Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis

In the last few years, deep learning classifiers have shown promising results in image-based medical diagnosis. However, interpreting the outputs of these models remains a challenge. In cancer diagnosis, interpretability can be achieved by localizing the region of the input image responsible for the output, i.e. the location of a lesion. Alternatively, segmentation or detection models can be trained with pixel-wise annotations indicating the locations of malignant lesions. Unfortunately, acquiring such labels is labor-intensive and requires medical expertise. To overcome this difficulty, weakly-supervised localization can be utilized. These methods allow neural network classifiers to output saliency maps highlighting the regions of the input most relevant to the classification task (e.g. malignant lesions in mammograms) using only image-level labels (e.g. whether the patient has cancer or not) during training. When applied to high-resolution images, existing methods produce low-resolution saliency maps. This is problematic in applications in which suspicious lesions are small in relation to the image size. In this work, we introduce a novel neural network architecture to perform weakly-supervised segmentation of high-resolution images. The proposed model selects regions of interest via coarse-level localization, and then performs fine-grained segmentation of those regions. We apply this model to breast cancer diagnosis with screening mammography, and validate it on a large clinically-realistic dataset. Measured by Dice similarity score, our approach outperforms existing methods by a large margin in terms of localization performance of benign and malignant lesions, relatively improving the performance by 39.6% and 20.0%, respectively. Code and the weights of some of the models are available at https://github.com/nyukat/GLAMComment: The last two authors contributed equally. Accepted to Medical Imaging with Deep Learning (MIDL) 202

Uczenie reprezentacji dla prewidywania implikacji tekstowych

Możliwość zgadnięcia czy jedno zdanie implikuje drugie, czy jemu zaprzecza, jest niezbędne do zrozumienia języka naturalnego. Celem tej pracy jest zbadanie wpływu wektorowej reprezentacji słów w zadaniu przewidywania implikacji między zdaniami. Porównujemy kilka metod reprezentacji na dwóch niedawno wprowadzonych zbiorach danych: SNLI oraz MultiNLI.The ability to predict whether one sentence entails another or contradicts it is essential to understand the natural language. The goal of this work is to investigate the importance of word embeddings in the Natural Language Inference problem. We compare several embedding methods on two recently introduced datasets: SNLI and MultiNLI

Fractal space and related metrics

W pracy wprowadzimy metryki Busemanna i Hausdorffa, następnie porównamy topologie przez nie generowane. Następnie określimy tak zwaną przestrzeń fraktali, czyli przestrzeń zbiorów niepustych i zwartych z metryką Hausdorffa. Na koniec zastanowimy się nad własnościami tej przestrzeni - zwartością i spójnością.We will introduce the Busemann metric and the Hausdorff metric and compare the topologies they generate. Then we will define so-called fractal space, i.e. the family of nonempty and compact sets with the Hausdorff distance. In the end we will think over some properties of this space - compactness and connectedness

Improving utilization of lexical knowledge in natural language inference

Natural language inference (NLI) is a central problem in natural language processing (NLP) of predicting the logical relationship between a pair of sentences. Lexical knowledge, which represents relations between words, is often important for solving NLI problems. This knowledge can be accessed by using an external knowledge base (KB), but this is limited to when such a resource is accessible. Instead of using a KB, we propose a simple architectural change for attention based models. We show that by adding a skip connection from the input to the attention layer we can utilize better the lexical knowledge already present in the pretrained word embeddings. Finally, we demonstrate that our strategy allows to use an external source of knowledge in a straightforward manner by incorporating a second word embedding space in the model

Robust learning-augmented caching : an experimental study

Effective caching is crucial for performance of modern-day computing systems. A key optimization problem arising in caching – which item to evict to make room for a new item – cannot be optimally solved without knowing the future. There are many classical approximation algorithms for this problem, but more recently researchers started to successfully apply machine learning to decide what to evict by discovering implicit input patterns and predicting the future. While machine learning typically does not provide any worst-case guarantees, the new field of learning-augmented algorithms proposes solutions which leverage classical online caching algorithms to make the machine-learned predictors robust. We are the first to comprehensively evaluate these learning-augmented algorithms on real-world caching datasets and state-of-the-art machine-learned predictors. We show that a straightforward method – blindly following either a predictor or a classical robust algorithm, and switching whenever one becomes worse than the other – has only a low overhead over a well-performing predictor, while competing with classical methods when the coupled predictor fails, thus providing a cheap worst-case insurance

From dataset recycling to multi-property extraction and beyond

This paper investigates various Transformerarchitectures on the WikiReading Informa-tion Extraction and Machine Reading Com-prehension dataset. The proposed dual-sourcemodel outperforms the current state-of-the-art by a large margin.Next, we intro-duce WikiReading Recycled—a newly devel-oped public dataset, and the task of multiple-property extraction. It uses the same data asWikiReading but does not inherit its predeces-sor’s identified disadvantages. In addition, weprovide a human-annotated test set with diag-nostic subsets for a detailed analysis of modelperformance