Titlebook: Medical Image Understanding and Analysis; 25th Annual Conferen Bart?omiej W. Papie?,Mohammad Yaqub,J. Alison Nobl Conference proceedings 20

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Suhita Karmakar,Dipayan Dewan,Lidia Ghosh,Abir Chowdhury,Amit Konar,Atulya K. Nagars mainly on the maximum rock temperature — to a minor degree on the “cooking time”. It is for this reason that close relationships exist between coalification and palaeogeothermics. The degree of rank increases with depth (the “rank gradient”) commonly reflects the maximum geothermal gradient which

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ally laminated, high resolution sediment sequences, subdivided by local lithozones. Using sediment increase rates, accumulation rates and the structure of varves it was possible to prove rapid climatic variations at the Pleistocene/Holocene transition..The boundaries of local lithozones and their co

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Exploring the Correlation Between Deep Learned and Clinical Features in Melanoma Detectiontionable due to the lack of explainability in their decision-making process. The highly opaque feature learning process of deep models makes it difficult to rationalize their behavior and exploit the potential bottlenecks. Hence it is crucial to verify whether these deep features correlate with the

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Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multi-invasive diagnostic tool for detecting and localising prostate tumours by specialised radiologists. These radiological examinations, for example, for differentiating malignant lesions from benign prostatic hyperplasia in transition zones and for defining the boundaries of clinically significant can

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Improved Artifact Detection in Endoscopy Imaging Through Profile Pruningh as blur, specularity, floating debris and pixel saturation is inevitable. Artifacts affect the quality of diagnosis and treatment as they can obscure features relevant for assessing inflammation and morphological changes that are characteristic to precursors of cancer. In addition, they affect any