A cancer pathology report is a valuable medical document that provides information for prognosis, personalised treatment plan and patient management. Developing countries still use the unstructured (free text) reporting format. However, this reporting format has been associated with several limitations arising from variations in the quality of reporting. Manual information extraction and report classification can be intrinsically complex and resource intensive, given a free-text format. Extracting information from these reports into a structured layout is also essential for research, auditing, and cancer incidence reporting. This study aimed to develop and evaluate strategies for extracting relevant information to classify cancer pathology reports and to develop a rule-based function to automatically extract cancer prognostic parameters from these reports and transform them into structured data to uncover the trend of the parameters over the years. We retrieved colorectal and prostate cancer diagnostic cases from the National Health Laboratory Services. TM and ML algorithms were used for data preprocessing, visualisations, feature selections, text classification and performance evaluation. Secondly, we developed a rule-based NLP algorithm that retrieved and extracted important prognostic parameters from the reports to explore their trends. Results showed inconsistencies and incompleteness in reporting each year and throughout the study period. The findings also indicate that the developed rule-based function achieved high accurate annotation for all the parameters extracted, with performance measures ranging from 83% -100%. The trend analysis result showed significant trends comparable to previous studies. In conclusion, we developed reproducible frameworks using NLP and ML algorithms that can form the basis for future studies in South Africa. Our study bridged the gap between data availability and actionable knowledge.

The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and therefore often predict quantities at a coarse spatial resolution. Statistical downscaling can provide an efficient method of upsampling low-resolution data. In this field, deep learning has been applied successfully, often using methods from the super-resolution domain in computer vision. Despite often achieving visually compelling results, such models often violate conservation laws when predicting physical variables. In order to conserve important physical quantities, we develop methods that guarantee physical constraints are satisfied by a deep downscaling model while also increasing their performance according to traditional metrics. We introduce two ways of constraining the network: A renormalization layer added to the end of the neural network and a successive approach that scales with increasing upsampling factors. We show the applicability of our methods across different popular architectures and upsampling factors using ERA5 reanalysis data.

Ensuring machines remain beneficial to humans requires that machine learning systems are still able to communicate their inner workings such that another observer can infer its reasoning and intent/s. This process, known as explainability, is crucial in helping shape our relationship with machine learning systems. Despite the advantages of existing approaches to implement explainability in machine learning systems and learn through more natural interactions with humans and other agents, current algorithms generally (1) are not evaluated in teamwork and human decision-making scenarios and (2) often require large numbers of examples on how to solve a task. These are both crucial aspects for humans to operate alongside machine learning systems, especially in interactive settings. To address the above-mentioned limitations, in our work we conducted three studies centered around first, understanding the role of explanations in human-machine teamwork, second, exploring human learning from intelligent systems using machine-generated explanations, and thirdly, incorporating human explanations into machine learning. In presenting our computational models around these aspects, we hope to advance our knowledge and understanding of different facets of explainable agency in machine learning and enable successful human-AI partnership and knowledge transfer.

For image segmentation, the current standard is to perform pixel-level optimization and inference in Euclidean output embedding spaces through linear hyperplanes. In this work, we show that hyperbolic manifolds provide a valuable alternative for image segmentation and propose a tractable formulation of hierarchical pixel-level classification in hyperbolic space. Hyperbolic Image Segmentation opens up new possibilities and practical benefits for segmentation, such as uncertainty estimation and boundary information for free, zero-label generalization, and increased performance in low-dimensional output embeddings.

We are excited to bring together 3 wonderful experts for our panel discussion:

Jenn Wortman Vaughan (Microsoft Research), Colin Raffel (University of North Carolina) Kristen Grauman (University of Texas at Austin)

Please join here: https://topia.io/neurips-2022-aff-joint-poster-session