Medical Image Retrieval

7 papers with code • 1 benchmarks • 3 datasets

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Most implemented papers

BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

MIMBCD-UI/prototype-multi-modality 7 Apr 2020

This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.

Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation

hungyiwu/mixed-distance 19 May 2015

This paper proposes to generate and to use barcodes to annotate medical images and/or their regions of interest such as organs, tumors and tissue types.

Medical Image Retrieval using Deep Convolutional Neural Network

himanshunaidu/cnn_adnan_fruit 24 Mar 2017

The learned features and the classification results are used to retrieve medical images.

Deep Triplet Hashing Network for Case-based Medical Image Retrieval

fjssharpsword/ATH 29 Jan 2021

The triplet cross-entropy loss can help to map the classification information of images and similarity between images into the hash codes.

CNN Based Autoencoder Application in Breast Cancer Image Retrieval

forderation/breast-cancer-retrieval International Seminar on Intelligent Technology and Its Applications (ISITIA) 2021

Overall, the results of image retrieval in breast cancer applying the CNN based Autoencoder method achieved higher performance compared to the method used in the previous study with an average precision of 0. 9237 in the mainclass dataset category and 0. 6825 in the subclass dataset category.

TorchXRayVision: A library of chest X-ray datasets and models

mlmed/torchxrayvision 31 Oct 2021

TorchXRayVision is an open source software library for working with chest X-ray datasets and deep learning models.

Medical Image Retrieval via Nearest Neighbor Search on Pre-trained Image Features

deepaknlp/dls 5 Oct 2022

We extensively tested the proposed NNS approach and compared the performance with state-of-the-art NNS approaches on benchmark datasets and our created medical image datasets.