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Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time, improving the accuracy of diagnosis and enhancing treatment. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, conclusions based on incorrect decisions may be fatal, especially in medicine. Despite these pitfalls, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. The Medico 2020 challenge received submissions from 17 teams, while the MedAI 2021 challenge also gathered submissions from another 17 distinct teams in the following year. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. Our analysis revealed that the participants improved dice coefficient metrics from 0.8607 in 2020 to 0.8993 in 2021 despite adding diverse and challenging frames (containing irregular, smaller, sessile, or flat polyps), which are frequently missed during a routine clinical examination. For the instrument segmentation task, the best team obtained a mean Intersection over union metric of 0.9364. For the transparency task, a multi-disciplinary team, including expert gastroenterologists, accessed each submission and evaluated the team based on open-source practices, failure case analysis, ablation studies, usability and understandability of evaluations to gain a deeper understanding of the models' credibility for clinical deployment. The best team obtained a final transparency score of 21 out of 25. Through the comprehensive analysis of the challenge, we not only highlight the advancements in polyp and surgical instrument segmentation but also encourage subjective evaluation for building more transparent and understandable AI-based colonoscopy systems. Moreover, we discuss the need for multi-center and out-of-distribution testing to address the current limitations of the methods to reduce the cancer burden and improve patient care.

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In this work, we proposed a patch-based classifier (PBC) using Convolutional neural network (CNN) for automatic classification of histopathological breast images. Presence of limited images necessitated extraction of patches and augmentation to boost the number of training samples. Thus patches of suitable sizes carrying crucial diagnostic information were extracted from the original images. The proposed classification system works in two different modes: one patch in one decision (OPOD) and all patches in one decision (APOD). The proposed PBC first predicts the class label of each patch by OPOD mode. If that class label is the same for all the extracted patches and that is the class label of that image, then the output is considered as correct classification. In another mode that is APOD, the class label of each extracted patch is extracted as done in OPOD and a majority voting scheme takes the final decision about class label of the image. We have used ICIAR 2018 breast histology image dataset for this work which comprises of 4 different classes namely normal, benign, in situ and invasive carcinoma. Experimental results show that our proposed OPOD mode achieved a patch-wise classification accuracy of 77.4% for 4 and 84.7% for 2 histopathological classes respectively on the test set obtained by splitting the training dataset. Also, our proposed APOD technique achieved image-wise classification accuracy of 90% for 4-class and 92.5% for 2-class classification respectively on the split test set. Further, we have achieved accuracy of 87% on the hidden test dataset of ICIAR-2018.

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RESUMEN

Segmentation of suspicious regions (SRs) of a thermal breast image (TBI) is a very significant and challenging problem for the identification of breast cancer. Therefore, in this work, we have proposed an active contour model for the segmentation of the SRs in TBI. The proposed segmentation method combines three significant steps. First, a novel method, called smaller-peaks corresponding to the high-intensity-pixels and the centroid-knowledge of SRs (SCH-CS), is proposed to approximately locate the SRs, whose contours are later used as the initial evolving curves of the level set method (LSM). Second, a new energy functional, called different local priorities embedded (DLPE), is proposed regarding the level set function. DLPE is then minimized using the interleaved level set evolution to segment the potential SRs in TBI more accurately. Finally, a new stopping criterion is incorporated into the proposed LSM. The proposed LSM not only increases the segmentation speed but also ameliorates the segmentation accuracy. The performance of our SR segmentation method was evaluated on two TBI databases, namely, DMR-IR and DBT-TU-JU, and the average segmentation accuracies obtained on these databases are 72.18% and 71.26% respectively, which are better than the other state-of-the-art methods. Beside this, a novel framework to analyze TBIs is proposed for differentiating abnormal and normal breasts on the basis of the segmented SRs. We have also shown experimentally that investigating only the SRs instead of the whole breast is more effective in differentiating abnormal and normal breasts.

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