiative (PPMI), OpenfMRI, and FCP/INDI (NEUROCON dataset and Tao Wu dataset). …”

Data Sheet 1_Motor learning leverages coordinated low-frequency cortico-basal ganglia activity to optimize motor preparation in humans with Parkinson’s disease.pdf by Kara N. Presbrey (21347171)

“…However, it is unclear which brain regions mediate preplanning or how this process evolves with learning. Recording cortico-basal ganglia field potentials during a multi-day typing task in four individuals with PD, we found evidence for network-wide multi-element preplanning that improved with learning, facilitated by functional connectivity. …”

Table_1_Construction of a risk screening and visualization system for pulmonary nodule in physical examination population based on feature self-recognition machine learning model.X... by Fang Tian (360682)

“…Among them, 1,168 had positive CT reports for pulmonary nodules, while 3,693 had negative findings. We developed a machine learning model using the XGBoost algorithm and employed an improved sooty tern optimization algorithm (ISTOA) for feature selection. …”

Presentation 1_Differences in firing patterns along the dorsal-intermediate hippocampal axis in a fixed route during a change in emotional context.pptx by Ryan Troha (22597001)

Table 1_Integrated transcriptomic analysis of COVID-19 stages and recovery: insights into key gene signatures, immune features, and diagnostic biomarkers through machine learning.x... by Zhiyuan Gong (51844)

“…Adaptive immune cells (e.g., B cells and T cells) decreased, while innate immune cells (e.g., monocytes and neutrophils) increased, particularly in ICU patients. …”

Exploring the relationship between graft dysfunction with serum metabolites and inflammatory proteins: integrating Mendelian randomization, single-cell analysis, machine learning,... by Jiyuan Li (4784196)

“…Second, we further intergrated the single-cell analysis, machine learning, and Shapley Additive exPlanations (SHAP) methods to validate the role of the inflammatory protein in rejected transplanted kidneys.…”

Branching behavior of a N = 10 units network at Trial T (case of strong inhibition = 0.60) by Elif Köksal-Ersöz (20378631)

Data_Sheet_1_Loneliness, online learning and student outcomes in college students living with disabilities: results from the National College Health Assessment Spring 2022.docx by William Bevens (9319157)

VMR task and subject behavior. by Corson N. Areshenkoff (20378635)

“…Using these report trials, we derived estimates of subjects’ total explicit and implicit learning during the task. …”

The whole leaf image segmented using SAM. by Afira Aslam (21433666)

“…The crop is susceptible to various diseases, leading to decreased yields. In recent years, advancements in deep learning methods have enabled researchers to develop automated methods for detecting diseases in cotton crops. …”

Cotton production in major countries. by Afira Aslam (21433666)

“…The crop is susceptible to various diseases, leading to decreased yields. In recent years, advancements in deep learning methods have enabled researchers to develop automated methods for detecting diseases in cotton crops. …”