AIMC Topic: Mycosis Fungoides

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Deep learning as a new tool in the diagnosis of mycosis fungoides.

Archives of dermatological research
Mycosis Fungoides (MF) makes up the most of the cutaneous lymphomas. As a malignant disease, the greatest diagnostical challenge is to timely differentiate MF from inflammatory diseases. Contemporary computational methods successfully identify cell n...

[Coexistence of mycosis fungoides and essential thrombocythemia with JAK2V617F].

Revista medica del Instituto Mexicano del Seguro Social
BACKGROUND: The coexistence of myeloproliferative neoplasms (MPNs), specifically essential thrombocythemia and lymphoproliferative neoplasms, are a very rare finding with a frequency < 1%.

Early diagnosis model of mycosis fungoides and five inflammatory skin diseases based on a multimodal data-based convolutional neural network.

The British journal of dermatology
BACKGROUND: Mycosis fungoides (MF) is the most common type of cutaneous T-cell lymphoma, and early-stage MF is difficult to differentiate from erythematous inflammatory disease. With the exception of biopsy, noninvasive information such as a patient'...

Deep Learning-Based Classification of Early-Stage Mycosis Fungoides and Benign Inflammatory Dermatoses on H&E-Stained Whole-Slide Images: A Retrospective, Proof-of-Concept Study.

The Journal of investigative dermatology
The diagnosis of early-stage mycosis fungoides (MF) is challenging owing to shared clinical and histopathological features with benign inflammatory dermatoses. Recent evidence has shown that deep learning (DL) can assist pathologists in cancer classi...