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Found 12 papers, 2 papers with code
Emergent Language Symbolic Autoencoder (ELSA) with Weak Supervision to Model Hierarchical Brain Networks
Brain networks display a hierarchical organization, a complexity that poses a challenge for existing deep learning models, often structured as flat classifiers, leading to difficulties in interpretability and the 'black box' issue.
Foundation Models for Biomedical Image Segmentation: A Survey
This transformative technology, originally developed for general-purpose computer vision, has found rapid application in medical image processing.
3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in Radiology
The increasing use of medical imaging in healthcare settings presents a significant challenge due to the increasing workload for radiologists, yet it also offers opportunity for enhancing healthcare outcomes if effectively leveraged.
Region-based Contrastive Pretraining for Medical Image Retrieval with Anatomic Query
We introduce a novel Region-based contrastive pretraining for Medical Image Retrieval (RegionMIR) that demonstrates the feasibility of medical image retrieval with similar anatomical regions.
Deep Labeling of fMRI Brain Networks
We prove the generalizability of our method by showing that the MLP performs at 100% accuracy in the holdout dataset and 98. 3% accuracy in three other sites' fMRI acquisitions.
Deep Labeling of fMRI Brain Networks Using Cloud Based Processing
We propose an end-to-end reproducible pipeline which incorporates image processing of rs-fMRI in a cloud-based workflow while using deep learning to automate the classification of RSNs.
Adversarial Attacks with Time-Scale Representations
Third, we generate new adversarial images by projecting back the original coefficients from the low scale and the perturbed coefficients from the high scale sub-space.
Symbolic Semantic Segmentation and Interpretation of COVID-19 Lung Infections in Chest CT volumes based on Emergent Languages
We show state of the art results for segmentation of COVID-19 lung infections in CT.
Emergent symbolic language based deep medical image classification
In this work, we demonstrate for the first time, the emer-gence of deep symbolic representations of emergent language in the frame-work of image classification.
Automated Phenotyping via Cell Auto Training (CAT) on the Cell DIVE Platform
We present a method for automatic cell classification in tissue samples using an automated training set from multiplexed immunofluorescence images.
Towards Emergent Language Symbolic Semantic Segmentation and Model Interpretability
A UNet-like architecture is used to generate input to the Sender network which produces a symbolic sentence, and a Receiver network co-generates the segmentation mask based on the sentence.
ESCELL: Emergent Symbolic Cellular Language
In one form of the game, a sender and a receiver observe a set of cells from 5 different cell phenotypes.
