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Found 19 papers, 5 papers with code
CytoFM: The first cytology foundation model
Cytology is essential for cancer diagnostics and screening due to its minimally invasive nature.
Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data
Foundation models in digital pathology use massive datasets to learn useful compact feature representations of complex histology images.
Zero-shot Medical Event Prediction Using a Generative Pre-trained Transformer on Electronic Health Records
This study presents the first comprehensive analysis of zero-shot forecasting with GPT-based foundational models in EHRs, introducing a novel pipeline that formulates medical concept prediction as a generative modeling task.
Evaluation Of P300 Speller Performance Using Large Language Models Along With Cross-Subject Training
A recurring theme in P300-based research is enhancing performance to enable faster subject interaction.
Reducing Overtreatment of Indeterminate Thyroid Nodules Using a Multimodal Deep Learning Model
Objective: Molecular testing (MT) classifies cytologically indeterminate thyroid nodules as benign or malignant with high sensitivity but low positive predictive value (PPV), only using molecular profiles, ignoring ultrasound (US) imaging and biopsy.
Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning
To handle volumetric patches, we used a modified video transformer with a deep feature extractor pretrained using self-supervised learning.
High Performance P300 Spellers Using GPT2 Word Prediction With Cross-Subject Training
Amyotrophic lateral sclerosis (ALS) severely impairs patients' ability to communicate, often leading to a decline in their quality of life within a few years of diagnosis.
Ultrasound Image Enhancement using CycleGAN and Perceptual Loss
These images are compared with paired images acquired from high resolution devices to demonstrate the model's ability to generate realistic high-quality images across organ systems.
Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning Models
This is a promising result that supports future applications of deep learning on CT and CTA for the identification of eligible AIS patients for MTB.
Design considerations for a hierarchical semantic compositional framework for medical natural language understanding
Medical natural language processing (NLP) systems are a key enabling technology for transforming Big Data from clinical report repositories to information used to support disease models and validate intervention methods.
A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning
Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra- observer agreement.
Intra-Domain Task-Adaptive Transfer Learning to Determine Acute Ischemic Stroke Onset Time
We apply this approach to both 2D and 3D CNN architectures with our top model achieving an ROC-AUC value of 0. 74, with a sensitivity of 0. 70 and a specificity of 0. 81 for classifying TSS < 4. 5 hours.
Bidirectional Representation Learning from Transformers using Multimodal Electronic Health Record Data to Predict Depression
We applied the current trend of pretraining and fine-tuning on EHR data to outperform the current state-of-the-art in chronic disease prediction, and to demonstrate the underlying relation between EHR codes in the sequence.
Attention-Guided Discriminative Region Localization and Label Distribution Learning for Bone Age Assessment
However, training with the global image underutilizes discriminative local information, while providing extra annotations is expensive and subjective.
Semi-supervised Learning using Adversarial Training with Good and Bad Samples
In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training.
Translating neural signals to text using a Brain-Computer Interface
To this end, we aim to create a BCI that decodes text directly from neural signals.
Translating neural signals to text using a Brain-Machine Interface
Brain-Computer Interfaces (BCI) help patients with faltering communication abilities due to neurodegenerative diseases produce text or speech output by direct neural processing.
An attention-based multi-resolution model for prostate whole slide imageclassification and localization
The model achieved state-of-the-art performance for prostate cancer grading with an accuracy of 85. 11\% for classifying benign, low-grade (Gleason grade 3+3 or 3+4), and high-grade (Gleason grade 4+3 or higher) slides on an independent test set.
Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach
Recently, semi-supervised learning methods based on generative adversarial networks (GANs) have received much attention.
