309,373 research outputs found
Deep Learning Body Region Classification of MRI and CT examinations
Standardized body region labelling of individual images provides data that
can improve human and computer use of medical images. A CNN-based classifier
was developed to identify body regions in CT and MRI. 17 CT (18 MRI) body
regions covering the entire human body were defined for the classification
task. Three retrospective databases were built for the AI model training,
validation, and testing, with a balanced distribution of studies per body
region. The test databases originated from a different healthcare network.
Accuracy, recall and precision of the classifier was evaluated for patient age,
patient gender, institution, scanner manufacturer, contrast, slice thickness,
MRI sequence, and CT kernel. The data included a retrospective cohort of 2,934
anonymized CT cases (training: 1,804 studies, validation: 602 studies, test:
528 studies) and 3,185 anonymized MRI cases (training: 1,911 studies,
validation: 636 studies, test: 638 studies). 27 institutions from primary care
hospitals, community hospitals and imaging centers contributed to the test
datasets. The data included cases of all genders in equal proportions and
subjects aged from a few months old to +90 years old. An image-level prediction
accuracy of 91.9% (90.2 - 92.1) for CT, and 94.2% (92.0 - 95.6) for MRI was
achieved. The classification results were robust across all body regions and
confounding factors. Due to limited data, performance results for subjects
under 10 years-old could not be reliably evaluated. We show that deep learning
models can classify CT and MRI images by body region including lower and upper
extremities with high accuracy.Comment: 21 pages, 2 figures, 4 table
Classification of Humans into Ayurvedic Prakruti Types using Computer Vision
Ayurveda, a 5000 years old Indian medical science, believes that the universe and hence humans are made up of five elements namely ether, fire, water, earth, and air. The three Doshas (Tridosha) Vata, Pitta, and Kapha originated from the combinations of these elements. Every person has a unique combination of Tridosha elements contributing to a person’s ‘Prakruti’. Prakruti governs the physiological and psychological tendencies in all living beings as well as the way they interact with the environment. This balance influences their physiological features like the texture and colour of skin, hair, eyes, length of fingers, the shape of the palm, body frame, strength of digestion and many more as well as the psychological features like their nature (introverted, extroverted, calm, excitable, intense, laidback), and their reaction to stress and diseases. All these features are coded in the constituents at the time of a person’s creation and do not change throughout their lifetime. Ayurvedic doctors analyze the Prakruti of a person either by assessing the physical features manually and/or by examining the nature of their heartbeat (pulse). Based on this analysis, they diagnose, prevent and cure the disease in patients by prescribing precision medicine.
This project focuses on identifying Prakruti of a person by analysing his facial features like hair, eyes, nose, lips and skin colour using facial recognition techniques in computer vision. This is the first of its kind research in this problem area that attempts to bring image processing into the domain of Ayurveda
Fashion Conversation Data on Instagram
The fashion industry is establishing its presence on a number of
visual-centric social media like Instagram. This creates an interesting clash
as fashion brands that have traditionally practiced highly creative and
editorialized image marketing now have to engage with people on the platform
that epitomizes impromptu, realtime conversation. What kinds of fashion images
do brands and individuals share and what are the types of visual features that
attract likes and comments? In this research, we take both quantitative and
qualitative approaches to answer these questions. We analyze visual features of
fashion posts first via manual tagging and then via training on convolutional
neural networks. The classified images were examined across four types of
fashion brands: mega couture, small couture, designers, and high street. We
find that while product-only images make up the majority of fashion
conversation in terms of volume, body snaps and face images that portray
fashion items more naturally tend to receive a larger number of likes and
comments by the audience. Our findings bring insights into building an
automated tool for classifying or generating influential fashion information.
We make our novel dataset of {24,752} labeled images on fashion conversations,
containing visual and textual cues, available for the research community.Comment: 10 pages, 6 figures, This paper will be presented at ICWSM'1
Person Recognition in Personal Photo Collections
Recognising persons in everyday photos presents major challenges (occluded
faces, different clothing, locations, etc.) for machine vision. We propose a
convnet based person recognition system on which we provide an in-depth
analysis of informativeness of different body cues, impact of training data,
and the common failure modes of the system. In addition, we discuss the
limitations of existing benchmarks and propose more challenging ones. Our
method is simple and is built on open source and open data, yet it improves the
state of the art results on a large dataset of social media photos (PIPA).Comment: Accepted to ICCV 2015, revise
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