1,882 research outputs found
DermX: an end-to-end framework for explainable automated dermatological diagnosis
Dermatological diagnosis automation is essential in addressing the high
prevalence of skin diseases and critical shortage of dermatologists. Despite
approaching expert-level diagnosis performance, convolutional neural network
(ConvNet) adoption in clinical practice is impeded by their limited
explainability, and by subjective, expensive explainability validations. We
introduce DermX and DermX+, an end-to-end framework for explainable automated
dermatological diagnosis. DermX is a clinically-inspired explainable
dermatological diagnosis ConvNet, trained using DermXDB, a 554 image dataset
annotated by eight dermatologists with diagnoses, supporting explanations, and
explanation attention maps. DermX+ extends DermX with guided attention training
for explanation attention maps. Both methods achieve near-expert diagnosis
performance, with DermX, DermX+, and dermatologist F1 scores of 0.79, 0.79, and
0.87, respectively. We assess the explanation performance in terms of
identification and localization by comparing model-selected with
dermatologist-selected explanations, and gradient-weighted class-activation
maps with dermatologist explanation maps, respectively. DermX obtained an
identification F1 score of 0.77, while DermX+ obtained 0.79. The localization
F1 score is 0.39 for DermX and 0.35 for DermX+. These results show that
explainability does not necessarily come at the expense of predictive power, as
our high-performance models provide expert-inspired explanations for their
diagnoses without lowering their diagnosis performance
Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores
Multiple Instance Learning: A Survey of Problem Characteristics and Applications
Multiple instance learning (MIL) is a form of weakly supervised learning
where training instances are arranged in sets, called bags, and a label is
provided for the entire bag. This formulation is gaining interest because it
naturally fits various problems and allows to leverage weakly labeled data.
Consequently, it has been used in diverse application fields such as computer
vision and document classification. However, learning from bags raises
important challenges that are unique to MIL. This paper provides a
comprehensive survey of the characteristics which define and differentiate the
types of MIL problems. Until now, these problem characteristics have not been
formally identified and described. As a result, the variations in performance
of MIL algorithms from one data set to another are difficult to explain. In
this paper, MIL problem characteristics are grouped into four broad categories:
the composition of the bags, the types of data distribution, the ambiguity of
instance labels, and the task to be performed. Methods specialized to address
each category are reviewed. Then, the extent to which these characteristics
manifest themselves in key MIL application areas are described. Finally,
experiments are conducted to compare the performance of 16 state-of-the-art MIL
methods on selected problem characteristics. This paper provides insight on how
the problem characteristics affect MIL algorithms, recommendations for future
benchmarking and promising avenues for research
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