AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth
Super-resolution microscopy, or nanoscopy, enables the use of fluorescent-based molecular
localization tools to study molecular structure at the nanoscale level in the intact cell …
localization tools to study molecular structure at the nanoscale level in the intact cell …
Masked autoencoders are scalable learners of cellular morphology
Inferring biological relationships from cellular phenotypes in high-content microscopy
screens provides significant opportunity and challenge in biological research. Prior results …
screens provides significant opportunity and challenge in biological research. Prior results …
Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations
Motivation Computer-aided analysis of biological images typically requires extensive
training on large-scale annotated datasets, which is not viable in many situations. In this …
training on large-scale annotated datasets, which is not viable in many situations. In this …
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
Featurizing microscopy images for use in biological research remains a significant
challenge especially for large-scale experiments spanning millions of images. This work …
challenge especially for large-scale experiments spanning millions of images. This work …
SAFE-MIL: a statistically interpretable framework for screening potential targeted therapy patients based on risk estimation
Y Guan, Z Xue, J Wang, X Ai, R Chen, X Yi, S Lu… - Frontiers in …, 2024 - frontiersin.org
Patients with the target gene mutation frequently derive significant clinical benefits from
target therapy. However, differences in the abundance level of mutations among patients …
target therapy. However, differences in the abundance level of mutations among patients …
Affective states classification performance of audio-visual stimuli from EEG signals with multiple-instance learning
Throughout various disciplines, emotion recognition continues to be an essential subject of
study. With the advancement of machine learning methods, accurate emotion recognition …
study. With the advancement of machine learning methods, accurate emotion recognition …
Deep learning for drug discovery: a study of identifying high efficacy drug compounds using a cascade transfer learning approach
D Zhuang, AK Ibrahim - Applied Sciences, 2021 - mdpi.com
In this research, we applied deep learning to rank the effectiveness of candidate drug
compounds in combating viral cells, in particular, SARS-Cov-2 viral cells. For this purpose …
compounds in combating viral cells, in particular, SARS-Cov-2 viral cells. For this purpose …
[PDF][PDF] Dual Space Multiple Instance Representative Learning for Medical Image Classification.
Medical image classification plays a vital role in AI-aided medical diagnosis and is often
addressed as a Multiple Instance Learning (MIL) issue (ie, each sample is a bag of …
addressed as a Multiple Instance Learning (MIL) issue (ie, each sample is a bag of …
A multi instance learning approach for critical view of safety detection in laparoscopic cholecystectomy
Y Colbeci, M Zohar, D Neimark… - Machine Learning …, 2022 - proceedings.mlr.press
Surgical procedures have a clear designated goal, which makes the art of performing
surgery a task-oriented action. The performing surgeon follows specific workflow steps that …
surgery a task-oriented action. The performing surgeon follows specific workflow steps that …
Weakly-Supervised Drug Efficiency Estimation with Confidence Score: Application to COVID-19 Drug Discovery
The COVID-19 pandemic has prompted a surge in drug repurposing studies. However,
many promising hits identified by modern neural networks failed in the preclinical research …
many promising hits identified by modern neural networks failed in the preclinical research …