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CAMIL: Channel attention based multiple instance learning for whole slide image classification.
Bioinformatics
January 2025
Geneis Beijing Co., Ltd, Beijing 100102, China.
Motivation: The classification task based on whole-slide images (WSIs) is a classic problem in computational pathology. Multiple Instance Learning (MIL) provides a robust framework for analyzing whole slide images with slide-level labels at gigapixel resolution. However, existing MIL models typically focus on modeling the relationships between instances while neglecting the variability across the channel dimensions of instances, which prevents the model from fully capturing critical information in the channel dimension.
View Article and Find Full Text PDFA Theoretically Informed Process Evaluation in Parallel to a Feasibility Study of a Complex Oral Health Intervention Using NICE Guidelines in a Care Home Setting.
Community Dent Oral Epidemiol
January 2025
Institute of Epidemiology & Health Care, University College London, London, UK.
Background: A theoretically informed process evaluation was undertaken in parallel to a study examining the feasibility of an oral health intervention based on an existing guideline for care homes. The objectives were to explore the factors that influenced the implementation of the intervention in order to understand the potential pathway to impact. The research team initially utilised Pfadenhauer et al.
View Article and Find Full Text PDFExploring the potential of compound-protein complex structure-free models in virtual screening using BlendNet.
Brief Bioinform
November 2024
Department of Computer Science, Yonsei University, Yonsei-ro 50, Seodaemun-gu, 03722, Seoul, Republic of Korea.
Identifying new compounds that interact with a target is a crucial time-limiting step in the initial phases of drug discovery. Compound-protein complex structure-based affinity prediction models can expedite this process; however, their dependence on high-quality three-dimensional (3D) complex structures limits their practical application. Prediction models that do not require 3D complex structures for binding-affinity estimation offer a theoretically attractive alternative; however, accurately predicting affinity without interaction information presents significant challenges.
View Article and Find Full Text PDFComparative analysis and enhancing rainfall prediction models for monthly rainfall prediction in the Eastern Thailand.
MethodsX
June 2025
Department of Royal Rainmaking and Agricultural Aviation, Bangkok 10900, Thailand.
Rainfall prediction is a crucial aspect of climate science, particularly in monsoon-influenced regions where accurate forecasts are essential. This study evaluates rainfall prediction models in the Eastern Thailand by examining an optimal lag time associated with the Oceanic Niño Index (ONI). Five deep learning models-RNN with ReLU, LSTM, GRU (single-layer), LSTM+LSTM, and LSTM+GRU (multi-layer)-were compared using mean absolute error (MAE) and root mean square error (RMSE).
View Article and Find Full Text PDFLongitudinal Psychological Distress After Malignant Brain Tumor Diagnosis: A Multilevel Analysis of Patients and Their Caregivers.
Psychooncology
January 2025
Department of Neurosurgery, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
Objective: Malignant brain tumors are associated with debilitating symptoms and a poor prognosis, resulting in high psychological distress for patients and caregivers. There is a lack of longitudinal studies investigating psychological distress in this group. This study evaluated fear of progression (FoP), anxiety and depression in patients and their caregivers in the 6 months following malignant brain tumor diagnosis.
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