Interpretable Diagnosis for Whole-Slide Melanoma Histology Images Using Convolutional Neural Network.

At present, deep learning-based medical image diagnosis had achieved high performance in several diseases. However, the black-box nature of the convolutional neural network (CNN) limits their role in diagnosis. In this study, a novel interpretable diagnosis pipeline using the CNN model was proposed.

Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study.

In traditional hospital systems, diagnosis and localization of melanoma are the critical challenges for pathological analysis, treatment instructions, and prognosis evaluation particularly in skin diseases. In literature, various studies have been reported to address these issues; however, a prominent smart diagnosis system is needed to be developed for the smart healthcare system. In this study, a deep learning-enabled diagnostic system is proposed and implemented that it has the capacity to automatically detect malignant melanoma in whole slide images (WSIs).

Real-time burn depth assessment using artificial networks: a large-scale, multicentre study.

Introduction: Early judgment of the depth of burns is very important for the accurate formulation of treatment plans. In medical imaging the application of Artificial Intelligence has the potential for serving as a very experienced assistant to improve early clinical diagnosis. Due to lack of large volume of a particular feature, there has been almost no progress in burn field.

RXRα ligand Z-10 induces PML-RARα cleavage and APL cell apoptosis through disrupting PML-RARα/RXRα complex in a cAMP-independent manner.

The major oncogenic driver of acute promyelocytic leukemia (APL) is the fusion protein PML-RARα originated from the chromosomal translocation t(15;17). All-trans retinoic acid (ATRA) and arsenic trioxide cure most patients by directly targeting PML-RARα. However, major issues including the resistance of ATRA and arsenic therapy still remain in APL clinical management.