292 research outputs found
Clinical and Dermoscopic Features of Melanocytic Lesions on the Face Versus the External Ear
Introduction
Melanoma of the external ear is a rare condition accounting for 7-20% of all melanomas of the head and neck region. They present classical features of extra-facial melanomas clinically and dermoscopically. In contrast, facial melanomas show peculiar patterns in dermoscopy.
Objectives
To evaluate whether there are clinical and/or dermoscopic differences in melanocytic lesions located either at the external ear or on the face.
Methods
In this retrospective study we reviewed an image database for clinical and dermoscopic images of melanomas and nevi located either on the face or at the level of the external ear.
Results
65 patients (37 men; 63.8%) with 65 lesions were included. We found no significant differences in comparing face melanomas with melanomas at the level of the external ear, neither clinically nor dermoscopically. However, we provided evidence for differences in some clinical and dermoscopic features of melanomas and nevi of the external ear.
Conclusions
In this study, we reported no significant differences in comparing melanomas on the face with melanomas of the external ear, both clinically and dermoscopically. Furthermore, we provided data on clinical and dermoscopic differences comparing nevi and melanoma of the external ear
Visualizing convolutional neural networks to improve decision support for skin lesion classification
Because of their state-of-the-art performance in computer vision, CNNs are
becoming increasingly popular in a variety of fields, including medicine.
However, as neural networks are black box function approximators, it is
difficult, if not impossible, for a medical expert to reason about their
output. This could potentially result in the expert distrusting the network
when he or she does not agree with its output. In such a case, explaining why
the CNN makes a certain decision becomes valuable information. In this paper,
we try to open the black box of the CNN by inspecting and visualizing the
learned feature maps, in the field of dermatology. We show that, to some
extent, CNNs focus on features similar to those used by dermatologists to make
a diagnosis. However, more research is required for fully explaining their
output.Comment: 8 pages, 6 figures, Workshop on Interpretability of Machine
Intelligence in Medical Image Computing at MICCAI 201
Study protocol for a prospective, non-controlled, multicentre clinical study to evaluate the diagnostic accuracy of a stepwise two-photon excited melanin fluorescence in pigmented lesions suspicious for melanoma (FLIMMA study)
Introduction: Non-invasive, nanosecond, stepwise two-photon laser excitation
of skin tissue was shown to induce melanin fluorescence spectra that allow for
the differentiation of melanocytic nevi from cutaneous melanoma. Methods and
analysis: This prospective, non-controlled, multicentre clinical study is
performed to evaluate the diagnostic performance of the stepwise two-photon
excited melanin fluorescence in the detection of cutaneous melanoma. The
comparator will be the histopathological diagnosis. A total of 620 pigmented
skin lesions suspicious for melanoma and intended for excision will be
enrolled. Ethics and dissemination: Ethics approval was provided by the local
ethics committees of the medical faculties of the University of Tuebingen,
Heidelberg and Berlin. Study registration: The FLIMMA study NCT02425475
Metabolic Signature of Atypical Fibroxanthoma and Pleomorphic Dermal Sarcoma: Expression of Hypoxia-inducible Factor-1α and Several of Its Downstream Targets
Metabolic reprogramming mediated by hypoxia-inducible factors play a crucial role in many human cancers. HIF-1α is activated under hypoxic conditions and is considered a key regulator of oxygen homoeostasis during tumor proliferation under hypoxia. Aim of this research was to analyze the immunohistochemical expression of HIF-1α, VEGF-A, Glut-1, MCT4, and CAIX in atypical fibroxanthoma (AFX) and pleomorphic dermal sarcoma (PDS). 21 paraffin-embedded AFX and 22 PDS were analysed by immunohistochemis-try, namely HIF-1α, VEGF-A (referred to as VEGF throughout the manuscript), Glut-1, MCT4, and CAIX. To quantify the protein expression, we considered the percentage of positive tumor cells (0: 0%, 1: up to 1%, 2: 2-10%, 3: 11-50%, 4: >50%) in relation to the staining intensity (0: negative, 1: low, 2: medium, 3: strong). HIF-1α expression (mean ± SD) in AFX (9.33±2.92) was significantly stronger than that in PDS (5.90±4.38; P= 0.007), whereas the expression of VEGF, Glut-1, MCT4, and CAIX did not show differences between AFX and PDS. When comparing all tumors without subgroup stratification, the expression of HIF-1α (P= 0.044) and MCT4 (P= 0.036) was significantly stronger in ulcerated tumors than in tumors without ulceration. Our findings provide the first evidence that HIF-1α-induced metabolic reprogramming may contribute to the pathogenesis of AFX and PDS. HIF-1α expression seems to be higher in AFX than in PDS, and ulcerated tumors show higher expression levels of HIF-1α and MCT4 irrespective of the diagnosis
A Midsagittal-View Magnetic Resonance Imaging Study of the Growth and Involution of the Adenoid Mass and Related Changes in Selected Velopharyngeal Structures
Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions
A high proportion of suspicious pigmented skin lesions referred for investigation are benign. Techniques to improve the accuracy of melanoma diagnoses throughout the patient pathway are needed to reduce the pressure on secondary care and pathology services
Deep neural network or dermatologist?
Deep learning techniques have proven high accuracy for identifying melanoma
in digitised dermoscopic images. A strength is that these methods are not
constrained by features that are pre-defined by human semantics. A down-side is
that it is difficult to understand the rationale of the model predictions and
to identify potential failure modes. This is a major barrier to adoption of
deep learning in clinical practice. In this paper we ask if two existing local
interpretability methods, Grad-CAM and Kernel SHAP, can shed light on
convolutional neural networks trained in the context of melanoma detection. Our
contributions are (i) we first explore the domain space via a reproducible,
end-to-end learning framework that creates a suite of 30 models, all trained on
a publicly available data set (HAM10000), (ii) we next explore the reliability
of GradCAM and Kernel SHAP in this context via some basic sanity check
experiments (iii) finally, we investigate a random selection of models from our
suite using GradCAM and Kernel SHAP. We show that despite high accuracy, the
models will occasionally assign importance to features that are not relevant to
the diagnostic task. We also show that models of similar accuracy will produce
different explanations as measured by these methods. This work represents first
steps in bridging the gap between model accuracy and interpretability in the
domain of skin cancer classification
Inflammoskopie: Dermatoskopie bei entz\ufcndlichen, infiltrierenden und infekti\uf6sen Dermatosen : Indikation und standardisierte dermatoskopische Terminologie
Dermatoscopy as a\ua0noninvasive diagnostic tool is not only useful in the differentiation of malignant and benign skin tumors, but is also effective in the diagnosis of inflammatory, infiltrative and infectious dermatoses. As a result, the need for diagnostic punch biopsies in dermatoses could be reduced. Hereby the selection of affected skin areas is essential. The diagnostic accuracy is independent of the skin type. Helpful dermatoscopic features include vessels morphology and distribution, scales colors and distribution, follicular findings, further structures such as colors and morphology as well as specific clues. The dermatoscopic diagnosis is made based on the descriptive approach in clinical routine, teaching and research. In all clinical and dermatoscopic diagnoses that remain unclear, a\ua0punch biopsy with histopathology should be performed. The dermatoscope should be cleaned after every examination according to the guidelines
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