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The prevalence of skin melanoma is rapidly increasing as well as the recorded death cases of its patients. Automatic image segmentation tools play an important role in providing standardized computer-assisted analysis for skin melanoma patients. Current state-of-the-art segmentation methods are based on fully convolutional neural networks, which utilize an encoder-decoder approach. However, these methods produce coarse segmentation masks due to the loss of location information during the encoding layers. Inspired by Pyramid Scene Parsing Network (PSP-Net), we propose an encoderdecoder model that utilizes pyramid pooling modules in the deep skip connections which aggregate the global context and compensate for the lost spatial information. We trained and validated our approach using ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection grand challenge dataset. Our approach showed a validation accuracy with a Jaccard index of 0.837, which outperforms U-Net. We believe that with this reported reliable accuracy, this method can be introduced for clinical practice.

To automate the process of segmenting an anatomy of interest, we can learn a model from previously annotated data. The learningbased approach uses annotations to train a model that tries to emulate the expert labeling on a new data set. While tremendous progress has been made using such approaches, labeling of medical images remains a time-consuming and expensive task. In this paper, we evaluate the utility of extreme points in learning to segment. Specifically, we propose a novel approach to compute a confidence map from extreme points that quantitatively encodes the priors derived from extreme points. We use the confidence map as a cue to train a deep neural network based on ResNet-101 and PSP module to develop a class-agnostic segmentation model that outperforms state-of-the-art method that employs extreme points as a cue. Further, we evaluate a realistic use-case by using our model to generate training data for supervised learning (U-Net) and observed that U-Net performs comparably when trained with either the generated data or the ground truth data. These findings suggest that models trained using cues can be used to generate reliable training data. = equal contribution

Imaging biomarkers derived from medical images play an important role in diagnosis, prognosis, and therapy response assessment. Developing prognostic imaging biomarkers which can achieve reliable survival prediction is essential for prognostication across various diseases and imaging modalities. In this work, we propose a method for discovering patch-level imaging patterns which we then use to predict mortality risk and identify prognostic biomarkers. Specifically, a contrastive learning model is first trained on patches to learn patch representations, followed by a clustering method to group similar underlying imaging patterns. The entire medical image can be thus represented by a long sequence of patch representations and their cluster assignments. Then a memory-efficient clustering Vision Transformer is proposed to aggregate all the patches to predict mortality risk of patients and identify highrisk patterns. To demonstrate the effectiveness and generalizability of our model, we test the survival prediction performance of our method on two sets of patients with idiopathic pulmonary fibrosis (IPF), a chronic, progressive, and life-threatening interstitial pneumonia of unknown etiology. Moreover, by comparing the high-risk imaging patterns extracted by our model with existing imaging patterns utilised in clinical practice, we can identify a novel biomarker that may help clinicians improve risk stratification of IPF patients.

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