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Noel C. F. Codella
IBM
Machine learningPattern recognitionComputer scienceMedicineSegmentation
79Publications
18H-index
1,476Citations
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Publications 68
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Recent progress on few-shot learning has largely re-lied on annotated data for meta-learning, sampled from the same domain as the novel classes. However, in many applications, collecting data for meta-learning is infeasible or impossible. This leads to the cross-domain few-shot learn-ing problem, where a large domain shift exists between base and novel classes. Although some preliminary investigation of the few-shot methods under domain shift exists, a standard benchmark for cross-domain few-sho...
#1Alex Bratt (Cornell University)H-Index: 1
#2Jiwon Kim (Cornell University)H-Index: 7
Last. Jonathan W. WeinsaftH-Index: 31
view all 15 authors...
Background Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow.
7 CitationsSource
Recent advances in computer vision and deep learning have led to breakthroughs in the development of automated skin image analysis. In particular, skin cancer classification models have achieved performance higher than trained expert dermatologists. However, no attempt has been made to evaluate the consistency in performance of machine learning models across populations with varying skin tones. In this paper, we present an approach to estimate skin tone in benchmark skin disease datasets, and in...
#1Michael A. Marchetti (MSK: Memorial Sloan Kettering Cancer Center)H-Index: 12
#2Konstantinos Liopyris (MSK: Memorial Sloan Kettering Cancer Center)H-Index: 5
Last. Allan C. Halpern (MSK: Memorial Sloan Kettering Cancer Center)H-Index: 59
view all 8 authors...
Abstract Background Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain. Objective To determine if computer algorithms from an international melanoma detection challenge can improve dermatologist melanoma diagnostic accuracy. Methods Cross-sectional study using 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from twenty-three teams. Ei...
Source
#1Philipp Tschandl (Medical University of Vienna)H-Index: 14
#2Noel C. F. Codella (IBM)H-Index: 18
Last. Harald Kittler (Medical University of Vienna)H-Index: 42
view all 28 authors...
Summary Background Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human readers for all clinically relevant types of benign and malignant pigmented skin lesions. Methods For this open, web-based, international, diagnostic study, human readers were asked to diagnose dermatoscopic images selected randomly in 30-ima...
12 CitationsSource
#1M. Emre Celebi (UCA: University of Central Arkansas)H-Index: 34
#2Noel C. F. Codella (IBM)H-Index: 18
Last. Allan C. Halpern (MSK: Memorial Sloan Kettering Cancer Center)H-Index: 59
view all 3 authors...
Dermoscopy is a non-invasive skin imaging technique that permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. While studies on the automated analysis of dermoscopy images date back to the late 1990s, because of various factors (lack of publicly available datasets, open-source software, computational power, etc.), the field progressed rather slowly in its first two decades. With the release of a large public dataset by th...
10 CitationsSource
#1M. Emre Celebi (UCA: University of Central Arkansas)H-Index: 34
#2Noel C. F. Codella (IBM)H-Index: 18
Last. Dou Shen (UNC: University of North Carolina at Chapel Hill)H-Index: 84
view all 4 authors...
The papers in this special section focus on the use of image analysis to detect Melanoma. Melanoma is deadliest form of skin cancer, with roughly 91,000 new cases reported every year in the US and more than 9,000 deaths. Unlike many other cancer types, the incidence rate of melanoma has been steadily increasing in the past several decades. Early diagnosis is crucial since melanoma can be cured with a simple excision, if detected early. The goals of this special issue are to summarize the state-o...
1 CitationsSource
3 CitationsSource
Jan 27, 2019 in AAAI (National Conference on Artificial Intelligence)
#1Michael Hind (IBM)H-Index: 25
#2Dennis Wei (IBM)H-Index: 14
Last. Kush R. Varshney (IBM)H-Index: 18
view all 8 authors...
Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions. However, as many of these systems are opaque in their operation, there is a growing demand for such systems to provide explanations for their decisions. Conventional approaches to this problem attempt to expose or discover the inner workings of a machine learning model with the hope that the resulting explanations will be mean...
3 CitationsSource
#1Noel C. F. CodellaH-Index: 18
#2Michael Hind (IBM)H-Index: 25
Last. Aleksandra MojsilovicH-Index: 24
view all 8 authors...
Using machine learning in high-stakes applications often requires predictions to be accompanied by explanations comprehensible to the domain user, who has ultimate responsibility for decisions and outcomes. Recently, a new framework for providing explanations, called TED, has been proposed to provide meaningful explanations for predictions. This framework augments training data to include explanations elicited from domain users, in addition to features and labels. This approach ensures that expl...
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