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Found 25 papers, 14 papers with code
How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model
Automated segmentation is a fundamental medical image analysis task, which enjoys significant advances due to the advent of deep learning.
Rethinking Perceptual Metrics for Medical Image Translation
Modern medical image translation methods use generative models for tasks such as the conversion of CT images to MRI.
ContourDiff: Unpaired Image Translation with Contour-Guided Diffusion Models
We evaluate our method by training a segmentation model on images translated from CT to MRI with their original CT masks and testing its performance on real MRIs.
NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries
Writing formulas on spreadsheets, such as Microsoft Excel and Google Sheets, is a widespread practice among users performing data analysis.
Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time Adaptation
Here, we approach such a task, of adapting a medical image segmentation model with only a single unlabeled test image.
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models
Diffusion models have enabled remarkably high-quality medical image generation, yet it is challenging to enforce anatomical constraints in generated images.
SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI
In our study, we propose a versatile, publicly available deep-learning model for bone segmentation in MRI across multiple standard MRI locations.
Rethinking Medical Report Generation: Disease Revealing Enhancement with Knowledge Graph
Knowledge Graph (KG) plays a crucial role in Medical Report Generation (MRG) because it reveals the relations among diseases and thus can be utilized to guide the generation process.
A systematic study of the foreground-background imbalance problem in deep learning for object detection
We experimentally study the effects of different aspects of F-B imbalance (object size, number of objects, dataset size, object type) on detection performance.
Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion
Given the scarcity of abnormal images and the abundance of normal images for this problem, an anomaly detection/localization approach could be well-suited.
Segment Anything Model for Medical Image Analysis: an Experimental Study
We conclude that SAM shows impressive zero-shot segmentation performance for certain medical imaging datasets, but moderate to poor performance for others.
Reflection of Thought: Inversely Eliciting Numerical Reasoning in Language Models via Solving Linear Systems
Numerical reasoning over natural language has been a long-standing goal for the research community.
The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning
These results give a more principled underpinning for the intuition that radiological images can be more challenging to apply deep learning to than natural image datasets common to machine learning research.
TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data
Existing auto-regressive pre-trained language models (PLMs) like T5 and BART, have been well applied to table question answering by UNIFIEDSKG and TAPEX, respectively, and demonstrated state-of-the-art results on multiple benchmarks.
PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation
However, directly learning the logical inference knowledge from table-text pairs is very difficult for neural models because of the ambiguity of natural language and the scarcity of parallel data.
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
Since a vast number of tables can be easily collected from web pages, spreadsheets, PDFs, and various other document types, a flurry of table pre-training frameworks have been proposed following the success of text and images, and they have achieved new state-of-the-arts on various tasks such as table question answering, table type recognition, column relation classification, table search, formula prediction, etc.
Lightweight Transformer Backbone for Medical Object Detection
Specifically, we propose a novel modification of visual transformer (ViT) on image feature patches to connect the feature patches of a tumor with healthy backgrounds of breast images and form a more robust backbone for tumor detection.
FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining
In this paper, we find that the spreadsheet formula, which performs calculations on numerical values in tables, is naturally a strong supervision of numerical reasoning.
HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation
HiTab provides 10, 686 QA pairs and descriptive sentences with well-annotated quantity and entity alignment on 3, 597 tables with broad coverage of table hierarchies and numerical reasoning types.
TableSense: Spreadsheet Table Detection with Convolutional Neural Networks
Spreadsheet table detection is the task of detecting all tables on a given sheet and locating their respective ranges.
Trust It or Not: Confidence-Guided Automatic Radiology Report Generation
Experimental results have demonstrated that the proposed method for model uncertainty characterization and estimation can produce more reliable confidence scores for radiology report generation, and the modified loss function, which takes into account the uncertainties, leads to better model performance on two public radiology report datasets.
Using Text to Teach Image Retrieval
Image retrieval relies heavily on the quality of the data modeling and the distance measurement in the feature space.
TUTA: Tree-based Transformers for Generally Structured Table Pre-training
First, we devise a unified tree-based structure, called a bi-dimensional coordinate tree, to describe both the spatial and hierarchical information of generally structured tables.
Rethinking Exposure Bias In Language Modeling
Exposure bias describes the phenomenon that a language model trained under the teacher forcing schema may perform poorly at the inference stage when its predictions are conditioned on its previous predictions unseen from the training corpus.
Semantic Structure Extraction for Spreadsheet Tables with a Multi-task Learning Architecture
To learn spatial correlations and capture semantics on spreadsheets, we have developed a novel learning-based framework for spreadsheet semantic structure extraction.
