Salient Object Detection via Augmented Hypotheses

In this paper, we propose using \textit{augmented hypotheses} which consider objectness, foreground and compactness for salient object detection. Our algorithm consists of four basic steps. First, our method generates the objectness map via objectness hypotheses. Based on the objectness map, we estimate the foreground margin and compute the corresponding foreground map which prefers the foreground objects. From the objectness map and the foreground map, the compactness map is formed to favor the compact objects. We then derive a saliency measure that produces a pixel-accurate saliency map which uniformly covers the objects of interest and consistently separates fore- and background. We finally evaluate the proposed framework on two challenging datasets, MSRA-1000 and iCoSeg. Our extensive experimental results show that our method outperforms state-of-the-art approaches.Comment: IJCAI 2015 pape

Superpixel-based spatial amplitude and phase modulation using a digital micromirror device

We present a superpixel method for full spatial phase and amplitude control of a light beam using a digital micromirror device (DMD) combined with a spatial filter. We combine square regions of nearby micromirrors into superpixels by low pass filtering in a Fourier plane of the DMD. At each superpixel we are able to independently modulate the phase and the amplitude of light, while retaining a high resolution and the very high speed of a DMD. The method achieves a measured fidelity $F=0.98$ for a target field with fully independent phase and amplitude at a resolution of $8\times 8$ pixels per diffraction limited spot. For the LG$_{10}$ orbital angular momentum mode the calculated fidelity is $F=0.99993$, using $768\times 768$ DMD pixels. The superpixel method reduces the errors when compared to the state of the art Lee holography method for these test fields by $50\%$ and $18\%$, with a comparable light efficiency of around $5\%$. Our control software is publicly available.Comment: 9 pages, 6 figure

ANALYZING PULMONARY ABNORMALITY WITH SUPERPIXEL BASED GRAPH NEURAL NETWORKS IN CHEST X-RAY

In recent years, the utilization of graph-based deep learning has gained prominence, yet its potential in the realm of medical diagnosis remains relatively unexplored. Convolutional Neural Network (CNN) has achieved state-of-the-art performance in areas such as computer vision, particularly for grid-like data such as images. However, they require a huge dataset to achieve top level of performance and challenge arises when learning from the inherent irregular/unordered nature of physiological data. In this thesis, the research primarily focuses on abnormality screening: classification of Chest X-Ray (CXR) as Tuberculosis positive or negative, using Graph Neural Networks (GNN) that uses Region Adjacency Graphs (RAGs), and each superpixel serves as a dedicated graph node. For graph classification, provided that the different classes are distinct enough GNN often classify graphs using just the graph structures. This study delves into the inquiry of whether the incorporation of node features, such as coordinate points and pixel intensity, along with structured data representing graph can enhance the learning process. By integration of residual and concatenation structures, this methodology adeptly captures essential features and relationships among superpixels, thereby contributing to advancements in tuberculosis identification. We achieved the best performance: accuracy of 0.80 and AUC of 0.79, through the union of state-of-the-art neural network architectures and innovative graph-based representations. This work introduces a new perspective to medical image analysis

Weakly supervised segmentation of polyps on colonoscopy images

openIl cancro del colon-retto (CRC) è una delle principali cause di morte a livello mondiale e continua a rappresentare una sfida critica per la salute pubblica, richiedendo una precisa e tempestiva diagnosi e un intervento mirato. La colonscopia, ovvero l'esame diagnostico volto a esplorare le pareti interne del colon per scoprire eventuali masse tumorali, ha dimostrato essere un metodo efficace per ridurre l'incidenza di mortalità. Le tecniche emergenti, come l'analisi avanzata delle immagini tramite reti neurali, sono promettenti per una diagnosi accurata. Tuttavia, alcuni studi hanno riportato che, per varie ragioni, una certa percentuale di polipi non viene rilevata correttamente durante la colonscopia. Una delle più importanti è la dipendenza dalle annotazioni a livello di pixel, che richiede molte risorse computazionali; per questo si rendono necessarie soluzioni innovative. Questa tesi introduce alcune strategie per migliorare l'identificazione dei polipi. A tal fine, le tecniche principali utilizzate coinvolgono i cosiddetti metodi di Explainable AI per l'analisi delle mappe di salienza e di attivazione, attraverso diversi algoritmi di rilevamento della salienza visiva e la Gradient-weighted Class Activation Mapping (Grad-CAM). Inoltre, viene utilizzata una rete neurale per la segmentazione con architettura DeepLabV3+, in cui vengono fornite le bounding box sulle immagini di addestramento, in un contesto debolmente supervisionato.Colorectal cancer (CRC) is one of the leading causes of death worldwide and continues to pose a critical public health challenge, demanding precise early detection and intervention. Colonoscopy, the diagnostic examination aimed at exploring the inner walls of the colon to discover any tumour masses, is an effective method to decrease mortality incidence. Emerging techniques, such as advanced image analysis driven by neural networks, hold promise for accurate diagnosis. However, studies have reported that, for various reasons, a certain percentage of polyps are not correctly detected during colonoscopy. One of the most important is the dependency on pixel-level annotations, which requires a lot of computational resources, making necessary innovative solutions. This thesis introduces strategies for improving polyp identification. For this purpose, the main techniques involve the so-called Explainable AI tools for analyzing saliency maps and activation maps, through several state-of-the-art visual saliency detection algorithms and Gradient-weighted Class Activation Mapping (Grad-CAM). In addition, a neural network for segmentation with DeepLabV3+ architecture is used, in which bounding boxes are provided on the training images, within a weakly supervised framework

Intensity and Compactness Enabled Saliency Estimation for Leakage Detection in Diabetic and Malarial Retinopathy

Leakage in retinal angiography currently is a key feature for confirming the activities of lesions in the management of a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. This paper proposes a new saliency-based method for the detection of leakage in fluorescein angiography. A superpixel approach is firstly employed to divide the image into meaningful patches (or superpixels) at different levels. Two saliency cues, intensity and compactness, are then proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. The two saliency maps over different cues are fused using a pixel-wise multiplication operator. Leaking regions are finally detected by thresholding the saliency map followed by a graph-cut segmentation. The proposed method has been validated using the only two publicly available datasets: one for malarial retinopathy and the other for diabetic retinopathy. The experimental results show that it outperforms one of the latest competitors and performs as well as a human expert for leakage detection and outperforms several state-of-the-art methods for saliency detection