Search Results for author:
Found 16 papers, 8 papers with code
Fine-Tuning In-House Large Language Models to Infer Differential Diagnosis from Radiology Reports
This study introduces a pipeline for developing in-house LLMs tailored to identify differential diagnoses from radiology reports.
BURExtract-Llama: An LLM for Clinical Concept Extraction in Breast Ultrasound Reports
We first use GPT-4 to create a small labeled dataset, then fine-tune a Llama3-8B model on it.
Understanding differences in applying DETR to natural and medical images
This study evaluates the applicability of these transformer-based design choices when applied to a screening mammography dataset that represents these distinct medical imaging data characteristics.
Leveraging Transformers to Improve Breast Cancer Classification and Risk Assessment with Multi-modal and Longitudinal Data
Breast cancer screening, primarily conducted through mammography, is often supplemented with ultrasound for women with dense breast tissue.
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning
The framework employs a novel self-paced sampling strategy to ensure the accuracy of pseudo labels.
Minimax Supervised Clustering in the Anisotropic Gaussian Mixture Model: A new take on Robust Interpolation
Our analysis also shows that interpolation can be robust to corruption in the covariance of the noise when the signal is aligned with the "clean" part of the covariance, for the properly defined notion of alignment.
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations
We discover a phenomenon that has been previously reported in the context of classification: the networks tend to first fit the clean pixel-level labels during an "early-learning" phase, before eventually memorizing the false annotations.
Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis
In cancer diagnosis, interpretability can be achieved by localizing the region of the input image responsible for the output, i. e. the location of a lesion.
Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms
Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost.
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
In this work, we extend the globally-aware multiple instance classifier, a framework we proposed to address these unique properties of medical images.
Screening Mammogram Classification with Prior Exams
Radiologists typically compare a patient's most recent breast cancer screening exam to their previous ones in making informed diagnoses.
Globally-Aware Multiple Instance Classifier for Breast Cancer Screening
Moreover, both the global structure and local details play important roles in medical image analysis tasks.
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200, 000 exams (over 1, 000, 000 images).
Breast density classification with deep convolutional neural networks
Breast density classification is an essential part of breast cancer screening.
High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks
In our work, we propose to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images.
